Pipeline-Klasifikasi Text Mining-NB-KNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QQzFvuEXYO29"
   },
   "source": [
    "## Klasifikasi Text Mining menggunakan Naive Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wDD8_BOAYhz9"
   },
   "source": [
    "# load drive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9156,
     "status": "ok",
     "timestamp": 1665403314011,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "nGp1bD18YjLY",
    "outputId": "9bd795fa-7e40-4777-98b6-b4690740f3e5"
   },
   "outputs": [],
   "source": [
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive')\n",
    "# import sys\n",
    "# sys.path.append('/content/drive/My Drive/Colab Notebooks/AMS2023')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4ha0Iz7KZPNI"
   },
   "source": [
    "# install library ekphrasis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2955,
     "status": "ok",
     "timestamp": 1665403320752,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "o0lyD-xfZQge",
    "outputId": "c4360fa6-dce1-43e9-b1c3-0effd16d0223"
   },
   "outputs": [],
   "source": [
    "# !pip install ekphrasis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8611,
     "status": "ok",
     "timestamp": 1665403332930,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "2n8VFUcCZWuV",
    "outputId": "1df07a58-882c-4480-d106-ef896b65e624",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/ekphrasis/classes/tokenizer.py:225: FutureWarning: Possible nested set at position 2190\n",
      "  self.tok = re.compile(r\"({})\".format(\"|\".join(pipeline)))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word statistics files not found!\n",
      "Downloading... done!\n",
      "Unpacking... done!\n",
      "Reading twitter - 1grams ...\n",
      "generating cache file for faster loading...\n",
      "reading ngrams /home/jovyan/.ekphrasis/stats/twitter/counts_1grams.txt\n",
      "Reading twitter - 2grams ...\n",
      "generating cache file for faster loading...\n",
      "reading ngrams /home/jovyan/.ekphrasis/stats/twitter/counts_2grams.txt\n",
      "Reading twitter - 1grams ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/ekphrasis/classes/exmanager.py:14: FutureWarning: Possible nested set at position 42\n",
      "  regexes = {k.lower(): re.compile(self.expressions[k]) for k, v in\n"
     ]
    }
   ],
   "source": [
    "from ekphrasis.classes.preprocessor import TextPreProcessor\n",
    "from ekphrasis.classes.tokenizer import SocialTokenizer\n",
    "from ekphrasis.dicts.emoticons import emoticons\n",
    "\n",
    "text_processor = TextPreProcessor(\n",
    "    # terms that will be normalized\n",
    "    normalize=['email', 'percent', 'money', 'phone', 'user',\n",
    "        'time', 'date', 'number'],\n",
    "    # terms that will be annotated\n",
    "    #annotate={\"hashtag\", \"allcaps\", \"elongated\", \"repeated\",'emphasis', 'censored'},\n",
    "    annotate={\"hashtag\",\"allcaps\",\"elongated\",\"repeated\",'emphasis','censored'},\n",
    "    fix_html=True,  # fix HTML tokens\n",
    "    \n",
    "    # corpus from which the word statistics are going to be used \n",
    "    # for word segmentation \n",
    "    segmenter=\"twitter\", \n",
    "    \n",
    "    # corpus from which the word statistics are going to be used \n",
    "    # for spell correction\n",
    "    corrector=\"twitter\", \n",
    "    \n",
    "    unpack_hashtags=True,  # perform word segmentation on hashtags\n",
    "    unpack_contractions=True,  # Unpack contractions (can't -> can not)\n",
    "    spell_correct_elong=False,  # spell correction for elongated words\n",
    "    \n",
    "    # select a tokenizer. You can use SocialTokenizer, or pass your own\n",
    "    # the tokenizer, should take as input a string and return a list of tokens\n",
    "    tokenizer=SocialTokenizer(lowercase=True).tokenize,\n",
    "    \n",
    "    # list of dictionaries, for replacing tokens extracted from the text,\n",
    "    # with other expressions. You can pass more than one dictionaries.\n",
    "    dicts=[emoticons]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OOwAOicWYajI"
   },
   "source": [
    "# load library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "BmapjRiEYGPd",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import string \n",
    "import pandas as pd\n",
    "from copy import deepcopy\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iR6F1PJMZgY_"
   },
   "source": [
    "# load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "i533Vd_pZfnq",
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('Dataset_Sentimen_Emosi.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1665403332933,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "tNNt78OHZp6G",
    "outputId": "42a33437-b602-423e-a352-5bc05c6d9879",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweet</th>\n",
       "      <th>Sentimen</th>\n",
       "      <th>Emosi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>emang lemes bgt ya mulut lu, gue yg mau beli-b...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cegah mata rantai Covid-19,mari kita dirumah s...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aku mohon yaAllah semoga wabah covid-19 menghi...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pemprov Papua Naikkan Status Jadi Tanggap Daru...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Covid belum nyampe prigen mbak hmm hoax</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Tweet  Sentimen  Emosi\n",
       "0  emang lemes bgt ya mulut lu, gue yg mau beli-b...      -1.0     -2\n",
       "1  Cegah mata rantai Covid-19,mari kita dirumah s...       1.0      1\n",
       "2  aku mohon yaAllah semoga wabah covid-19 menghi...       1.0     -1\n",
       "3  Pemprov Papua Naikkan Status Jadi Tanggap Daru...       1.0      1\n",
       "4            Covid belum nyampe prigen mbak hmm hoax       0.0     -2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wI1_sMOYZ10L"
   },
   "source": [
    "karena kita hanya ingin fokus di Sentimen analysis, maka kita hanya pakai `Sentimen` sebagai target class nya **class**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "ORRk3QgCZzIZ",
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = df.drop(['Emosi'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1665403332933,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "Fw9T_vxxaGlO",
    "outputId": "b0788005-2a3f-44cf-8d49-e1fac9f69c79",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweet</th>\n",
       "      <th>Sentimen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>emang lemes bgt ya mulut lu, gue yg mau beli-b...</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cegah mata rantai Covid-19,mari kita dirumah s...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aku mohon yaAllah semoga wabah covid-19 menghi...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pemprov Papua Naikkan Status Jadi Tanggap Daru...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Covid belum nyampe prigen mbak hmm hoax</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Tweet  Sentimen\n",
       "0  emang lemes bgt ya mulut lu, gue yg mau beli-b...      -1.0\n",
       "1  Cegah mata rantai Covid-19,mari kita dirumah s...       1.0\n",
       "2  aku mohon yaAllah semoga wabah covid-19 menghi...       1.0\n",
       "3  Pemprov Papua Naikkan Status Jadi Tanggap Daru...       1.0\n",
       "4            Covid belum nyampe prigen mbak hmm hoax       0.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BJQVC4IFaQVN"
   },
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gsZ7w0D6bB_G"
   },
   "source": [
    "## setting fungsi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "CUL7cOXDaKwK",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def bersih_data(text):\n",
    "    return \" \".join(text_processor.pre_process_doc(text))\n",
    "\n",
    "def non_ascii(text):\n",
    "    return text.encode('ascii', 'replace').decode('ascii')\n",
    "\n",
    "def remove_space_alzami(text):\n",
    "    return \" \".join(text.split())\n",
    "\n",
    "def remove_emoji_alzami(text):\n",
    "    return ' '.join(re.sub(\"([x#][A-Za-z0-9]+)\",\" \", text).split())\n",
    "\n",
    "def remove_tab(text):\n",
    "    return text.replace('\\\\t',\" \").replace('\\\\n',\" \").replace('\\\\u',\" \").replace('\\\\',\"\")\n",
    "\n",
    "def remove_tab2(text):\n",
    "    return re.sub('\\s+',' ',text)\n",
    "\n",
    "def remove_rt(text):\n",
    "    return text.replace('RT',\" \")\n",
    "\n",
    "def remove_mention(text):\n",
    "    return ' '.join(re.sub(\"([@#][A-Za-z0-9]+)|(\\w+:\\/\\/\\S+)\",\" \", text).split())\n",
    "\n",
    "def remove_incomplete_url(text):\n",
    "    return text.replace(\"http://\", \" \").replace(\"https://\", \" \")\n",
    "\n",
    "def remove_single_char(text):\n",
    "    return re.sub(r\"\\b[a-zA-Z]\\b\", \"\", text)\n",
    "\n",
    "def change_stripe(text):\n",
    "    return text.replace('-',\" \")\n",
    "\n",
    "def lower(text):\n",
    "    return text.lower()\n",
    "\n",
    "def remove_punctuation(text):\n",
    "    remove = string.punctuation\n",
    "    remove = remove.replace(\"_\", \"\") # don't remove hyphens\n",
    "    pattern = r\"[{}]\".format(remove) # create the pattern\n",
    "    return re.sub(pattern, \"\", text) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dKWc2zLWa1vU"
   },
   "source": [
    "## running preprocessing\n",
    "logika main disini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "WK9Su5bBbG2G",
    "tags": []
   },
   "outputs": [],
   "source": [
    "final_string = []\n",
    "for text in df['Tweet'].values:\n",
    "    EachReviewText = \"\"\n",
    "    proc = bersih_data(text)\n",
    "    proc = remove_rt(proc) \n",
    "    proc = lower(proc)\n",
    "    proc = change_stripe(proc)\n",
    "    proc = remove_tab(proc)\n",
    "    proc = remove_tab2(proc)\n",
    "    proc = non_ascii(proc)\n",
    "    proc = remove_incomplete_url(proc)\n",
    "    proc = remove_single_char(proc)\n",
    "    proc = remove_punctuation(proc)\n",
    "    proc = remove_space_alzami(proc)\n",
    "    EachReviewText = proc\n",
    "    final_string.append(EachReviewText)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gG-9xjmJbrja"
   },
   "source": [
    "masukkan hasil preprocessing ke kolom `step01`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "ALrxEwRJbjb2",
    "tags": []
   },
   "outputs": [],
   "source": [
    "df[\"step01\"] = final_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1665403340439,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "hOmgeMAMbwGu",
    "outputId": "d961e3d7-3d9a-40a8-c94a-c5dfc8bc30a1",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweet</th>\n",
       "      <th>Sentimen</th>\n",
       "      <th>step01</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>emang lemes bgt ya mulut lu, gue yg mau beli-b...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>emang lemes bgt ya mulut lu gue yg mau beli be...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Tweet  Sentimen   \n",
       "0  emang lemes bgt ya mulut lu, gue yg mau beli-b...      -1.0  \\\n",
       "\n",
       "                                              step01  \n",
       "0  emang lemes bgt ya mulut lu gue yg mau beli be...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M_mswe6Ab5Fn"
   },
   "source": [
    "cek jumlah dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1,
     "status": "ok",
     "timestamp": 1665403342089,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "RmArdHI8bxVD",
    "outputId": "1365258e-5712-4932-8700-63305e36768c",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 905 entries, 0 to 904\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Tweet     905 non-null    object \n",
      " 1   Sentimen  904 non-null    float64\n",
      " 2   step01    905 non-null    object \n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 21.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ERYpQ-KYb9rJ"
   },
   "source": [
    "## hapus record yang kosong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "ro18ixq6b7aS",
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_hapus = df[~df['step01'].str.contains(\" \")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "tk6cqwtvcBNT",
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_new = df[~df.isin(df_hapus)].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1665403344503,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "Yu6dRIGycCuH",
    "outputId": "f75ac4c4-ee55-4336-be4d-ef1d8b197f33",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 903 entries, 0 to 904\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Tweet     903 non-null    object \n",
      " 1   Sentimen  903 non-null    float64\n",
      " 2   step01    903 non-null    object \n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 28.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_new.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HpC4mJyDcLNo"
   },
   "source": [
    "## normalisasi kata slang"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1665403346314,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "3GTurr6RcYiW",
    "outputId": "431592cb-8dce-4b66-e6ed-d75d1b35f4fe",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/jovyan/nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
     ]
    }
   ],
   "source": [
    "# token\n",
    "import nltk\n",
    "nltk.download('punkt')\n",
    "from nltk.tokenize import word_tokenize "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "qMe9R6gLcaQ-",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def word_tokenize_wrapper(text):\n",
    "  return word_tokenize(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "jMoHPkw7cdMq",
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_new['tokens'] = df['step01'].apply(word_tokenize_wrapper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1665403350052,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "xebbaeOVcfGT",
    "outputId": "0e82aa13-eaa2-49b8-a2fa-5b10edb10097",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweet</th>\n",
       "      <th>Sentimen</th>\n",
       "      <th>step01</th>\n",
       "      <th>tokens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>emang lemes bgt ya mulut lu, gue yg mau beli-b...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>emang lemes bgt ya mulut lu gue yg mau beli be...</td>\n",
       "      <td>[emang, lemes, bgt, ya, mulut, lu, gue, yg, ma...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cegah mata rantai Covid-19,mari kita dirumah s...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>cegah mata rantai covid number mari kita dirum...</td>\n",
       "      <td>[cegah, mata, rantai, covid, number, mari, kit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aku mohon yaAllah semoga wabah covid-19 menghi...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>aku mohon yaallah semoga wabah covid number me...</td>\n",
       "      <td>[aku, mohon, yaallah, semoga, wabah, covid, nu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pemprov Papua Naikkan Status Jadi Tanggap Daru...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pemprov papua naikkan status jadi tanggap daru...</td>\n",
       "      <td>[pemprov, papua, naikkan, status, jadi, tangga...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Covid belum nyampe prigen mbak hmm hoax</td>\n",
       "      <td>0.0</td>\n",
       "      <td>covid belum nyampe prigen mbak hmm hoax</td>\n",
       "      <td>[covid, belum, nyampe, prigen, mbak, hmm, hoax]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Nyuruh orang pintar, lu aja Togog. Itu kerumun...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>nyuruh orang pintar lu aja togog itu kerumunan...</td>\n",
       "      <td>[nyuruh, orang, pintar, lu, aja, togog, itu, k...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Pikir2 balik byk mnde plk nk setelkn lepas covid.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>pikir2 balik byk mnde plk nk setelkn lepas covid</td>\n",
       "      <td>[pikir2, balik, byk, mnde, plk, nk, setelkn, l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Selamat pagi, hari jum'at. Jum'at keempat di k...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>selamat pagi hari jum at jum at keempat di kal...</td>\n",
       "      <td>[selamat, pagi, hari, jum, at, jum, at, keempa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Hikmah di balik musibah covid-19, smg para pej...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>hikmah di balik musibah covid number smg para ...</td>\n",
       "      <td>[hikmah, di, balik, musibah, covid, number, sm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Cegah covid-19 beserta jajaran Polsek Kuranji ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>cegah covid number beserta jajaran polsek kura...</td>\n",
       "      <td>[cegah, covid, number, beserta, jajaran, polse...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Tweet  Sentimen   \n",
       "0  emang lemes bgt ya mulut lu, gue yg mau beli-b...      -1.0  \\\n",
       "1  Cegah mata rantai Covid-19,mari kita dirumah s...       1.0   \n",
       "2  aku mohon yaAllah semoga wabah covid-19 menghi...       1.0   \n",
       "3  Pemprov Papua Naikkan Status Jadi Tanggap Daru...       1.0   \n",
       "4            Covid belum nyampe prigen mbak hmm hoax       0.0   \n",
       "5  Nyuruh orang pintar, lu aja Togog. Itu kerumun...      -1.0   \n",
       "6  Pikir2 balik byk mnde plk nk setelkn lepas covid.       0.0   \n",
       "7  Selamat pagi, hari jum'at. Jum'at keempat di k...       1.0   \n",
       "8  Hikmah di balik musibah covid-19, smg para pej...       1.0   \n",
       "9  Cegah covid-19 beserta jajaran Polsek Kuranji ...       1.0   \n",
       "\n",
       "                                              step01   \n",
       "0  emang lemes bgt ya mulut lu gue yg mau beli be...  \\\n",
       "1  cegah mata rantai covid number mari kita dirum...   \n",
       "2  aku mohon yaallah semoga wabah covid number me...   \n",
       "3  pemprov papua naikkan status jadi tanggap daru...   \n",
       "4            covid belum nyampe prigen mbak hmm hoax   \n",
       "5  nyuruh orang pintar lu aja togog itu kerumunan...   \n",
       "6   pikir2 balik byk mnde plk nk setelkn lepas covid   \n",
       "7  selamat pagi hari jum at jum at keempat di kal...   \n",
       "8  hikmah di balik musibah covid number smg para ...   \n",
       "9  cegah covid number beserta jajaran polsek kura...   \n",
       "\n",
       "                                              tokens  \n",
       "0  [emang, lemes, bgt, ya, mulut, lu, gue, yg, ma...  \n",
       "1  [cegah, mata, rantai, covid, number, mari, kit...  \n",
       "2  [aku, mohon, yaallah, semoga, wabah, covid, nu...  \n",
       "3  [pemprov, papua, naikkan, status, jadi, tangga...  \n",
       "4    [covid, belum, nyampe, prigen, mbak, hmm, hoax]  \n",
       "5  [nyuruh, orang, pintar, lu, aja, togog, itu, k...  \n",
       "6  [pikir2, balik, byk, mnde, plk, nk, setelkn, l...  \n",
       "7  [selamat, pagi, hari, jum, at, jum, at, keempa...  \n",
       "8  [hikmah, di, balik, musibah, covid, number, sm...  \n",
       "9  [cegah, covid, number, beserta, jajaran, polse...  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "eFYvCzaLcD2k",
    "tags": []
   },
   "outputs": [],
   "source": [
    "normalized_word = pd.read_excel(\"kamus perbaikan kata.xlsx\")\n",
    "\n",
    "normalized_word_dict = {}\n",
    "\n",
    "for index, row in normalized_word.iterrows():\n",
    "    if row[0] not in normalized_word_dict:\n",
    "        normalized_word_dict[row[0]] = row[1] \n",
    "\n",
    "def normalized_term(document):\n",
    "    return [normalized_word_dict[term] if term in normalized_word_dict else term for term in document]\n",
    "\n",
    "df_new['tokens_perbaikan'] = df_new['tokens'].apply(normalized_term)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 302
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1665403352451,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "nAmSDdMfdIVR",
    "outputId": "90af7759-2d8e-42d5-be68-e9aabfaf158b",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweet</th>\n",
       "      <th>Sentimen</th>\n",
       "      <th>step01</th>\n",
       "      <th>tokens</th>\n",
       "      <th>tokens_perbaikan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>emang lemes bgt ya mulut lu, gue yg mau beli-b...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>emang lemes bgt ya mulut lu gue yg mau beli be...</td>\n",
       "      <td>[emang, lemes, bgt, ya, mulut, lu, gue, yg, ma...</td>\n",
       "      <td>[memang, lemes, banget, iya, mulut, kamu, saya...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cegah mata rantai Covid-19,mari kita dirumah s...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>cegah mata rantai covid number mari kita dirum...</td>\n",
       "      <td>[cegah, mata, rantai, covid, number, mari, kit...</td>\n",
       "      <td>[cegah, mata, rantai, covid, number, mari, kit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aku mohon yaAllah semoga wabah covid-19 menghi...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>aku mohon yaallah semoga wabah covid number me...</td>\n",
       "      <td>[aku, mohon, yaallah, semoga, wabah, covid, nu...</td>\n",
       "      <td>[aku, mohon, yaallah, semoga, wabah, covid, nu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pemprov Papua Naikkan Status Jadi Tanggap Daru...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pemprov papua naikkan status jadi tanggap daru...</td>\n",
       "      <td>[pemprov, papua, naikkan, status, jadi, tangga...</td>\n",
       "      <td>[pemprov, papua, naikkan, status, jadi, tangga...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Covid belum nyampe prigen mbak hmm hoax</td>\n",
       "      <td>0.0</td>\n",
       "      <td>covid belum nyampe prigen mbak hmm hoax</td>\n",
       "      <td>[covid, belum, nyampe, prigen, mbak, hmm, hoax]</td>\n",
       "      <td>[covid, belum, nyampe, prigen, mbak, aduh, boh...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Tweet  Sentimen   \n",
       "0  emang lemes bgt ya mulut lu, gue yg mau beli-b...      -1.0  \\\n",
       "1  Cegah mata rantai Covid-19,mari kita dirumah s...       1.0   \n",
       "2  aku mohon yaAllah semoga wabah covid-19 menghi...       1.0   \n",
       "3  Pemprov Papua Naikkan Status Jadi Tanggap Daru...       1.0   \n",
       "4            Covid belum nyampe prigen mbak hmm hoax       0.0   \n",
       "\n",
       "                                              step01   \n",
       "0  emang lemes bgt ya mulut lu gue yg mau beli be...  \\\n",
       "1  cegah mata rantai covid number mari kita dirum...   \n",
       "2  aku mohon yaallah semoga wabah covid number me...   \n",
       "3  pemprov papua naikkan status jadi tanggap daru...   \n",
       "4            covid belum nyampe prigen mbak hmm hoax   \n",
       "\n",
       "                                              tokens   \n",
       "0  [emang, lemes, bgt, ya, mulut, lu, gue, yg, ma...  \\\n",
       "1  [cegah, mata, rantai, covid, number, mari, kit...   \n",
       "2  [aku, mohon, yaallah, semoga, wabah, covid, nu...   \n",
       "3  [pemprov, papua, naikkan, status, jadi, tangga...   \n",
       "4    [covid, belum, nyampe, prigen, mbak, hmm, hoax]   \n",
       "\n",
       "                                    tokens_perbaikan  \n",
       "0  [memang, lemes, banget, iya, mulut, kamu, saya...  \n",
       "1  [cegah, mata, rantai, covid, number, mari, kit...  \n",
       "2  [aku, mohon, yaallah, semoga, wabah, covid, nu...  \n",
       "3  [pemprov, papua, naikkan, status, jadi, tangga...  \n",
       "4  [covid, belum, nyampe, prigen, mbak, aduh, boh...  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1665403354361,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "mDa2wUiPwkgp",
    "outputId": "3ed47017-eede-4414-aa9f-fae5eae9c8dc",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['emang',\n",
       " 'lemes',\n",
       " 'bgt',\n",
       " 'ya',\n",
       " 'mulut',\n",
       " 'lu',\n",
       " 'gue',\n",
       " 'yg',\n",
       " 'mau',\n",
       " 'beli',\n",
       " 'beli',\n",
       " 'obat',\n",
       " 'aja',\n",
       " 'mikirr',\n",
       " 'drmn',\n",
       " 'bisa',\n",
       " 'beli',\n",
       " 'obat',\n",
       " 'kalo',\n",
       " 'ga',\n",
       " 'kerja',\n",
       " 'devil']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.iloc[0].tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1243,
     "status": "ok",
     "timestamp": 1665403356414,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "z1ock8LEwKeL",
    "outputId": "c1b0b93e-39aa-4532-8a7c-89a1a8baad58",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['memang',\n",
       " 'lemes',\n",
       " 'banget',\n",
       " 'iya',\n",
       " 'mulut',\n",
       " 'kamu',\n",
       " 'saya',\n",
       " 'yang',\n",
       " 'mau',\n",
       " 'beli',\n",
       " 'beli',\n",
       " 'obat',\n",
       " 'saja',\n",
       " 'mikirr',\n",
       " 'drmn',\n",
       " 'bisa',\n",
       " 'beli',\n",
       " 'obat',\n",
       " 'kalau',\n",
       " 'tidak',\n",
       " 'kerja',\n",
       " 'devil']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.iloc[0].tokens_perbaikan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oZAFzaeMGppU"
   },
   "source": [
    "## Stopword - Menggunakan Modul NLTK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1665403357115,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "I5YUrQfjGtD0",
    "outputId": "094fd773-cb0f-4240-816e-f1729419a6df",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/jovyan/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.corpus import stopwords\n",
    "nltk.download(\"stopwords\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 641
    },
    "executionInfo": {
     "elapsed": 519,
     "status": "ok",
     "timestamp": 1665403359936,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "AlpimHRsGs_v",
    "outputId": "0d9a7d33-4213-4fee-93f4-e3562397437d",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweet</th>\n",
       "      <th>Sentimen</th>\n",
       "      <th>step01</th>\n",
       "      <th>tokens</th>\n",
       "      <th>tokens_perbaikan</th>\n",
       "      <th>Stop_removal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>emang lemes bgt ya mulut lu, gue yg mau beli-b...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>emang lemes bgt ya mulut lu gue yg mau beli be...</td>\n",
       "      <td>[emang, lemes, bgt, ya, mulut, lu, gue, yg, ma...</td>\n",
       "      <td>[memang, lemes, banget, iya, mulut, kamu, saya...</td>\n",
       "      <td>[lemes, banget, iya, mulut, beli, beli, obat, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cegah mata rantai Covid-19,mari kita dirumah s...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>cegah mata rantai covid number mari kita dirum...</td>\n",
       "      <td>[cegah, mata, rantai, covid, number, mari, kit...</td>\n",
       "      <td>[cegah, mata, rantai, covid, number, mari, kit...</td>\n",
       "      <td>[cegah, mata, rantai, covid, number, mari, dir...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aku mohon yaAllah semoga wabah covid-19 menghi...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>aku mohon yaallah semoga wabah covid number me...</td>\n",
       "      <td>[aku, mohon, yaallah, semoga, wabah, covid, nu...</td>\n",
       "      <td>[aku, mohon, yaallah, semoga, wabah, covid, nu...</td>\n",
       "      <td>[mohon, yaallah, semoga, wabah, covid, number,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pemprov Papua Naikkan Status Jadi Tanggap Daru...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pemprov papua naikkan status jadi tanggap daru...</td>\n",
       "      <td>[pemprov, papua, naikkan, status, jadi, tangga...</td>\n",
       "      <td>[pemprov, papua, naikkan, status, jadi, tangga...</td>\n",
       "      <td>[pemprov, papua, naikkan, status, tanggap, dar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Covid belum nyampe prigen mbak hmm hoax</td>\n",
       "      <td>0.0</td>\n",
       "      <td>covid belum nyampe prigen mbak hmm hoax</td>\n",
       "      <td>[covid, belum, nyampe, prigen, mbak, hmm, hoax]</td>\n",
       "      <td>[covid, belum, nyampe, prigen, mbak, aduh, boh...</td>\n",
       "      <td>[covid, nyampe, prigen, mbak, aduh, bohong]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>Seluruh negara di dunia mengalami masa sulit k...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>seluruh negara di dunia mengalami masa sulit k...</td>\n",
       "      <td>[seluruh, negara, di, dunia, mengalami, masa, ...</td>\n",
       "      <td>[seluruh, negara, di, dunia, mengalami, masa, ...</td>\n",
       "      <td>[negara, dunia, mengalami, sulit, covid, numbe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>901</th>\n",
       "      <td>Setelah covid dan skripsi disaster selesai, ma...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>setelah covid dan skripsi disaster selesai mau...</td>\n",
       "      <td>[setelah, covid, dan, skripsi, disaster, seles...</td>\n",
       "      <td>[setelah, covid, dan, skripsi, disaster, seles...</td>\n",
       "      <td>[covid, skripsi, disaster, selesai, banget, be...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>Malam ini!! Projek \"BENDA BOLEH BINCANG\" 9 mal...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>malam ini repeated projek allcaps benda boleh ...</td>\n",
       "      <td>[malam, ini, repeated, projek, allcaps, benda,...</td>\n",
       "      <td>[malam, ini, repeated, proyek, allcaps, benda,...</td>\n",
       "      <td>[malam, repeated, proyek, allcaps, benda, binc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903</th>\n",
       "      <td>Pontang - panting di koyak covid 19</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>pontang panting di koyak covid number</td>\n",
       "      <td>[pontang, panting, di, koyak, covid, number]</td>\n",
       "      <td>[pontang, panting, di, koyak, covid, number]</td>\n",
       "      <td>[pontang, panting, koyak, covid, number]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>904</th>\n",
       "      <td>Masalahnya sekarang isu jangkitan covid. Alaaa...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>masalahnya sekarang isu jangkitan covid ala el...</td>\n",
       "      <td>[masalahnya, sekarang, isu, jangkitan, covid, ...</td>\n",
       "      <td>[masalahnya, sekarang, isu, jangkitan, covid, ...</td>\n",
       "      <td>[isu, jangkitan, covid, ala, elongated, bosan,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>903 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Tweet  Sentimen   \n",
       "0    emang lemes bgt ya mulut lu, gue yg mau beli-b...      -1.0  \\\n",
       "1    Cegah mata rantai Covid-19,mari kita dirumah s...       1.0   \n",
       "2    aku mohon yaAllah semoga wabah covid-19 menghi...       1.0   \n",
       "3    Pemprov Papua Naikkan Status Jadi Tanggap Daru...       1.0   \n",
       "4              Covid belum nyampe prigen mbak hmm hoax       0.0   \n",
       "..                                                 ...       ...   \n",
       "900  Seluruh negara di dunia mengalami masa sulit k...       1.0   \n",
       "901  Setelah covid dan skripsi disaster selesai, ma...       1.0   \n",
       "902  Malam ini!! Projek \"BENDA BOLEH BINCANG\" 9 mal...       0.0   \n",
       "903                Pontang - panting di koyak covid 19      -1.0   \n",
       "904  Masalahnya sekarang isu jangkitan covid. Alaaa...      -1.0   \n",
       "\n",
       "                                                step01   \n",
       "0    emang lemes bgt ya mulut lu gue yg mau beli be...  \\\n",
       "1    cegah mata rantai covid number mari kita dirum...   \n",
       "2    aku mohon yaallah semoga wabah covid number me...   \n",
       "3    pemprov papua naikkan status jadi tanggap daru...   \n",
       "4              covid belum nyampe prigen mbak hmm hoax   \n",
       "..                                                 ...   \n",
       "900  seluruh negara di dunia mengalami masa sulit k...   \n",
       "901  setelah covid dan skripsi disaster selesai mau...   \n",
       "902  malam ini repeated projek allcaps benda boleh ...   \n",
       "903              pontang panting di koyak covid number   \n",
       "904  masalahnya sekarang isu jangkitan covid ala el...   \n",
       "\n",
       "                                                tokens   \n",
       "0    [emang, lemes, bgt, ya, mulut, lu, gue, yg, ma...  \\\n",
       "1    [cegah, mata, rantai, covid, number, mari, kit...   \n",
       "2    [aku, mohon, yaallah, semoga, wabah, covid, nu...   \n",
       "3    [pemprov, papua, naikkan, status, jadi, tangga...   \n",
       "4      [covid, belum, nyampe, prigen, mbak, hmm, hoax]   \n",
       "..                                                 ...   \n",
       "900  [seluruh, negara, di, dunia, mengalami, masa, ...   \n",
       "901  [setelah, covid, dan, skripsi, disaster, seles...   \n",
       "902  [malam, ini, repeated, projek, allcaps, benda,...   \n",
       "903       [pontang, panting, di, koyak, covid, number]   \n",
       "904  [masalahnya, sekarang, isu, jangkitan, covid, ...   \n",
       "\n",
       "                                      tokens_perbaikan   \n",
       "0    [memang, lemes, banget, iya, mulut, kamu, saya...  \\\n",
       "1    [cegah, mata, rantai, covid, number, mari, kit...   \n",
       "2    [aku, mohon, yaallah, semoga, wabah, covid, nu...   \n",
       "3    [pemprov, papua, naikkan, status, jadi, tangga...   \n",
       "4    [covid, belum, nyampe, prigen, mbak, aduh, boh...   \n",
       "..                                                 ...   \n",
       "900  [seluruh, negara, di, dunia, mengalami, masa, ...   \n",
       "901  [setelah, covid, dan, skripsi, disaster, seles...   \n",
       "902  [malam, ini, repeated, proyek, allcaps, benda,...   \n",
       "903       [pontang, panting, di, koyak, covid, number]   \n",
       "904  [masalahnya, sekarang, isu, jangkitan, covid, ...   \n",
       "\n",
       "                                          Stop_removal  \n",
       "0    [lemes, banget, iya, mulut, beli, beli, obat, ...  \n",
       "1    [cegah, mata, rantai, covid, number, mari, dir...  \n",
       "2    [mohon, yaallah, semoga, wabah, covid, number,...  \n",
       "3    [pemprov, papua, naikkan, status, tanggap, dar...  \n",
       "4          [covid, nyampe, prigen, mbak, aduh, bohong]  \n",
       "..                                                 ...  \n",
       "900  [negara, dunia, mengalami, sulit, covid, numbe...  \n",
       "901  [covid, skripsi, disaster, selesai, banget, be...  \n",
       "902  [malam, repeated, proyek, allcaps, benda, binc...  \n",
       "903           [pontang, panting, koyak, covid, number]  \n",
       "904  [isu, jangkitan, covid, ala, elongated, bosan,...  \n",
       "\n",
       "[903 rows x 6 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopword = nltk.corpus.stopwords.words('indonesian')\n",
    "\n",
    "def remove_stopwords(text):\n",
    "  text = [word for word in text if word not in stopword]\n",
    "  return text\n",
    "\n",
    "df_new['Stop_removal'] = df_new['tokens_perbaikan'].apply(lambda x: remove_stopwords(x))\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1107,
     "status": "ok",
     "timestamp": 1665403365473,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "gOSOFPEBwx5v",
    "outputId": "71ef1320-3cfd-42dd-836e-96003bf1414f",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['memang',\n",
       " 'lemes',\n",
       " 'banget',\n",
       " 'iya',\n",
       " 'mulut',\n",
       " 'kamu',\n",
       " 'saya',\n",
       " 'yang',\n",
       " 'mau',\n",
       " 'beli',\n",
       " 'beli',\n",
       " 'obat',\n",
       " 'saja',\n",
       " 'mikirr',\n",
       " 'drmn',\n",
       " 'bisa',\n",
       " 'beli',\n",
       " 'obat',\n",
       " 'kalau',\n",
       " 'tidak',\n",
       " 'kerja',\n",
       " 'devil']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.iloc[0].tokens_perbaikan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1665403366290,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "w9Zqsdw8w0YU",
    "outputId": "b42295ba-7ba9-4031-9058-71abb6bba538",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['lemes',\n",
       " 'banget',\n",
       " 'iya',\n",
       " 'mulut',\n",
       " 'beli',\n",
       " 'beli',\n",
       " 'obat',\n",
       " 'mikirr',\n",
       " 'drmn',\n",
       " 'beli',\n",
       " 'obat',\n",
       " 'kerja',\n",
       " 'devil']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.iloc[0].Stop_removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "GscKs3PpJLcc",
    "tags": []
   },
   "outputs": [],
   "source": [
    "i=0\n",
    "final_string_tokens = []\n",
    "for text in df_new['Stop_removal'].values:\n",
    "    EachReviewText = \"\"\n",
    "    EachReviewText = ' '.join(text)\n",
    "    final_string_tokens.append(EachReviewText)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "o4-6_3weJkEj",
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_new[\"step02\"] = final_string_tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 337
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1665403375908,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "Ty8ykeoaJpO7",
    "outputId": "b86c98f9-a60a-4027-dd95-7cfa6d1fe054",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweet</th>\n",
       "      <th>Sentimen</th>\n",
       "      <th>step01</th>\n",
       "      <th>tokens</th>\n",
       "      <th>tokens_perbaikan</th>\n",
       "      <th>Stop_removal</th>\n",
       "      <th>step02</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>emang lemes bgt ya mulut lu, gue yg mau beli-b...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>emang lemes bgt ya mulut lu gue yg mau beli be...</td>\n",
       "      <td>[emang, lemes, bgt, ya, mulut, lu, gue, yg, ma...</td>\n",
       "      <td>[memang, lemes, banget, iya, mulut, kamu, saya...</td>\n",
       "      <td>[lemes, banget, iya, mulut, beli, beli, obat, ...</td>\n",
       "      <td>lemes banget iya mulut beli beli obat mikirr d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cegah mata rantai Covid-19,mari kita dirumah s...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>cegah mata rantai covid number mari kita dirum...</td>\n",
       "      <td>[cegah, mata, rantai, covid, number, mari, kit...</td>\n",
       "      <td>[cegah, mata, rantai, covid, number, mari, kit...</td>\n",
       "      <td>[cegah, mata, rantai, covid, number, mari, dir...</td>\n",
       "      <td>cegah mata rantai covid number mari dirumah mi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aku mohon yaAllah semoga wabah covid-19 menghi...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>aku mohon yaallah semoga wabah covid number me...</td>\n",
       "      <td>[aku, mohon, yaallah, semoga, wabah, covid, nu...</td>\n",
       "      <td>[aku, mohon, yaallah, semoga, wabah, covid, nu...</td>\n",
       "      <td>[mohon, yaallah, semoga, wabah, covid, number,...</td>\n",
       "      <td>mohon yaallah semoga wabah covid number menghi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pemprov Papua Naikkan Status Jadi Tanggap Daru...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pemprov papua naikkan status jadi tanggap daru...</td>\n",
       "      <td>[pemprov, papua, naikkan, status, jadi, tangga...</td>\n",
       "      <td>[pemprov, papua, naikkan, status, jadi, tangga...</td>\n",
       "      <td>[pemprov, papua, naikkan, status, tanggap, dar...</td>\n",
       "      <td>pemprov papua naikkan status tanggap darurat c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Covid belum nyampe prigen mbak hmm hoax</td>\n",
       "      <td>0.0</td>\n",
       "      <td>covid belum nyampe prigen mbak hmm hoax</td>\n",
       "      <td>[covid, belum, nyampe, prigen, mbak, hmm, hoax]</td>\n",
       "      <td>[covid, belum, nyampe, prigen, mbak, aduh, boh...</td>\n",
       "      <td>[covid, nyampe, prigen, mbak, aduh, bohong]</td>\n",
       "      <td>covid nyampe prigen mbak aduh bohong</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Tweet  Sentimen   \n",
       "0  emang lemes bgt ya mulut lu, gue yg mau beli-b...      -1.0  \\\n",
       "1  Cegah mata rantai Covid-19,mari kita dirumah s...       1.0   \n",
       "2  aku mohon yaAllah semoga wabah covid-19 menghi...       1.0   \n",
       "3  Pemprov Papua Naikkan Status Jadi Tanggap Daru...       1.0   \n",
       "4            Covid belum nyampe prigen mbak hmm hoax       0.0   \n",
       "\n",
       "                                              step01   \n",
       "0  emang lemes bgt ya mulut lu gue yg mau beli be...  \\\n",
       "1  cegah mata rantai covid number mari kita dirum...   \n",
       "2  aku mohon yaallah semoga wabah covid number me...   \n",
       "3  pemprov papua naikkan status jadi tanggap daru...   \n",
       "4            covid belum nyampe prigen mbak hmm hoax   \n",
       "\n",
       "                                              tokens   \n",
       "0  [emang, lemes, bgt, ya, mulut, lu, gue, yg, ma...  \\\n",
       "1  [cegah, mata, rantai, covid, number, mari, kit...   \n",
       "2  [aku, mohon, yaallah, semoga, wabah, covid, nu...   \n",
       "3  [pemprov, papua, naikkan, status, jadi, tangga...   \n",
       "4    [covid, belum, nyampe, prigen, mbak, hmm, hoax]   \n",
       "\n",
       "                                    tokens_perbaikan   \n",
       "0  [memang, lemes, banget, iya, mulut, kamu, saya...  \\\n",
       "1  [cegah, mata, rantai, covid, number, mari, kit...   \n",
       "2  [aku, mohon, yaallah, semoga, wabah, covid, nu...   \n",
       "3  [pemprov, papua, naikkan, status, jadi, tangga...   \n",
       "4  [covid, belum, nyampe, prigen, mbak, aduh, boh...   \n",
       "\n",
       "                                        Stop_removal   \n",
       "0  [lemes, banget, iya, mulut, beli, beli, obat, ...  \\\n",
       "1  [cegah, mata, rantai, covid, number, mari, dir...   \n",
       "2  [mohon, yaallah, semoga, wabah, covid, number,...   \n",
       "3  [pemprov, papua, naikkan, status, tanggap, dar...   \n",
       "4        [covid, nyampe, prigen, mbak, aduh, bohong]   \n",
       "\n",
       "                                              step02  \n",
       "0  lemes banget iya mulut beli beli obat mikirr d...  \n",
       "1  cegah mata rantai covid number mari dirumah mi...  \n",
       "2  mohon yaallah semoga wabah covid number menghi...  \n",
       "3  pemprov papua naikkan status tanggap darurat c...  \n",
       "4               covid nyampe prigen mbak aduh bohong  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-F9_5pJIHw6-"
   },
   "source": [
    "## Stemming - Menggunakan Sastrawi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5113,
     "status": "ok",
     "timestamp": 1665403398110,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "fYG4PHGxGs8E",
    "outputId": "d34f2e26-7fb3-48af-b4fc-e5a934fc3080",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !pip install sastrawi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "Qhuf9k2CGs5z",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from Sastrawi.Stemmer.StemmerFactory import StemmerFactory\n",
    "factory = StemmerFactory()\n",
    "stemmer = factory.create_stemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "nd-QecH2Gs3O",
    "tags": []
   },
   "outputs": [],
   "source": [
    "final_string = []\n",
    "s = \"\"\n",
    "for sentence in df_new[\"step02\"].values:\n",
    "    filteredSentence = []\n",
    "    EachReviewText = \"\"\n",
    "    s = (stemmer.stem(sentence))\n",
    "    filteredSentence.append(s)\n",
    "    \n",
    "    EachReviewText = ' '.join(filteredSentence)\n",
    "    final_string.append(EachReviewText)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "6Fu05R6NN1nU",
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_new[\"ProcessedText\"] = final_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 684
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1665403748666,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "HRbSfQ91Gszo",
    "outputId": "0a44cccb-381d-4b68-f345-816014788c7e",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweet</th>\n",
       "      <th>Sentimen</th>\n",
       "      <th>step01</th>\n",
       "      <th>tokens</th>\n",
       "      <th>tokens_perbaikan</th>\n",
       "      <th>Stop_removal</th>\n",
       "      <th>step02</th>\n",
       "      <th>ProcessedText</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>emang lemes bgt ya mulut lu, gue yg mau beli-b...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>emang lemes bgt ya mulut lu gue yg mau beli be...</td>\n",
       "      <td>[emang, lemes, bgt, ya, mulut, lu, gue, yg, ma...</td>\n",
       "      <td>[memang, lemes, banget, iya, mulut, kamu, saya...</td>\n",
       "      <td>[lemes, banget, iya, mulut, beli, beli, obat, ...</td>\n",
       "      <td>lemes banget iya mulut beli beli obat mikirr d...</td>\n",
       "      <td>lemes banget iya mulut beli beli obat mikirr d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cegah mata rantai Covid-19,mari kita dirumah s...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>cegah mata rantai covid number mari kita dirum...</td>\n",
       "      <td>[cegah, mata, rantai, covid, number, mari, kit...</td>\n",
       "      <td>[cegah, mata, rantai, covid, number, mari, kit...</td>\n",
       "      <td>[cegah, mata, rantai, covid, number, mari, dir...</td>\n",
       "      <td>cegah mata rantai covid number mari dirumah mi...</td>\n",
       "      <td>cegah mata rantai covid number mari rumah ming...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aku mohon yaAllah semoga wabah covid-19 menghi...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>aku mohon yaallah semoga wabah covid number me...</td>\n",
       "      <td>[aku, mohon, yaallah, semoga, wabah, covid, nu...</td>\n",
       "      <td>[aku, mohon, yaallah, semoga, wabah, covid, nu...</td>\n",
       "      <td>[mohon, yaallah, semoga, wabah, covid, number,...</td>\n",
       "      <td>mohon yaallah semoga wabah covid number menghi...</td>\n",
       "      <td>mohon yaallah moga wabah covid number hilang r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pemprov Papua Naikkan Status Jadi Tanggap Daru...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pemprov papua naikkan status jadi tanggap daru...</td>\n",
       "      <td>[pemprov, papua, naikkan, status, jadi, tangga...</td>\n",
       "      <td>[pemprov, papua, naikkan, status, jadi, tangga...</td>\n",
       "      <td>[pemprov, papua, naikkan, status, tanggap, dar...</td>\n",
       "      <td>pemprov papua naikkan status tanggap darurat c...</td>\n",
       "      <td>pemprov papua naik status tanggap darurat covi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Covid belum nyampe prigen mbak hmm hoax</td>\n",
       "      <td>0.0</td>\n",
       "      <td>covid belum nyampe prigen mbak hmm hoax</td>\n",
       "      <td>[covid, belum, nyampe, prigen, mbak, hmm, hoax]</td>\n",
       "      <td>[covid, belum, nyampe, prigen, mbak, aduh, boh...</td>\n",
       "      <td>[covid, nyampe, prigen, mbak, aduh, bohong]</td>\n",
       "      <td>covid nyampe prigen mbak aduh bohong</td>\n",
       "      <td>covid nyampe prigen mbak aduh bohong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Nyuruh orang pintar, lu aja Togog. Itu kerumun...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>nyuruh orang pintar lu aja togog itu kerumunan...</td>\n",
       "      <td>[nyuruh, orang, pintar, lu, aja, togog, itu, k...</td>\n",
       "      <td>[nyuruh, orang, pintar, kamu, saja, togog, itu...</td>\n",
       "      <td>[nyuruh, orang, pintar, togog, kerumunan, oran...</td>\n",
       "      <td>nyuruh orang pintar togog kerumunan orang nula...</td>\n",
       "      <td>nyuruh orang pintar togog kerumun orang nulari...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Pikir2 balik byk mnde plk nk setelkn lepas covid.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>pikir2 balik byk mnde plk nk setelkn lepas covid</td>\n",
       "      <td>[pikir2, balik, byk, mnde, plk, nk, setelkn, l...</td>\n",
       "      <td>[pikir2, balik, banyak, mnde, plk, nk, setelkn...</td>\n",
       "      <td>[pikir2, mnde, plk, nk, setelkn, lepas, covid]</td>\n",
       "      <td>pikir2 mnde plk nk setelkn lepas covid</td>\n",
       "      <td>pikir2 mnde plk nk setelkn lepas covid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Selamat pagi, hari jum'at. Jum'at keempat di k...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>selamat pagi hari jum at jum at keempat di kal...</td>\n",
       "      <td>[selamat, pagi, hari, jum, at, jum, at, keempa...</td>\n",
       "      <td>[selamat, pagi, hari, jum, at, jum, at, keempa...</td>\n",
       "      <td>[selamat, pagi, jum, at, jum, at, keempat, pan...</td>\n",
       "      <td>selamat pagi jum at jum at keempat pandemi cov...</td>\n",
       "      <td>selamat pagi jum at jum at empat pandemi covid...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Hikmah di balik musibah covid-19, smg para pej...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>hikmah di balik musibah covid number smg para ...</td>\n",
       "      <td>[hikmah, di, balik, musibah, covid, number, sm...</td>\n",
       "      <td>[hikmah, di, balik, musibah, covid, number, se...</td>\n",
       "      <td>[hikmah, musibah, covid, number, semoga, pejab...</td>\n",
       "      <td>hikmah musibah covid number semoga pejabat pem...</td>\n",
       "      <td>hikmah musibah covid number moga jabat perinta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Cegah covid-19 beserta jajaran Polsek Kuranji ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>cegah covid number beserta jajaran polsek kura...</td>\n",
       "      <td>[cegah, covid, number, beserta, jajaran, polse...</td>\n",
       "      <td>[cegah, covid, number, beserta, jajaran, polse...</td>\n",
       "      <td>[cegah, covid, number, beserta, jajaran, polse...</td>\n",
       "      <td>cegah covid number beserta jajaran polsek kura...</td>\n",
       "      <td>cegah covid number serta jajar polsek ranji ak...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Tweet  Sentimen   \n",
       "0  emang lemes bgt ya mulut lu, gue yg mau beli-b...      -1.0  \\\n",
       "1  Cegah mata rantai Covid-19,mari kita dirumah s...       1.0   \n",
       "2  aku mohon yaAllah semoga wabah covid-19 menghi...       1.0   \n",
       "3  Pemprov Papua Naikkan Status Jadi Tanggap Daru...       1.0   \n",
       "4            Covid belum nyampe prigen mbak hmm hoax       0.0   \n",
       "5  Nyuruh orang pintar, lu aja Togog. Itu kerumun...      -1.0   \n",
       "6  Pikir2 balik byk mnde plk nk setelkn lepas covid.       0.0   \n",
       "7  Selamat pagi, hari jum'at. Jum'at keempat di k...       1.0   \n",
       "8  Hikmah di balik musibah covid-19, smg para pej...       1.0   \n",
       "9  Cegah covid-19 beserta jajaran Polsek Kuranji ...       1.0   \n",
       "\n",
       "                                              step01   \n",
       "0  emang lemes bgt ya mulut lu gue yg mau beli be...  \\\n",
       "1  cegah mata rantai covid number mari kita dirum...   \n",
       "2  aku mohon yaallah semoga wabah covid number me...   \n",
       "3  pemprov papua naikkan status jadi tanggap daru...   \n",
       "4            covid belum nyampe prigen mbak hmm hoax   \n",
       "5  nyuruh orang pintar lu aja togog itu kerumunan...   \n",
       "6   pikir2 balik byk mnde plk nk setelkn lepas covid   \n",
       "7  selamat pagi hari jum at jum at keempat di kal...   \n",
       "8  hikmah di balik musibah covid number smg para ...   \n",
       "9  cegah covid number beserta jajaran polsek kura...   \n",
       "\n",
       "                                              tokens   \n",
       "0  [emang, lemes, bgt, ya, mulut, lu, gue, yg, ma...  \\\n",
       "1  [cegah, mata, rantai, covid, number, mari, kit...   \n",
       "2  [aku, mohon, yaallah, semoga, wabah, covid, nu...   \n",
       "3  [pemprov, papua, naikkan, status, jadi, tangga...   \n",
       "4    [covid, belum, nyampe, prigen, mbak, hmm, hoax]   \n",
       "5  [nyuruh, orang, pintar, lu, aja, togog, itu, k...   \n",
       "6  [pikir2, balik, byk, mnde, plk, nk, setelkn, l...   \n",
       "7  [selamat, pagi, hari, jum, at, jum, at, keempa...   \n",
       "8  [hikmah, di, balik, musibah, covid, number, sm...   \n",
       "9  [cegah, covid, number, beserta, jajaran, polse...   \n",
       "\n",
       "                                    tokens_perbaikan   \n",
       "0  [memang, lemes, banget, iya, mulut, kamu, saya...  \\\n",
       "1  [cegah, mata, rantai, covid, number, mari, kit...   \n",
       "2  [aku, mohon, yaallah, semoga, wabah, covid, nu...   \n",
       "3  [pemprov, papua, naikkan, status, jadi, tangga...   \n",
       "4  [covid, belum, nyampe, prigen, mbak, aduh, boh...   \n",
       "5  [nyuruh, orang, pintar, kamu, saja, togog, itu...   \n",
       "6  [pikir2, balik, banyak, mnde, plk, nk, setelkn...   \n",
       "7  [selamat, pagi, hari, jum, at, jum, at, keempa...   \n",
       "8  [hikmah, di, balik, musibah, covid, number, se...   \n",
       "9  [cegah, covid, number, beserta, jajaran, polse...   \n",
       "\n",
       "                                        Stop_removal   \n",
       "0  [lemes, banget, iya, mulut, beli, beli, obat, ...  \\\n",
       "1  [cegah, mata, rantai, covid, number, mari, dir...   \n",
       "2  [mohon, yaallah, semoga, wabah, covid, number,...   \n",
       "3  [pemprov, papua, naikkan, status, tanggap, dar...   \n",
       "4        [covid, nyampe, prigen, mbak, aduh, bohong]   \n",
       "5  [nyuruh, orang, pintar, togog, kerumunan, oran...   \n",
       "6     [pikir2, mnde, plk, nk, setelkn, lepas, covid]   \n",
       "7  [selamat, pagi, jum, at, jum, at, keempat, pan...   \n",
       "8  [hikmah, musibah, covid, number, semoga, pejab...   \n",
       "9  [cegah, covid, number, beserta, jajaran, polse...   \n",
       "\n",
       "                                              step02   \n",
       "0  lemes banget iya mulut beli beli obat mikirr d...  \\\n",
       "1  cegah mata rantai covid number mari dirumah mi...   \n",
       "2  mohon yaallah semoga wabah covid number menghi...   \n",
       "3  pemprov papua naikkan status tanggap darurat c...   \n",
       "4               covid nyampe prigen mbak aduh bohong   \n",
       "5  nyuruh orang pintar togog kerumunan orang nula...   \n",
       "6             pikir2 mnde plk nk setelkn lepas covid   \n",
       "7  selamat pagi jum at jum at keempat pandemi cov...   \n",
       "8  hikmah musibah covid number semoga pejabat pem...   \n",
       "9  cegah covid number beserta jajaran polsek kura...   \n",
       "\n",
       "                                       ProcessedText  \n",
       "0  lemes banget iya mulut beli beli obat mikirr d...  \n",
       "1  cegah mata rantai covid number mari rumah ming...  \n",
       "2  mohon yaallah moga wabah covid number hilang r...  \n",
       "3  pemprov papua naik status tanggap darurat covi...  \n",
       "4               covid nyampe prigen mbak aduh bohong  \n",
       "5  nyuruh orang pintar togog kerumun orang nulari...  \n",
       "6             pikir2 mnde plk nk setelkn lepas covid  \n",
       "7  selamat pagi jum at jum at empat pandemi covid...  \n",
       "8  hikmah musibah covid number moga jabat perinta...  \n",
       "9  cegah covid number serta jajar polsek ranji ak...  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jVMr6XUee0Y6"
   },
   "source": [
    "## simpan hasil preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "GGZFw6G9evQG",
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_new.to_csv('clean_dataset_part01.csv',sep=\";\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MrkJ4dCTe-_J"
   },
   "source": [
    "# Penataan Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "llyNObWFMbT_"
   },
   "source": [
    "## pembagian fitur dan label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "yNpmt4magLnx",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "6uHL0jjJLjJo",
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('clean_dataset_part01.csv',sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1665403778927,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "jxvzWAcdyKRD",
    "outputId": "469d3977-f880-4eb1-8abe-12ad1477869d",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 903 entries, 0 to 902\n",
      "Data columns (total 9 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Unnamed: 0        903 non-null    int64  \n",
      " 1   Tweet             903 non-null    object \n",
      " 2   Sentimen          903 non-null    float64\n",
      " 3   step01            903 non-null    object \n",
      " 4   tokens            903 non-null    object \n",
      " 5   tokens_perbaikan  903 non-null    object \n",
      " 6   Stop_removal      903 non-null    object \n",
      " 7   step02            903 non-null    object \n",
      " 8   ProcessedText     903 non-null    object \n",
      "dtypes: float64(1), int64(1), object(7)\n",
      "memory usage: 63.6+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "2CQhuVgALr0J",
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset_feature = dataset['ProcessedText'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 513,
     "status": "ok",
     "timestamp": 1665403809731,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "nhOB8hceMQhE",
    "outputId": "d9ed6c29-7781-44b3-ce1e-6145d6ceaf43",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    lemes banget iya mulut beli beli obat mikirr d...\n",
       "1    cegah mata rantai covid number mari rumah ming...\n",
       "2    mohon yaallah moga wabah covid number hilang r...\n",
       "3    pemprov papua naik status tanggap darurat covi...\n",
       "4                 covid nyampe prigen mbak aduh bohong\n",
       "5    nyuruh orang pintar togog kerumun orang nulari...\n",
       "6               pikir2 mnde plk nk setelkn lepas covid\n",
       "7    selamat pagi jum at jum at empat pandemi covid...\n",
       "8    hikmah musibah covid number moga jabat perinta...\n",
       "9    cegah covid number serta jajar polsek ranji ak...\n",
       "Name: ProcessedText, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_feature.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "V0JQGHvqMQw3",
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset_label = dataset['Sentimen']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1665403822152,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "3uxBA6OiMUy_",
    "outputId": "eeb2c564-3378-4ac8-da82-9cd9ebf13a79",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -1.0\n",
       "1    1.0\n",
       "2    1.0\n",
       "3    1.0\n",
       "4    0.0\n",
       "5   -1.0\n",
       "6    0.0\n",
       "7    1.0\n",
       "8    1.0\n",
       "9    1.0\n",
       "Name: Sentimen, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_label.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iCSb35pwMXvP"
   },
   "source": [
    "## distribusi label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "dYRcULM9MaJm",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 553
    },
    "executionInfo": {
     "elapsed": 647,
     "status": "ok",
     "timestamp": 1665403829194,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "FtY6ATtHMgfh",
    "outputId": "dd84fade-1874-4d85-86b8-efff71775c56",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_481906/1470841120.py:3: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(dataset_label, label=f'target, skew: {dataset_label.skew():.2f}')\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the target variable\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.distplot(dataset_label, label=f'target, skew: {dataset_label.skew():.2f}')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1056,
     "status": "ok",
     "timestamp": 1665403841125,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "zvqzsl49MheB",
    "outputId": "d8589690-7831-4d87-f99c-debeba024cdc",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sentimen\n",
       " 1.0    441\n",
       " 0.0    266\n",
       "-1.0    196\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_label.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AjkdN3kOMk_I"
   },
   "source": [
    "# Ekstraksi Fitur dan Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "-aMoIuqfMkYK",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "JDw5U3eRMx1K",
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_x, test_x, train_label, test_label = train_test_split(dataset_feature, dataset_label, test_size = 0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70     tips ilmuwan tingkat kebal tubuh mari lawan co...\n",
       "457    cegah covid number serta jajar polsek ranji ak...\n",
       "218    warga kabupaten lampung utara nyata positif pa...\n",
       "250    mimpi malam allcaps psbb allcaps tahan batal c...\n",
       "39     ayo elongated repeated putus mata rantai seman...\n",
       "                             ...                        \n",
       "864          orang bahaya covid number lurus belok pergi\n",
       "638    halo gerak lawan covid number ayo juang inform...\n",
       "352    personil polsek karanglewas laksana patroli ma...\n",
       "465    amal langkah langkah cegah tular allcaps covid...\n",
       "640    wakakakkaka repeated puter cahaya tolong time ...\n",
       "Name: ProcessedText, Length: 271, dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vPvBu0LrM0oD"
   },
   "source": [
    "## load TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "XwzScubZMzgY",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "Tfidf_Vectorizer = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sYaLrOg2M5AP"
   },
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "id": "2i-BhMXXM4FS",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "id": "2qSlLSEGM7sa",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "classifier_nb = Pipeline([\n",
    "    ('tfidf', TfidfVectorizer()),\n",
    "    ('model', MultinomialNB()),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "id": "brvxUKO0M87t",
    "tags": []
   },
   "outputs": [],
   "source": [
    "parameters_nb = {\n",
    "    'tfidf__ngram_range': [(1, 1), (1, 2), (1, 3), (1, 4)],\n",
    "    'model__alpha': (0.0001, 0.001, 0.1, 1, 10, 100)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "UEQHo9ZoNACG",
    "tags": []
   },
   "outputs": [],
   "source": [
    "classifier_nb = GridSearchCV(classifier_nb, parameters_nb, cv = 3, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4388,
     "status": "ok",
     "timestamp": 1665403952442,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "Y64S8WkyNBoL",
    "outputId": "0d3dd373-e9cf-4e7c-c814-8df95e10b5a7",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3,\n",
       "             estimator=Pipeline(steps=[(&#x27;tfidf&#x27;, TfidfVectorizer()),\n",
       "                                       (&#x27;model&#x27;, MultinomialNB())]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;model__alpha&#x27;: (0.0001, 0.001, 0.1, 1, 10, 100),\n",
       "                         &#x27;tfidf__ngram_range&#x27;: [(1, 1), (1, 2), (1, 3),\n",
       "                                                (1, 4)]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3,\n",
       "             estimator=Pipeline(steps=[(&#x27;tfidf&#x27;, TfidfVectorizer()),\n",
       "                                       (&#x27;model&#x27;, MultinomialNB())]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;model__alpha&#x27;: (0.0001, 0.001, 0.1, 1, 10, 100),\n",
       "                         &#x27;tfidf__ngram_range&#x27;: [(1, 1), (1, 2), (1, 3),\n",
       "                                                (1, 4)]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;tfidf&#x27;, TfidfVectorizer()), (&#x27;model&#x27;, MultinomialNB())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=3,\n",
       "             estimator=Pipeline(steps=[('tfidf', TfidfVectorizer()),\n",
       "                                       ('model', MultinomialNB())]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'model__alpha': (0.0001, 0.001, 0.1, 1, 10, 100),\n",
       "                         'tfidf__ngram_range': [(1, 1), (1, 2), (1, 3),\n",
       "                                                (1, 4)]})"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_nb.fit(train_x, train_label.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1665403955097,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "ffb4ytJlNC0F",
    "outputId": "179303c8-5387-4be4-e531-61cf2761d3ea",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Training set:  0.9762658227848101\n",
      "Accuracy Test set:  0.5904059040590406\n"
     ]
    }
   ],
   "source": [
    "y_pred_nb_train = classifier_nb.predict(train_x)\n",
    "accuracy_nb_train = accuracy_score(train_label, y_pred_nb_train)\n",
    "print(\"Accuracy Training set: \", accuracy_nb_train)\n",
    "\n",
    "y_pred_nb_test = classifier_nb.predict(test_x)\n",
    "accuracy_nb_test = accuracy_score(test_label, y_pred_nb_test)\n",
    "print(\"Accuracy Test set: \", accuracy_nb_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 514,
     "status": "ok",
     "timestamp": 1665403972006,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "JxAILTVzNEeq",
    "outputId": "7377595f-6abb-47c4-d29f-6b49ddaf2718",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall Training set:  0.9762658227848101\n",
      "Recall Test set:  0.5904059040590406\n"
     ]
    }
   ],
   "source": [
    "recall_nb_train = recall_score(train_label, y_pred_nb_train, average='weighted')\n",
    "print(\"Recall Training set: \", recall_nb_train)\n",
    "\n",
    "recall_nb_test = recall_score(test_label, y_pred_nb_test, average='weighted')\n",
    "print(\"Recall Test set: \", recall_nb_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 494,
     "status": "ok",
     "timestamp": 1665403980709,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "CZtIV1euNF69",
    "outputId": "57fdf33f-e025-4553-e035-9a23a2840a27",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision Training set:  0.9763676139089341\n",
      "Precision Test set:  0.569541371806291\n"
     ]
    }
   ],
   "source": [
    "precision_nb_train = precision_score(train_label, y_pred_nb_train, average='weighted')\n",
    "print(\"Precision Training set: \", precision_nb_train)\n",
    "\n",
    "precision_nb_test = precision_score(test_label, y_pred_nb_test, average='weighted')\n",
    "print(\"Precision Test set: \", precision_nb_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1665404008359,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "-9gyuq9rNHWf",
    "outputId": "0fb06b4d-ce82-427d-b81e-1f2b0c02e7dc",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Training set:  0.9762757318902684\n",
      "F1 Test set:  0.5723164269765864\n"
     ]
    }
   ],
   "source": [
    "f1_nb_train = f1_score(train_label, y_pred_nb_train, average='weighted')\n",
    "print(\"F1 Training set: \", f1_nb_train)\n",
    "\n",
    "f1_nb_test = f1_score(test_label, y_pred_nb_test, average='weighted')\n",
    "print(\"F1 Test set: \", f1_nb_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 323
    },
    "executionInfo": {
     "elapsed": 551,
     "status": "ok",
     "timestamp": 1665404017403,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "-zZjJnhVNIjD",
    "outputId": "b35fd7e4-039e-4548-c461-8618341f0513",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "sns.heatmap(confusion_matrix(test_label, y_pred_nb_test), annot=True, cmap = 'viridis', fmt='.0f')\n",
    "plt.xlabel(\"Predicted values\", fontdict = {'size':14}, labelpad = 10)\n",
    "plt.ylabel(\"Actual values\"   , fontdict = {'size':14}, labelpad = 10)\n",
    "plt.title (\"Confusion Matrix\", fontdict = {'size':18}, pad = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 499,
     "status": "ok",
     "timestamp": 1665404030703,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "DCTkN29ENJrT",
    "outputId": "1913896a-345b-4e25-a68b-d2bc53d1905f",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;tfidf&#x27;, TfidfVectorizer()),\n",
       "                (&#x27;model&#x27;, MultinomialNB(alpha=0.1))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;tfidf&#x27;, TfidfVectorizer()),\n",
       "                (&#x27;model&#x27;, MultinomialNB(alpha=0.1))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB(alpha=0.1)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('tfidf', TfidfVectorizer()),\n",
       "                ('model', MultinomialNB(alpha=0.1))])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_nb.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1665404032621,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "d1u8QJO_NLGt",
    "outputId": "5162ef46-3df8-495e-b62d-c1a1a35de7ed",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model__alpha: 0.1\n",
      "tfidf__ngram_range: (1, 1)\n"
     ]
    }
   ],
   "source": [
    "for param_name in sorted(parameters_nb.keys()):\n",
    "    print(\"%s: %r\" % (param_name, classifier_nb.best_params_[param_name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "train_x_encoded = label_encoder.fit_transform(train_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_x_encoded = label_encoder.fit_transform(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x_encoded.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_x_encoded = train_x_encoded.reshape(-1, 1)\n",
    "# train_label = train_label.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_x_encoded = test_x_encoded.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = KNeighborsClassifier(n_neighbors=3)\n",
    "model.fit(train_x_encoded, train_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Training set:  0.6819620253164557\n",
      "Accuracy Test set:  0.36162361623616235\n"
     ]
    }
   ],
   "source": [
    "y_pred_knn_train = model.predict(train_x_encoded)\n",
    "accuracy_knn_train = accuracy_score(train_label, y_pred_knn_train)\n",
    "print(\"Accuracy Training set: \", accuracy_knn_train)\n",
    "\n",
    "y_pred_knn_test = model.predict(test_x_encoded)\n",
    "accuracy_knn_test = accuracy_score(test_label, y_pred_knn_test)\n",
    "print(\"Accuracy Test set: \", accuracy_knn_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall Training set:  0.6819620253164557\n",
      "Recall Test set:  0.36162361623616235\n"
     ]
    }
   ],
   "source": [
    "recall_knn_train = recall_score(train_label, y_pred_knn_train, average='weighted')\n",
    "print(\"Recall Training set: \", recall_knn_train)\n",
    "\n",
    "recall_knn_test = recall_score(test_label, y_pred_knn_test, average='weighted')\n",
    "print(\"Recall Test set: \", recall_knn_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision Training set:  0.7011272070435527\n",
      "Precision Test set:  0.39319546180536435\n"
     ]
    }
   ],
   "source": [
    "precision_knn_train = precision_score(train_label, y_pred_knn_train, average='weighted')\n",
    "print(\"Precision Training set: \", precision_knn_train)\n",
    "\n",
    "precision_knn_test = precision_score(test_label, y_pred_knn_test, average='weighted')\n",
    "print(\"Precision Test set: \", precision_knn_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Training set:  0.6865068520468985\n",
      "F1 Test set:  0.3717346622765151\n"
     ]
    }
   ],
   "source": [
    "f1_knn_train = f1_score(train_label, y_pred_knn_train, average='weighted')\n",
    "print(\"F1 Training set: \", f1_knn_train)\n",
    "\n",
    "f1_knn_test = f1_score(test_label, y_pred_knn_test, average='weighted')\n",
    "print(\"F1 Test set: \", f1_knn_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "sns.heatmap(confusion_matrix(test_label, y_pred_knn_test), annot=True, cmap = 'viridis', fmt='.0f')\n",
    "plt.xlabel(\"Predicted values\", fontdict = {'size':14}, labelpad = 10)\n",
    "plt.ylabel(\"Actual values\"   , fontdict = {'size':14}, labelpad = 10)\n",
    "plt.title (\"Confusion Matrix\", fontdict = {'size':18}, pad = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sx9QGpMhNQz2"
   },
   "source": [
    "# Visualisasi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "id": "PNe8zekbNMzJ",
    "tags": []
   },
   "outputs": [],
   "source": [
    "models = [        \n",
    "          ('Naive Bayes Multinomial', accuracy_nb_train, accuracy_nb_test),\n",
    "          ('KNN', accuracy_knn_train, accuracy_knn_test),\n",
    "         ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81
    },
    "executionInfo": {
     "elapsed": 662,
     "status": "ok",
     "timestamp": 1665404063287,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "PMDt0SP3NTvf",
    "outputId": "954a53fd-f2c6-4b45-9053-6e38b09bc7b9",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Training Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Naive Bayes Multinomial</td>\n",
       "      <td>0.976266</td>\n",
       "      <td>0.590406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.681962</td>\n",
       "      <td>0.361624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Model  Training Accuracy  Test Accuracy\n",
       "0  Naive Bayes Multinomial           0.976266       0.590406\n",
       "1                      KNN           0.681962       0.361624"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict = pd.DataFrame(data = models, columns=['Model', 'Training Accuracy', 'Test Accuracy'])\n",
    "predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "id": "E7dNcRN6NU7c",
    "tags": []
   },
   "outputs": [],
   "source": [
    "models_comparison = [                        \n",
    "                        ('Naive Bayes Multinomial', accuracy_nb_test, recall_nb_test, precision_nb_test, f1_nb_test),\n",
    "                        ('KNN', accuracy_knn_test, recall_knn_test, precision_knn_test, f1_knn_test),                   \n",
    "                    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81
    },
    "executionInfo": {
     "elapsed": 730,
     "status": "ok",
     "timestamp": 1665404079597,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "ag1KxvizNYKY",
    "outputId": "a3bd2829-8109-4ba6-eaca-718f698b1520",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1 Weighted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Naive Bayes Multinomial</td>\n",
       "      <td>0.590406</td>\n",
       "      <td>0.590406</td>\n",
       "      <td>0.569541</td>\n",
       "      <td>0.572316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.361624</td>\n",
       "      <td>0.361624</td>\n",
       "      <td>0.393195</td>\n",
       "      <td>0.371735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Model  Accuracy    Recall  Precision  F1 Weighted\n",
       "0  Naive Bayes Multinomial  0.590406  0.590406   0.569541     0.572316\n",
       "1                      KNN  0.361624  0.361624   0.393195     0.371735"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison = pd.DataFrame(data = models_comparison, columns=['Model', 'Accuracy', 'Recall', 'Precision', 'F1 Weighted'])\n",
    "comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 611
    },
    "executionInfo": {
     "elapsed": 762,
     "status": "ok",
     "timestamp": 1665404086405,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "xfmY9uPfNaBl",
    "outputId": "859615dc-c64d-47e7-9d89-79c7e12f2c31",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "f, axes = plt.subplots(2,1, figsize=(14,10))\n",
    "\n",
    "predict.sort_values(by=['Training Accuracy'], ascending=False, inplace=True)\n",
    "\n",
    "sns.barplot(x='Training Accuracy', y='Model', data = predict, palette='Blues_d', ax = axes[0])\n",
    "#axes[0].set(xlabel='Region', ylabel='Charges')\n",
    "axes[0].set_xlabel('Training Accuracy', size=16)\n",
    "axes[0].set_ylabel('Model')\n",
    "axes[0].set_xlim(0,1.0)\n",
    "axes[0].set_xticks(np.arange(0, 1.1, 0.1))\n",
    "\n",
    "predict.sort_values(by=['Test Accuracy'], ascending=False, inplace=True)\n",
    "\n",
    "sns.barplot(x='Test Accuracy', y='Model', data = predict, palette='Greens_d', ax = axes[1])\n",
    "#axes[0].set(xlabel='Region', ylabel='Charges')\n",
    "axes[1].set_xlabel('Test Accuracy', size=16)\n",
    "axes[1].set_ylabel('Model')\n",
    "axes[1].set_xlim(0,1.0)\n",
    "axes[1].set_xticks(np.arange(0, 1.1, 0.1))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ax_0BYFhQoGl"
   },
   "source": [
    "# simpan kedalam joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "id": "GXb1o8AeNfQf",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1230,
     "status": "ok",
     "timestamp": 1665404099162,
     "user": {
      "displayName": "farrikh alzami",
      "userId": "11964535993149439504"
     },
     "user_tz": -420
    },
    "id": "BM-gUqDMQqmv",
    "outputId": "0da7bb8c-2616-46bd-b1d1-09f873738db0",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['classifier_nb.best_estimator_.sav']"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save the model to disk\n",
    "joblib.dump(classifier_nb.best_estimator_, 'classifier_nb.best_estimator_.sav', compress=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyPYBsAJvbBhGEKY+lDlRj65",
   "collapsed_sections": [],
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}