{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2ae2c777-5a38-4a72-a3f5-34f309b3716a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.Import data dari csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "aef4febf-8655-42c8-a7cc-5cc56caec1b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1fab43e8-27fa-4b6c-a342-7638094f505d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>created_at</th>\n",
       "      <th>id_str</th>\n",
       "      <th>full_text</th>\n",
       "      <th>quote_count</th>\n",
       "      <th>reply_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>lang</th>\n",
       "      <th>user_id_str</th>\n",
       "      <th>conversation_id_str</th>\n",
       "      <th>username</th>\n",
       "      <th>tweet_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sat Feb 10 23:30:48 +0000 2024</td>\n",
       "      <td>1756460740138119204</td>\n",
       "      <td>@geriisme Ini bisa basis partai, financing, ak...</td>\n",
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       "      <td>1756382503865327916</td>\n",
       "      <td>kwaier_</td>\n",
       "      <td>https://twitter.com/kwaier_/status/17564607401...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sat Feb 10 23:30:23 +0000 2024</td>\n",
       "      <td>1756460633535766878</td>\n",
       "      <td>Hallo semuanya!!!! Tak terasa Pemilihan Legisl...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1172089528393203712</td>\n",
       "      <td>1756460633535766878</td>\n",
       "      <td>MudiBanten</td>\n",
       "      <td>https://twitter.com/MudiBanten/status/17564606...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sat Feb 10 23:30:17 +0000 2024</td>\n",
       "      <td>1756460610106413545</td>\n",
       "      <td>2025   Anis presiden  Kader partai pendukungny...</td>\n",
       "      <td>0</td>\n",
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       "      <td>1756460610106413545</td>\n",
       "      <td>jogjaeth</td>\n",
       "      <td>https://twitter.com/jogjaeth/status/1756460610...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sat Feb 10 23:30:00 +0000 2024</td>\n",
       "      <td>1756460540531208457</td>\n",
       "      <td>@calunaeruby @losta_masta_ @peachyvann @mapedo...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>in</td>\n",
       "      <td>1481882706233688064</td>\n",
       "      <td>1755207806284931256</td>\n",
       "      <td>Blokshia</td>\n",
       "      <td>https://twitter.com/Blokshia/status/1756460540...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sat Feb 10 23:29:44 +0000 2024</td>\n",
       "      <td>1756460470264090668</td>\n",
       "      <td>Udah masuk masa tenang tapi cuma ngingetin, ca...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>in</td>\n",
       "      <td>83742072</td>\n",
       "      <td>1756460470264090668</td>\n",
       "      <td>ofuku89</td>\n",
       "      <td>https://twitter.com/ofuku89/status/17564604702...</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>Fri Feb 09 23:11:32 +0000 2024</td>\n",
       "      <td>1756093502600147146</td>\n",
       "      <td>@Rapdo5 @Miduk17 bukan hebat tp culas dan lici...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>in</td>\n",
       "      <td>1538715717838974976</td>\n",
       "      <td>1755995641363534093</td>\n",
       "      <td>MsElva10530471</td>\n",
       "      <td>https://twitter.com/MsElva10530471/status/1756...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>Fri Feb 09 23:11:21 +0000 2024</td>\n",
       "      <td>1756093457985388779</td>\n",
       "      <td>@KakekHalal Kalau melalui proses Pemilu Langsu...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>in</td>\n",
       "      <td>1466979738560258049</td>\n",
       "      <td>1755993623945220130</td>\n",
       "      <td>Aryadwiwarna1</td>\n",
       "      <td>https://twitter.com/Aryadwiwarna1/status/17560...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>Fri Feb 09 23:10:00 +0000 2024</td>\n",
       "      <td>1756093116237389849</td>\n",
       "      <td>Kenalan yuk dengan Caleg DPR RI Partai Demokra...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>in</td>\n",
       "      <td>284830933</td>\n",
       "      <td>1756093116237389849</td>\n",
       "      <td>Demokrat_TV</td>\n",
       "      <td>https://twitter.com/Demokrat_TV/status/1756093...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>Fri Feb 09 23:09:17 +0000 2024</td>\n",
       "      <td>1756092936113267194</td>\n",
       "      <td>@sunprintme aku tempelin sticker gede ya, stic...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>in</td>\n",
       "      <td>1742568163601166336</td>\n",
       "      <td>1756005949641462263</td>\n",
       "      <td>vaneelya</td>\n",
       "      <td>https://twitter.com/vaneelya/status/1756092936...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>Fri Feb 09 23:08:42 +0000 2024</td>\n",
       "      <td>1756092789165785325</td>\n",
       "      <td>@Twillight_Loid @GusbacheV Hebat. Jokowi bener...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>in</td>\n",
       "      <td>1492510742817681409</td>\n",
       "      <td>1755978097164517699</td>\n",
       "      <td>AbdulRozakUB</td>\n",
       "      <td>https://twitter.com/AbdulRozakUB/status/175609...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>546 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         created_at               id_str  \\\n",
       "0    Sat Feb 10 23:30:48 +0000 2024  1756460740138119204   \n",
       "1    Sat Feb 10 23:30:23 +0000 2024  1756460633535766878   \n",
       "2    Sat Feb 10 23:30:17 +0000 2024  1756460610106413545   \n",
       "3    Sat Feb 10 23:30:00 +0000 2024  1756460540531208457   \n",
       "4    Sat Feb 10 23:29:44 +0000 2024  1756460470264090668   \n",
       "..                              ...                  ...   \n",
       "541  Fri Feb 09 23:11:32 +0000 2024  1756093502600147146   \n",
       "542  Fri Feb 09 23:11:21 +0000 2024  1756093457985388779   \n",
       "543  Fri Feb 09 23:10:00 +0000 2024  1756093116237389849   \n",
       "544  Fri Feb 09 23:09:17 +0000 2024  1756092936113267194   \n",
       "545  Fri Feb 09 23:08:42 +0000 2024  1756092789165785325   \n",
       "\n",
       "                                             full_text  quote_count  \\\n",
       "0    @geriisme Ini bisa basis partai, financing, ak...            0   \n",
       "1    Hallo semuanya!!!! Tak terasa Pemilihan Legisl...            0   \n",
       "2    2025   Anis presiden  Kader partai pendukungny...            0   \n",
       "3    @calunaeruby @losta_masta_ @peachyvann @mapedo...            0   \n",
       "4    Udah masuk masa tenang tapi cuma ngingetin, ca...            0   \n",
       "..                                                 ...          ...   \n",
       "541  @Rapdo5 @Miduk17 bukan hebat tp culas dan lici...            0   \n",
       "542  @KakekHalal Kalau melalui proses Pemilu Langsu...            0   \n",
       "543  Kenalan yuk dengan Caleg DPR RI Partai Demokra...            0   \n",
       "544  @sunprintme aku tempelin sticker gede ya, stic...            0   \n",
       "545  @Twillight_Loid @GusbacheV Hebat. Jokowi bener...            0   \n",
       "\n",
       "     reply_count  retweet_count  favorite_count lang          user_id_str  \\\n",
       "0              0              0               0   in  1613742953473900546   \n",
       "1              0              0               0   in  1172089528393203712   \n",
       "2              0              0               0   in  1482641858514022404   \n",
       "3              0              0               1   in  1481882706233688064   \n",
       "4              0              0               0   in             83742072   \n",
       "..           ...            ...             ...  ...                  ...   \n",
       "541            0              0               0   in  1538715717838974976   \n",
       "542            0              0               4   in  1466979738560258049   \n",
       "543            0              0               1   in            284830933   \n",
       "544            1              0               0   in  1742568163601166336   \n",
       "545            0              0               2   in  1492510742817681409   \n",
       "\n",
       "     conversation_id_str        username  \\\n",
       "0    1756382503865327916         kwaier_   \n",
       "1    1756460633535766878      MudiBanten   \n",
       "2    1756460610106413545        jogjaeth   \n",
       "3    1755207806284931256        Blokshia   \n",
       "4    1756460470264090668         ofuku89   \n",
       "..                   ...             ...   \n",
       "541  1755995641363534093  MsElva10530471   \n",
       "542  1755993623945220130   Aryadwiwarna1   \n",
       "543  1756093116237389849     Demokrat_TV   \n",
       "544  1756005949641462263        vaneelya   \n",
       "545  1755978097164517699    AbdulRozakUB   \n",
       "\n",
       "                                             tweet_url  \n",
       "0    https://twitter.com/kwaier_/status/17564607401...  \n",
       "1    https://twitter.com/MudiBanten/status/17564606...  \n",
       "2    https://twitter.com/jogjaeth/status/1756460610...  \n",
       "3    https://twitter.com/Blokshia/status/1756460540...  \n",
       "4    https://twitter.com/ofuku89/status/17564604702...  \n",
       "..                                                 ...  \n",
       "541  https://twitter.com/MsElva10530471/status/1756...  \n",
       "542  https://twitter.com/Aryadwiwarna1/status/17560...  \n",
       "543  https://twitter.com/Demokrat_TV/status/1756093...  \n",
       "544  https://twitter.com/vaneelya/status/1756092936...  \n",
       "545  https://twitter.com/AbdulRozakUB/status/175609...  \n",
       "\n",
       "[546 rows x 12 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data_test.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e500eda1-311d-4691-9b41-553c57cac920",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_at</th>\n",
       "      <th>full_text</th>\n",
       "      <th>user_id_str</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sat Feb 10 23:30:48 +0000 2024</td>\n",
       "      <td>@geriisme Ini bisa basis partai, financing, ak...</td>\n",
       "      <td>1613742953473900546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sat Feb 10 23:30:23 +0000 2024</td>\n",
       "      <td>Hallo semuanya!!!! Tak terasa Pemilihan Legisl...</td>\n",
       "      <td>1172089528393203712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sat Feb 10 23:30:17 +0000 2024</td>\n",
       "      <td>2025   Anis presiden  Kader partai pendukungny...</td>\n",
       "      <td>1482641858514022404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sat Feb 10 23:30:00 +0000 2024</td>\n",
       "      <td>@calunaeruby @losta_masta_ @peachyvann @mapedo...</td>\n",
       "      <td>1481882706233688064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sat Feb 10 23:29:44 +0000 2024</td>\n",
       "      <td>Udah masuk masa tenang tapi cuma ngingetin, ca...</td>\n",
       "      <td>83742072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>Fri Feb 09 23:11:32 +0000 2024</td>\n",
       "      <td>@Rapdo5 @Miduk17 bukan hebat tp culas dan lici...</td>\n",
       "      <td>1538715717838974976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>Fri Feb 09 23:11:21 +0000 2024</td>\n",
       "      <td>@KakekHalal Kalau melalui proses Pemilu Langsu...</td>\n",
       "      <td>1466979738560258049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>Fri Feb 09 23:10:00 +0000 2024</td>\n",
       "      <td>Kenalan yuk dengan Caleg DPR RI Partai Demokra...</td>\n",
       "      <td>284830933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>Fri Feb 09 23:09:17 +0000 2024</td>\n",
       "      <td>@sunprintme aku tempelin sticker gede ya, stic...</td>\n",
       "      <td>1742568163601166336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>Fri Feb 09 23:08:42 +0000 2024</td>\n",
       "      <td>@Twillight_Loid @GusbacheV Hebat. Jokowi bener...</td>\n",
       "      <td>1492510742817681409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>546 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         created_at  \\\n",
       "0    Sat Feb 10 23:30:48 +0000 2024   \n",
       "1    Sat Feb 10 23:30:23 +0000 2024   \n",
       "2    Sat Feb 10 23:30:17 +0000 2024   \n",
       "3    Sat Feb 10 23:30:00 +0000 2024   \n",
       "4    Sat Feb 10 23:29:44 +0000 2024   \n",
       "..                              ...   \n",
       "541  Fri Feb 09 23:11:32 +0000 2024   \n",
       "542  Fri Feb 09 23:11:21 +0000 2024   \n",
       "543  Fri Feb 09 23:10:00 +0000 2024   \n",
       "544  Fri Feb 09 23:09:17 +0000 2024   \n",
       "545  Fri Feb 09 23:08:42 +0000 2024   \n",
       "\n",
       "                                             full_text          user_id_str  \n",
       "0    @geriisme Ini bisa basis partai, financing, ak...  1613742953473900546  \n",
       "1    Hallo semuanya!!!! Tak terasa Pemilihan Legisl...  1172089528393203712  \n",
       "2    2025   Anis presiden  Kader partai pendukungny...  1482641858514022404  \n",
       "3    @calunaeruby @losta_masta_ @peachyvann @mapedo...  1481882706233688064  \n",
       "4    Udah masuk masa tenang tapi cuma ngingetin, ca...             83742072  \n",
       "..                                                 ...                  ...  \n",
       "541  @Rapdo5 @Miduk17 bukan hebat tp culas dan lici...  1538715717838974976  \n",
       "542  @KakekHalal Kalau melalui proses Pemilu Langsu...  1466979738560258049  \n",
       "543  Kenalan yuk dengan Caleg DPR RI Partai Demokra...            284830933  \n",
       "544  @sunprintme aku tempelin sticker gede ya, stic...  1742568163601166336  \n",
       "545  @Twillight_Loid @GusbacheV Hebat. Jokowi bener...  1492510742817681409  \n",
       "\n",
       "[546 rows x 3 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[[ 'created_at','full_text','user_id_str']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "aa073590-2ad7-4e6e-bd1e-0eb5f1b7717c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.cleaning data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "78149731-c5e3-4cb4-a586-496c52963645",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(546, 3)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "5eb1c631-73e1-4332-9810-dd3572d6832b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "f5ce7f88-13f7-4dd6-a10c-b44614150dee",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop_duplicates(subset=['full_text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "a7dd506c-d8ed-4e2e-ba05-6a23b53cd881",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "1b0ef15a-be11-45ea-a7d1-29c908884f6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "created_at     0\n",
       "full_text      0\n",
       "user_id_str    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "4cb70598-a556-4918-9302-db96c085dd67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(513, 3)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "cd4f2551-a87f-4bdc-a1ed-b0dc44b85421",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_twitter_text(text):\n",
    "    # Menghapus karakter @mentions, #hastag, dan url\n",
    "    text = re.sub(r'@[A-Za-z0-9_]+', '', text)\n",
    "    text = re.sub(r'#\\w+', '', text)\n",
    "    text = re.sub(r'RT[\\s]+', '', text)\n",
    "    text = re.sub(r'https?://\\S+', '', text)\n",
    "\n",
    "    text = re.sub(r'[^A-Za-z0-9 ]', '', text)\n",
    "    text = re.sub(r'\\s+', ' ', text)\n",
    "\n",
    "    return text\n",
    "\n",
    "# Memanggil fungsi clean_twitter_text() setelah didefinisikan\n",
    "df.loc[:, 'full_text'] = df['full_text'].apply(clean_twitter_text)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "c200e99f-2770-41dd-818f-a11d93429436",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>Sat Feb 10 23:30:48 +0000 2024</td>\n",
       "      <td>ini bisa basis partai financing akses ke figu...</td>\n",
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       "      <td>hallo semuanya tak terasa pemilihan legislatif...</td>\n",
       "      <td>1172089528393203712</td>\n",
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       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>Fri Feb 09 23:11:21 +0000 2024</td>\n",
       "      <td>kalau melalui proses pemilu langsung menurut ...</td>\n",
       "      <td>1466979738560258049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>Fri Feb 09 23:10:00 +0000 2024</td>\n",
       "      <td>kenalan yuk dengan caleg dpr ri partai demokra...</td>\n",
       "      <td>284830933</td>\n",
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       "      <th>544</th>\n",
       "      <td>Fri Feb 09 23:09:17 +0000 2024</td>\n",
       "      <td>aku tempelin sticker gede ya sticker partai</td>\n",
       "      <td>1742568163601166336</td>\n",
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       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>Fri Feb 09 23:08:42 +0000 2024</td>\n",
       "      <td>hebat jokowi bener2 serius buat bangsa ini be...</td>\n",
       "      <td>1492510742817681409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>513 rows × 3 columns</p>\n",
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      ],
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       "                         created_at  \\\n",
       "0    Sat Feb 10 23:30:48 +0000 2024   \n",
       "1    Sat Feb 10 23:30:23 +0000 2024   \n",
       "2    Sat Feb 10 23:30:17 +0000 2024   \n",
       "3    Sat Feb 10 23:30:00 +0000 2024   \n",
       "4    Sat Feb 10 23:29:44 +0000 2024   \n",
       "..                              ...   \n",
       "541  Fri Feb 09 23:11:32 +0000 2024   \n",
       "542  Fri Feb 09 23:11:21 +0000 2024   \n",
       "543  Fri Feb 09 23:10:00 +0000 2024   \n",
       "544  Fri Feb 09 23:09:17 +0000 2024   \n",
       "545  Fri Feb 09 23:08:42 +0000 2024   \n",
       "\n",
       "                                             full_text          user_id_str  \n",
       "0     ini bisa basis partai financing akses ke figu...  1613742953473900546  \n",
       "1    hallo semuanya tak terasa pemilihan legislatif...  1172089528393203712  \n",
       "2    2025 anis presiden kader partai pendukungnya k...  1482641858514022404  \n",
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       "4    udah masuk masa tenang tapi cuma ngingetin cal...             83742072  \n",
       "..                                                 ...                  ...  \n",
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       "543  kenalan yuk dengan caleg dpr ri partai demokra...            284830933  \n",
       "544        aku tempelin sticker gede ya sticker partai  1742568163601166336  \n",
       "545   hebat jokowi bener2 serius buat bangsa ini be...  1492510742817681409  \n",
       "\n",
       "[513 rows x 3 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, 'full_text'] = df['full_text'].str.lower()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "6cf0e8d9-5364-4c8b-8d4e-5b6580b496d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3.prepocessing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "6ce3bf2a-c24b-4dec-85f8-aaab10b1316f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3.1 Normalisasi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "864462f3-d623-4c73-aa98-42b34adf39b4",
   "metadata": {},
   "outputs": [
    {
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       "                         created_at  \\\n",
       "0    Sat Feb 10 23:30:48 +0000 2024   \n",
       "1    Sat Feb 10 23:30:23 +0000 2024   \n",
       "2    Sat Feb 10 23:30:17 +0000 2024   \n",
       "3    Sat Feb 10 23:30:00 +0000 2024   \n",
       "4    Sat Feb 10 23:29:44 +0000 2024   \n",
       "..                              ...   \n",
       "541  Fri Feb 09 23:11:32 +0000 2024   \n",
       "542  Fri Feb 09 23:11:21 +0000 2024   \n",
       "543  Fri Feb 09 23:10:00 +0000 2024   \n",
       "544  Fri Feb 09 23:09:17 +0000 2024   \n",
       "545  Fri Feb 09 23:08:42 +0000 2024   \n",
       "\n",
       "                                             full_text          user_id_str  \n",
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       "1    hallo semuanya tak terasa pemilihan legislatif...  1172089528393203712  \n",
       "2    2025 anis presiden kader partai pendukungnya k...  1482641858514022404  \n",
       "3     u think hal ky gini pure dr paslon 02 mereka ...  1481882706233688064  \n",
       "4    udah masuk masa tenang tapi cuma ngingetin cal...             83742072  \n",
       "..                                                 ...                  ...  \n",
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       "543  kenalan yuk dengan caleg dpr ri partai demokra...            284830933  \n",
       "544        aku tempelin sticker gede ya sticker partai  1742568163601166336  \n",
       "545   hebat jokowi bener2 serius buat bangsa ini be...  1492510742817681409  \n",
       "\n",
       "[513 rows x 3 columns]"
      ]
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     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "50f2c622-b3d9-4914-b634-7a55ae358def",
   "metadata": {},
   "outputs": [
    {
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       "                         created_at  \\\n",
       "0    Sat Feb 10 23:30:48 +0000 2024   \n",
       "1    Sat Feb 10 23:30:23 +0000 2024   \n",
       "2    Sat Feb 10 23:30:17 +0000 2024   \n",
       "3    Sat Feb 10 23:30:00 +0000 2024   \n",
       "4    Sat Feb 10 23:29:44 +0000 2024   \n",
       "..                              ...   \n",
       "541  Fri Feb 09 23:11:32 +0000 2024   \n",
       "542  Fri Feb 09 23:11:21 +0000 2024   \n",
       "543  Fri Feb 09 23:10:00 +0000 2024   \n",
       "544  Fri Feb 09 23:09:17 +0000 2024   \n",
       "545  Fri Feb 09 23:08:42 +0000 2024   \n",
       "\n",
       "                                             full_text          user_id_str  \n",
       "0    ini bisa basis partai financing akses ke figur...  1613742953473900546  \n",
       "1    hallo semuanya tak terasa pemilihan legislatif...  1172089528393203712  \n",
       "2    2025 anis presiden kader partai pendukungnya k...  1482641858514022404  \n",
       "3    u think hal ky begini pure dari pasangan calon...  1481882706233688064  \n",
       "4    sudah masuk masa tenang tapi cuma ngingetin ca...             83742072  \n",
       "..                                                 ...                  ...  \n",
       "541  bukan hebat tetapi culas dan licik di angkat d...  1538715717838974976  \n",
       "542  kalau melalui proses pemilihan umum langsung m...  1466979738560258049  \n",
       "543  kenalan yuk dengan calon legislatif dewan perw...            284830933  \n",
       "544       aku tempelin sticker besar ya sticker partai  1742568163601166336  \n",
       "545  hebat jokowi bener2 serius buat bangsa ini bel...  1492510742817681409  \n",
       "\n",
       "[513 rows x 3 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm = {\n",
    "    'emang': 'memang',\n",
    "    'yg': 'yang',\n",
    "    'tau': 'tahu',\n",
    "    'itu': 'itu',\n",
    "    'ngga': 'tidak',\n",
    "    'gak': 'tidak',\n",
    "    'udah': 'sudah',\n",
    "    'ngomong': 'berbicara',\n",
    "    'sampe': 'sampai',\n",
    "    'tsb': 'tersebut',\n",
    "    'aja': 'saja',\n",
    "    'gaess': 'teman-teman',\n",
    "    'gue': 'saya',\n",
    "    'bgt': 'banget',\n",
    "    'bro': 'sobat',\n",
    "    'loh': 'lho',\n",
    "    'klo': 'kalau',\n",
    "    'gtu': 'begitu',\n",
    "    'wkwkwk': 'tertawa',\n",
    "    'mbak': 'kakak perempuan',\n",
    "    'masnya': 'kakak laki-laki',\n",
    "    'nih': 'ini',\n",
    "    'sbg': 'sebagai',\n",
    "    'ampun': 'maaf',\n",
    "    'tp': 'tetapi',\n",
    "    'krna': 'karena',\n",
    "    'jd': 'jadi',\n",
    "    'kl': 'kalau',\n",
    "    'klh': 'kalah',\n",
    "    'bs': 'bisa',\n",
    "    'dlu': 'dulu',\n",
    "    'cm': 'hanya',\n",
    "    'ntn': 'menonton',\n",
    "    'blm': 'belum',\n",
    "    'klu': 'kalau',\n",
    "    'skrg': 'sekarang',\n",
    "    'mu': 'dirimu',\n",
    "    'kmu': 'kamu',\n",
    "    'dgn': 'dengan',\n",
    "    'nyinyir': 'bercanda',\n",
    "    'sosmed': 'media sosial',\n",
    "    'mk': 'mahkamah konstitusi',\n",
    "    'nyerah': 'menyerah',\n",
    "    'ngetren': 'populer',\n",
    "    'kwkwk': 'tertawa',\n",
    "    'klw': 'kalau',\n",
    "    'gmn': 'bagaimana',\n",
    "    'gaada': 'tidak ada',\n",
    "    'mjd': 'menjadi',\n",
    "    'yaa': 'ya',\n",
    "    'jg': 'juga',\n",
    "    'biar': 'agar',\n",
    "    'masi': 'masih',\n",
    "    'jgn': 'jangan',\n",
    "    'emg': 'memang',\n",
    "    'hmm': 'hmm',\n",
    "    'bodoamat': 'tidak peduli',\n",
    "    'kayak': 'seperti',\n",
    "    'apapun': 'apapun',\n",
    "    'ga': 'tidak',\n",
    "    'muji': 'memuji',\n",
    "    'td': 'tadi',\n",
    "    'napa': 'kenapa',\n",
    "    'ketum': 'ketua umum',\n",
    "    'ngegas': 'bersikap tegas',\n",
    "    'bener': 'benar',\n",
    "    'lg': 'lagi',\n",
    "    'skrng': 'sekarang',\n",
    "    'knp': 'kenapa',\n",
    "    'yaudah': 'ya sudah',\n",
    "    'tdk': 'tidak',\n",
    "    'pdhl': 'padahal',\n",
    "    'bngt': 'banget',\n",
    "    'kasian': 'kasihan',\n",
    "    'dasar': 'dasar',\n",
    "    'akuny': 'akunnya',\n",
    "    'kok': 'kenapa',\n",
    "    'paslon': 'pasangan calon',\n",
    "    'pemilu': 'pemilihan umum',\n",
    "    'pilpres': 'pemilihan presiden',\n",
    "    'caleg': 'calon legislatif',\n",
    "    'timses': 'tim sukses',\n",
    "    'tpi': 'tetapi',\n",
    "    'lah': 'lah',\n",
    "    'ngaku': 'mengaku',\n",
    "    'dpr': 'dewan perwakilan rakyat',\n",
    "    'dprd': 'dewan perwakilan rakyat daerah',\n",
    "    'dpd': 'dewan perwakilan daerah',\n",
    "    'ngasih': 'memberikan',\n",
    "    'doang': 'saja',\n",
    "    'pdip': 'partai demokrasi indonesia perjuangan',\n",
    "    'kepantasan': 'kelayakan',\n",
    "    'jaman': 'zaman',\n",
    "    'rebo': 'rabu',\n",
    "    'tmsk': 'termasuk',\n",
    "    'lu': 'kamu',\n",
    "    'palingan': 'paling',\n",
    "    'lebih': 'lebih',\n",
    "    'jelasin': 'jelaskan',\n",
    "    'ini': 'ini',\n",
    "    'kalimatnya': 'kalimatnya',\n",
    "    'sm': 'sama',\n",
    "    'sklrg': 'sekarang',\n",
    "    'diatas': 'di atas',\n",
    "    'bnyk': 'banyak',\n",
    "    'jd': 'jadi',\n",
    "    'bocor': 'bocor',\n",
    "    'sbb': 'sebab',\n",
    "    'bodoamat': 'tidak peduli',\n",
    "    'bgst': 'bangsat',\n",
    "    'pd': 'pada',\n",
    "    'sma': 'sama',\n",
    "    'bego': 'bodoh',\n",
    "    'sbgi': 'sebagai',\n",
    "    'blm': 'belum',\n",
    "    'knp': 'kenapa',\n",
    "    'gitu': 'begitu',\n",
    "    'lucu2': 'lucu-lucu',\n",
    "    'sih': 'sih',\n",
    "    'mikir': 'berpikir',\n",
    "    'lu': 'kamu',\n",
    "    'gini': 'begini',\n",
    "    'apaan': 'apa',\n",
    "    'kgk': 'tidak',\n",
    "    'dr': 'dari',\n",
    "    'tuk': 'untuk',\n",
    "    'nah': 'nah',\n",
    "    'yaudah': 'ya sudah',\n",
    "    'nntn': 'menonton',\n",
    "    'tahka': 'tahta',\n",
    "    'ngapa' : 'mengapa',\n",
    "    'isteri' : 'istri',\n",
    "    'alm'   : 'almarhum',\n",
    "    'utk'   : 'untuk',\n",
    "    'btw' : 'omong-omong',\n",
    "    'pks' : 'Partai Keadilan Sejahtera',\n",
    "    'ngebolehin' : 'mengizinkan',\n",
    "    'ttg': 'tentang',\n",
    "    'gede': 'besar',\n",
    "    'rebu': 'ribu',\n",
    "}\n",
    "\n",
    "def normalisasi_text(text):\n",
    "    if isinstance(text, str):\n",
    "        words = text.split()\n",
    "        normalized_words = [norm[word] if word in norm else word for word in words]\n",
    "        return ' '.join(normalized_words)\n",
    "    else:\n",
    "        return text\n",
    "\n",
    "# Melakukan normalisasi pada kolom 'full_text'\n",
    "df.loc[:, 'full_text'] = df['full_text'].apply(normalisasi_text)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "c0e99d53-41b7-41b7-af54-1a61365545b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#3.2 Stopword"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "a276f5b9-5033-4028-801a-f2ba96ca53ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: Sastrawi in c:\\laragon\\bin\\python\\python-3.10\\lib\\site-packages (1.0.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install Sastrawi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "d08abf95-4264-41de-8075-5b37ce43847a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>user_id_str</th>\n",
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       "      <th>0</th>\n",
       "      <td>Sat Feb 10 23:30:48 +0000 2024</td>\n",
       "      <td>bisa basis partai financing akses figure milit...</td>\n",
       "      <td>1613742953473900546</td>\n",
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       "      <th>1</th>\n",
       "      <td>Sat Feb 10 23:30:23 +0000 2024</td>\n",
       "      <td>hallo semuanya tak terasa pemilihan legislatif...</td>\n",
       "      <td>1172089528393203712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sat Feb 10 23:30:17 +0000 2024</td>\n",
       "      <td>2025 anis presiden kader partai pendukungnya k...</td>\n",
       "      <td>1482641858514022404</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sat Feb 10 23:30:00 +0000 2024</td>\n",
       "      <td>u think ky begini pure pasangan calon 02 kan p...</td>\n",
       "      <td>1481882706233688064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sat Feb 10 23:29:44 +0000 2024</td>\n",
       "      <td>masuk masa tenang cuma ngingetin calon legisla...</td>\n",
       "      <td>83742072</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>Fri Feb 09 23:11:32 +0000 2024</td>\n",
       "      <td>bukan hebat culas licik angkat nol smpai berku...</td>\n",
       "      <td>1538715717838974976</td>\n",
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       "      <th>542</th>\n",
       "      <td>Fri Feb 09 23:11:21 +0000 2024</td>\n",
       "      <td>kalau melalui proses pemilihan umum langsung s...</td>\n",
       "      <td>1466979738560258049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>Fri Feb 09 23:10:00 +0000 2024</td>\n",
       "      <td>kenalan yuk calon legislatif dewan perwakilan ...</td>\n",
       "      <td>284830933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>Fri Feb 09 23:09:17 +0000 2024</td>\n",
       "      <td>aku tempelin sticker besar sticker partai</td>\n",
       "      <td>1742568163601166336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>Fri Feb 09 23:08:42 +0000 2024</td>\n",
       "      <td>hebat jokowi bener2 serius buat bangsa beliyau...</td>\n",
       "      <td>1492510742817681409</td>\n",
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       "<p>513 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         created_at  \\\n",
       "0    Sat Feb 10 23:30:48 +0000 2024   \n",
       "1    Sat Feb 10 23:30:23 +0000 2024   \n",
       "2    Sat Feb 10 23:30:17 +0000 2024   \n",
       "3    Sat Feb 10 23:30:00 +0000 2024   \n",
       "4    Sat Feb 10 23:29:44 +0000 2024   \n",
       "..                              ...   \n",
       "541  Fri Feb 09 23:11:32 +0000 2024   \n",
       "542  Fri Feb 09 23:11:21 +0000 2024   \n",
       "543  Fri Feb 09 23:10:00 +0000 2024   \n",
       "544  Fri Feb 09 23:09:17 +0000 2024   \n",
       "545  Fri Feb 09 23:08:42 +0000 2024   \n",
       "\n",
       "                                             full_text          user_id_str  \n",
       "0    bisa basis partai financing akses figure milit...  1613742953473900546  \n",
       "1    hallo semuanya tak terasa pemilihan legislatif...  1172089528393203712  \n",
       "2    2025 anis presiden kader partai pendukungnya k...  1482641858514022404  \n",
       "3    u think ky begini pure pasangan calon 02 kan p...  1481882706233688064  \n",
       "4    masuk masa tenang cuma ngingetin calon legisla...             83742072  \n",
       "..                                                 ...                  ...  \n",
       "541  bukan hebat culas licik angkat nol smpai berku...  1538715717838974976  \n",
       "542  kalau melalui proses pemilihan umum langsung s...  1466979738560258049  \n",
       "543  kenalan yuk calon legislatif dewan perwakilan ...            284830933  \n",
       "544          aku tempelin sticker besar sticker partai  1742568163601166336  \n",
       "545  hebat jokowi bener2 serius buat bangsa beliyau...  1492510742817681409  \n",
       "\n",
       "[513 rows x 3 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from Sastrawi.StopWordRemover.StopWordRemoverFactory import StopWordRemoverFactory, StopWordRemover, ArrayDictionary\n",
    "\n",
    "# Tambahkan stop words tambahan\n",
    "more_stop_words = [\"tidak\"]\n",
    "\n",
    "# Buat stop word remover factory\n",
    "factory = StopWordRemoverFactory()\n",
    "stop_words = factory.get_stop_words()\n",
    "stop_words.extend(more_stop_words)\n",
    "\n",
    "# Buat array dictionary dengan stop words baru\n",
    "new_array = ArrayDictionary(stop_words)\n",
    "stop_word_remover = StopWordRemover(new_array)\n",
    "\n",
    "# Fungsi untuk menghapus stop words\n",
    "def remove_stop_words(text):\n",
    "    return stop_word_remover.remove(text)\n",
    "\n",
    "# Menggunakan .loc untuk menghindari SettingWithCopyWarning\n",
    "df.loc[:, 'full_text'] = df['full_text'].apply(remove_stop_words)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "f2d91400-10b1-43da-a8ce-f30ea6b031c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3.3 Tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "054bc36a-81c6-4a83-81a2-cb7440a65bcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\Rizqi\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "C:\\Users\\Rizqi\\AppData\\Local\\Temp\\ipykernel_1452\\2370743566.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.loc[:, 'tokenized'] = df['full_text'].apply(word_tokenize)\n"
     ]
    },
    {
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      "text/plain": [
       "                                             full_text  \\\n",
       "0    bisa basis partai financing akses figure milit...   \n",
       "1    hallo semuanya tak terasa pemilihan legislatif...   \n",
       "2    2025 anis presiden kader partai pendukungnya k...   \n",
       "3    u think ky begini pure pasangan calon 02 kan p...   \n",
       "4    masuk masa tenang cuma ngingetin calon legisla...   \n",
       "..                                                 ...   \n",
       "541  bukan hebat culas licik angkat nol smpai berku...   \n",
       "542  kalau melalui proses pemilihan umum langsung s...   \n",
       "543  kenalan yuk calon legislatif dewan perwakilan ...   \n",
       "544          aku tempelin sticker besar sticker partai   \n",
       "545  hebat jokowi bener2 serius buat bangsa beliyau...   \n",
       "\n",
       "                                             tokenized  \n",
       "0    [bisa, basis, partai, financing, akses, figure...  \n",
       "1    [hallo, semuanya, tak, terasa, pemilihan, legi...  \n",
       "2    [2025, anis, presiden, kader, partai, pendukun...  \n",
       "3    [u, think, ky, begini, pure, pasangan, calon, ...  \n",
       "4    [masuk, masa, tenang, cuma, ngingetin, calon, ...  \n",
       "..                                                 ...  \n",
       "541  [bukan, hebat, culas, licik, angkat, nol, smpa...  \n",
       "542  [kalau, melalui, proses, pemilihan, umum, lang...  \n",
       "543  [kenalan, yuk, calon, legislatif, dewan, perwa...  \n",
       "544   [aku, tempelin, sticker, besar, sticker, partai]  \n",
       "545  [hebat, jokowi, bener2, serius, buat, bangsa, ...  \n",
       "\n",
       "[513 rows x 2 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "import pandas as pd\n",
    "\n",
    "# Unduh data punkt\n",
    "nltk.download('punkt')\n",
    "# Menggunakan .loc untuk menghindari SettingWithCopyWarning\n",
    "df.loc[:, 'tokenized'] = df['full_text'].apply(word_tokenize)\n",
    "\n",
    "# Tampilkan hasil tokenisasi\n",
    "df[['full_text', 'tokenized']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "12c8b002-54f9-4ac1-a1bb-b5ab33697ba3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sebelum stemming: bisa basis partai financing akses figure militansi sejarah budaya menarik kalau liat pemilihan umum philippines taun lalu rame banget milih leni robredo berhasil dapet simpati semua kalangan masih kalah marcosduterte but lets see on 14\n",
      "Sesudah stemming: bisa basis partai financing akses figure militansi sejarah budaya tarik kalau liat pilih umum philippines taun lalu rame banget milih leni robredo hasil dapet simpati semua kalang masih kalah marcosduterte but lets see on 14\n",
      "\n",
      "Sebelum stemming: hallo semuanya tak terasa pemilihan legislatif tinggal menghitung hari yah jangan lupa tanggal 14 februari datang tps coblos ricky kurniawan chairul calon legislatif dewan perwakilan rakyat daerah provinsi banten dapil tangerang c nomor urut 1 partai demokrat semangat\n",
      "Sesudah stemming: hallo semua tak asa pilih legislatif tinggal hitung hari yah jangan lupa tanggal 14 februari datang tps coblos ricky kurniawan chairul calon legislatif dewan wakil rakyat daerah provinsi banten dapil tangerang c nomor urut 1 partai demokrat semangat\n",
      "\n",
      "Sebelum stemming: 2025 anis presiden kader partai pendukungnya korupsi suporter anis kampret kamu korupsi kalau jelek bilang jelek kami cebokin ya tetep korupsinya\n",
      "Sesudah stemming: 2025 anis presiden kader partai dukung korupsi suporter anis kampret kamu korupsi kalau jelek bilang jelek kami cebokin ya tetep korupsi\n",
      "\n",
      "Sebelum stemming: u think ky begini pure pasangan calon 02 kan punya timsespunya partai dn mereka punya gmna konsep kampanye berkedok sedekah omong-omong literasi dh gk cuma konsep 02 0103 sama lagian dulu metode berbagi ky begini memang udh\n",
      "Sesudah stemming: u think ky begini pure pasang calon 02 kan punya timsespunya partai dn mereka punya gmna konsep kampanye kedok sedekah omong literasi dh gk cuma konsep 02 0103 sama lagi dulu metode bagi ky begini memang udh\n",
      "\n",
      "Sebelum stemming: masuk masa tenang cuma ngingetin calon legislatif tu anggota partai jadi akan bekerja tujuan partai fitrahnya memang dah saja selamat mencoblos\n",
      "Sesudah stemming: masuk masa tenang cuma ngingetin calon legislatif tu anggota partai jadi akan kerja tuju partai fitrah memang dah saja selamat coblos\n",
      "\n",
      "Sebelum stemming: komposisi non muslim besar 03 menang ln partai demokrasi indonesia perjuangan menang partai memberi cek kosong jokowi sekarag rasakaan akibatnya\n",
      "Sesudah stemming: komposisi non muslim besar 03 menang ln partai demokrasi indonesia juang menang partai beri cek kosong jokowi sekarag rasakaan akibat\n",
      "\n",
      "Sebelum stemming: bersama partai demokrat wujudkan kesejahteraan bersama bisa ricky kurniawan chairul dewan perwakilan rakyat daerah provinsi banten dapil tangerang c\n",
      "Sesudah stemming: sama partai demokrat wujud sejahtera sama bisa ricky kurniawan chairul dewan wakil rakyat daerah provinsi banten dapil tangerang c\n",
      "\n",
      "Sebelum stemming: sejarah mencatat ada pemilihan presiden didukung partai tersandera kasus korupsi melanggar etik berat paman mahkamah konstitusi paman kpu melakukan pembagian bansos bukan kepentingan rakyat ingat dosa ditanggung pendukung kezoliman\n",
      "Sesudah stemming: sejarah catat ada pilih presiden dukung partai sandera kasus korupsi langgar etik berat paman mahkamah konstitusi paman kpu laku bagi bansos bukan penting rakyat ingat dosa tanggung dukung kezoliman\n",
      "\n",
      "Sebelum stemming: apa diharapkan debat partai kosong begini mas partai tidak sengaja dibuat partai dibuat pelengkap rame2 an\n",
      "Sesudah stemming: apa harap debat partai kosong begini mas partai tidak sengaja buat partai buat lengkap rame2 an\n",
      "\n",
      "Sebelum stemming: allah selalu memengkan partai partai islam masuk parlemen jangan psi masuk\n",
      "Sesudah stemming: allah selalu kan partai partai islam masuk parlemen jangan psi masuk\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from Sastrawi.Stemmer.StemmerFactory import StemmerFactory\n",
    "import pandas as pd\n",
    "\n",
    "# Fungsi untuk melakukan stemming pada setiap kalimat\n",
    "def stemming(text):\n",
    "    factory = StemmerFactory()\n",
    "    stemmer = factory.create_stemmer()\n",
    "    return stemmer.stem(text)\n",
    "\n",
    "# Mengambil kolom yang diperlukan\n",
    "df = df[['created_at', 'full_text', 'user_id_str']]\n",
    "\n",
    "# Melakukan stemming pada kolom 'full_text'\n",
    "df['stemmed_text'] = df['full_text'].apply(stemming)\n",
    "\n",
    "# Menyimpan hasil ke dalam file CSV dengan kolom yang diminta\n",
    "df.to_csv('data_test_fix.csv', index=False)\n",
    "\n",
    "# Cetak hasil sebelum dan sesudah stemming setelah 10 baris pertama di-Stem\n",
    "stemmed_df = df.head(10)\n",
    "for index, row in stemmed_df.iterrows():\n",
    "    print(\"Sebelum stemming:\", row['full_text'])\n",
    "    print(\"Sesudah stemming:\", row['stemmed_text'])\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "52dfb7d0-ef5e-4926-b7e1-a055e1551138",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          created_at  \\\n",
      "0     Sat Feb 10 23:59:55 +0000 2024   \n",
      "1     Sat Feb 10 23:59:51 +0000 2024   \n",
      "2     Sat Feb 10 23:59:46 +0000 2024   \n",
      "3     Sat Feb 10 23:59:34 +0000 2024   \n",
      "4     Sat Feb 10 23:59:28 +0000 2024   \n",
      "...                              ...   \n",
      "1299  Fri Feb 09 23:25:58 +0000 2024   \n",
      "1300  Fri Feb 09 23:25:56 +0000 2024   \n",
      "1301  Fri Feb 09 23:23:59 +0000 2024   \n",
      "1302  Fri Feb 09 23:23:52 +0000 2024   \n",
      "1303  Fri Feb 09 23:23:29 +0000 2024   \n",
      "\n",
      "                                              full_text          user_id_str  \\\n",
      "0                tahu partai nya gabener di pilih hadeh  1489144415021322242   \n",
      "1     bukan tahun 2000an awal orang2 Partai Keadilan...  1358428069845889026   \n",
      "2     imagine invalidating someones fear and calling...  1334684348184887297   \n",
      "3     jangan lupa yah teman-teman 2024 pilih partai ...  1679186924685385728   \n",
      "4     h3 pemilihan umum heran seakan2 jokowi dosanya...            171050686   \n",
      "...                                                 ...                  ...   \n",
      "1299  tahu orang tidak pernah mengkampanyekan partai...  1172134585502588929   \n",
      "1300  zarr tahu hati kecil kamu mengakui yang benar ...  1017062768631898113   \n",
      "1301                             pilih partai buruh eti            226128927   \n",
      "1302  coba kau tanya presiden jokowiyuwono ibu megaw...  1487765772130992129   \n",
      "1303  terkesan ketua partai politik presiden atas pr...  1466979738560258049   \n",
      "\n",
      "                                           stemmed_text label_text_number  \n",
      "0                tahu partai nya gabener di pilih hadeh           negatif  \n",
      "1     bukan tahun 2000an awal orang2 partai adil sej...           negatif  \n",
      "2     imagine invalidating someones fear and calling...            netral  \n",
      "3     jangan lupa yah teman 2024 pilih partai ummat ...           positif  \n",
      "4     h3 pilih umum heran seakan2 jokowi dosa paling...           positif  \n",
      "...                                                 ...               ...  \n",
      "1299  tahu orang tidak pernah kampanye partai nya do...           negatif  \n",
      "1300  zarr tahu hati kecil kamu aku yang benar ayo p...            netral  \n",
      "1301                             pilih partai buruh eti           positif  \n",
      "1302  coba kau tanya presiden jokowiyuwono ibu megaw...           negatif  \n",
      "1303  kes ketua partai politik presiden atas preside...           negatif  \n",
      "\n",
      "[1304 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Baca file CSV ke DataFrame\n",
    "data = pd.read_csv(\"labeled_data.csv\", index_col=False)\n",
    "\n",
    "# Ubah nilai kolom label_text_number\n",
    "data['label_text_number'] = data['label_text_number'].replace({1: 'positif', 2: 'negatif', 3: 'netral'})\n",
    "\n",
    "# Tampilkan DataFrame yang sudah diubah\n",
    "print(data)\n",
    "\n",
    "# Simpan DataFrame yang sudah diubah ke file CSV baru\n",
    "data.to_csv('labeled_data_updated.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "3765c433-6377-4f05-b957-666bbcbf7b2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[149  21  11]\n",
      " [ 74  37  13]\n",
      " [ 43  12  32]]\n",
      "Validation Accuracy: 0.5561224489795918\n",
      "Prediksi untuk data uji telah disimpan dalam 'data_test_with_predictions_higher_accuracy.csv'\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Baca data pelatihan dan data uji\n",
    "data_train = pd.read_csv('labeled_data_updated.csv', index_col=False)\n",
    "data_test = pd.read_csv('data_test_fix.csv', index_col=False)\n",
    "\n",
    "# Inisialisasi model Naive Bayes dengan smoothing parameter yang berbeda dan parameter lainnya\n",
    "model = MultinomialNB(alpha=0.5, fit_prior=False)  # Contoh: menggunakan alpha=0.5 dan fit_prior=False\n",
    "\n",
    "# Pipeline untuk melakukan TfidfVectorizer dan pemodelan dengan Naive Bayes\n",
    "pipeline = Pipeline([\n",
    "    ('tfidf', TfidfVectorizer(max_features=10000, ngram_range=(1, 2))),  # Menambahkan pembuatan n-grams dan membatasi jumlah fitur\n",
    "    ('clf', model)\n",
    "])\n",
    "\n",
    "# Pisahkan data pelatihan menjadi data pelatihan dan data validasi\n",
    "X_train, X_val, y_train, y_val = train_test_split(data_train['stemmed_text'], data_train['label_text'], test_size=0.3, random_state=42)\n",
    "\n",
    "# Lakukan pelatihan dengan data pelatihan dan evaluasi dengan data validasi\n",
    "pipeline.fit(X_train, y_train)\n",
    "\n",
    "# Prediksi label untuk data validasi\n",
    "val_predictions = pipeline.predict(X_val)\n",
    "\n",
    "# Tampilkan confusion matrix\n",
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(y_val, val_predictions))\n",
    "\n",
    "# Hitung dan cetak akurasi\n",
    "accuracy = (val_predictions == y_val).mean()\n",
    "print(f'Validation Accuracy: {accuracy}')\n",
    "\n",
    "# Lakukan pelatihan dengan semua data pelatihan\n",
    "pipeline.fit(data_train['stemmed_text'], data_train['label_text'])\n",
    "\n",
    "# Prediksi label untuk data uji\n",
    "predictions = pipeline.predict(data_test['stemmed_text'])\n",
    "\n",
    "# Tambahkan kolom prediksi ke data uji\n",
    "data_test['label_text'] = predictions\n",
    "\n",
    "# Simpan hasil prediksi ke file baru\n",
    "data_test.to_csv('data_test_with_predictions_higher_accuracy.csv', index=False)\n",
    "\n",
    "print(\"Prediksi untuk data uji telah disimpan dalam 'data_test_with_predictions_higher_accuracy.csv'\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "53ea2ced-d081-4ba1-aaac-91ca91f8de35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[149  21  11]\n",
      " [ 74  37  13]\n",
      " [ 43  12  32]]\n",
      "Validation Accuracy: 0.5561224489795918\n",
      "Prediksi untuk data uji telah disimpan dalam 'data_test_with_predictions_higher_accuracy.csv'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "# Baca data pelatihan dan data uji\n",
    "data_train = pd.read_csv('labeled_data_updated.csv', index_col=False)\n",
    "data_test = pd.read_csv('data_test_fix.csv', index_col=False)\n",
    "\n",
    "# Inisialisasi model Naive Bayes dengan smoothing parameter yang berbeda dan parameter lainnya\n",
    "model = MultinomialNB(alpha=0.5, fit_prior=False)  # Contoh: menggunakan alpha=0.5 dan fit_prior=False\n",
    "\n",
    "# Pipeline untuk melakukan TfidfVectorizer dan pemodelan dengan Naive Bayes\n",
    "pipeline = Pipeline([\n",
    "    ('tfidf', TfidfVectorizer(max_features=10000, ngram_range=(1, 2))),  # Menambahkan pembuatan n-grams dan membatasi jumlah fitur\n",
    "    ('clf', model)\n",
    "])\n",
    "\n",
    "# Pisahkan data pelatihan menjadi data pelatihan dan data validasi\n",
    "X_train, X_val, y_train, y_val = train_test_split(data_train['stemmed_text'], data_train['label_text'], test_size=0.3, random_state=42)\n",
    "\n",
    "# Lakukan pelatihan dengan data pelatihan dan evaluasi dengan data validasi\n",
    "pipeline.fit(X_train, y_train)\n",
    "\n",
    "# Prediksi label untuk data validasi\n",
    "val_predictions = pipeline.predict(X_val)\n",
    "\n",
    "# Tampilkan confusion matrix\n",
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(y_val, val_predictions))\n",
    "\n",
    "# Hitung dan cetak akurasi\n",
    "accuracy = (val_predictions == y_val).mean()\n",
    "print(f'Validation Accuracy: {accuracy}')\n",
    "\n",
    "# Lakukan pelatihan dengan semua data pelatihan\n",
    "pipeline.fit(data_train['stemmed_text'], data_train['label_text'])\n",
    "\n",
    "# Prediksi label untuk data uji\n",
    "predictions = pipeline.predict(data_test['stemmed_text'])\n",
    "\n",
    "# Tambahkan kolom prediksi ke data uji\n",
    "data_test['label_text'] = predictions\n",
    "\n",
    "# Simpan hasil prediksi ke file baru\n",
    "data_test.to_csv('data_test_with_predictions_higher_accuracy.csv', index=False)\n",
    "\n",
    "print(\"Prediksi untuk data uji telah disimpan dalam 'data_test_with_predictions_higher_accuracy.csv'\")\n",
    "\n",
    "# Menggabungkan teks berdasarkan prediksi sentimen\n",
    "netral_text = \" \".join(data_test[data_test['label_text'] == 'netral']['stemmed_text'])\n",
    "positif_text = \" \".join(data_test[data_test['label_text'] == 'positif']['stemmed_text'])\n",
    "negatif_text = \" \".join(data_test[data_test['label_text'] == 'negatif']['stemmed_text'])\n",
    "\n",
    "# Fungsi untuk menghitung bobot TF-IDF dan membuat word cloud\n",
    "def generate_tfidf_wordcloud(texts, title, colormap, filename):\n",
    "    vectorizer = TfidfVectorizer(max_features=10000, ngram_range=(1, 2))\n",
    "    tfidf_matrix = vectorizer.fit_transform(texts)\n",
    "    tfidf_scores = dict(zip(vectorizer.get_feature_names_out(), tfidf_matrix.sum(axis=0).tolist()[0]))\n",
    "    wordcloud = WordCloud(width=800, height=400, background_color='white', colormap=colormap, collocations=False).generate_from_frequencies(tfidf_scores)\n",
    "    plt.imshow(wordcloud, interpolation='bilinear')\n",
    "    plt.axis('off')\n",
    "    plt.title(title)\n",
    "    plt.savefig(f\"{filename}.png\")\n",
    "    plt.clf()\n",
    "\n",
    "# Menyimpan word cloud untuk setiap sentimen sebagai file terpisah\n",
    "generate_tfidf_wordcloud([netral_text], 'Word Cloud untuk Sentimen Netral', 'viridis', 'wordcloud_netral')\n",
    "generate_tfidf_wordcloud([positif_text], 'Word Cloud untuk Sentimen Positif', 'plasma', 'wordcloud_positif')\n",
    "generate_tfidf_wordcloud([negatif_text], 'Word Cloud untuk Sentimen Negatif', 'inferno', 'wordcloud_negatif')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "c15a558b-57c4-482b-bae4-e800240f2270",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[138  27  16]\n",
      " [ 62  39  23]\n",
      " [ 40  13  34]]\n",
      "Validation Accuracy: 0.5382653061224489\n",
      "Prediksi untuk data uji telah disimpan dalam 'data_test_with_predictions_higher_accuracy.csv'\n",
      "Fitur TF-IDF telah disimpan dalam 'tfidf_features.csv'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "# Baca data pelatihan dan data uji\n",
    "data_train = pd.read_csv('labeled_data_updated.csv', index_col=False)\n",
    "data_test = pd.read_csv('data_test_fix.csv', index_col=False)\n",
    "\n",
    "# Inisialisasi model Naive Bayes dengan smoothing parameter yang berbeda dan parameter lainnya\n",
    "model = MultinomialNB(alpha=0.5, fit_prior=False)  # Contoh: menggunakan alpha=0.5 dan fit_prior=False\n",
    "\n",
    "# Pipeline untuk melakukan TfidfVectorizer dan pemodelan dengan Naive Bayes\n",
    "pipeline = Pipeline([\n",
    "    ('tfidf', TfidfVectorizer(max_features=10000, ngram_range=(1, 1))),  # Menggunakan unigrams saja\n",
    "    ('clf', model)\n",
    "])\n",
    "\n",
    "# Pisahkan data pelatihan menjadi data pelatihan dan data validasi\n",
    "X_train, X_val, y_train, y_val = train_test_split(data_train['stemmed_text'], data_train['label_text'], test_size=0.3, random_state=42)\n",
    "\n",
    "# Lakukan pelatihan dengan data pelatihan dan evaluasi dengan data validasi\n",
    "pipeline.fit(X_train, y_train)\n",
    "\n",
    "# Prediksi label untuk data validasi\n",
    "val_predictions = pipeline.predict(X_val)\n",
    "\n",
    "# Tampilkan confusion matrix\n",
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(y_val, val_predictions))\n",
    "\n",
    "# Hitung dan cetak akurasi\n",
    "accuracy = (val_predictions == y_val).mean()\n",
    "print(f'Validation Accuracy: {accuracy}')\n",
    "\n",
    "# Lakukan pelatihan dengan semua data pelatihan\n",
    "pipeline.fit(data_train['stemmed_text'], data_train['label_text'])\n",
    "\n",
    "# Prediksi label untuk data uji\n",
    "predictions = pipeline.predict(data_test['stemmed_text'])\n",
    "\n",
    "# Tambahkan kolom prediksi ke data uji\n",
    "data_test['label_text'] = predictions\n",
    "\n",
    "# Simpan hasil prediksi ke file baru\n",
    "data_test.to_csv('data_test_with_predictions_higher_accuracy.csv', index=False)\n",
    "\n",
    "print(\"Prediksi untuk data uji telah disimpan dalam 'data_test_with_predictions_higher_accuracy.csv'\")\n",
    "\n",
    "# Mengambil kata-kata yang digunakan dalam TfidfVectorizer dan bobot TF-IDF\n",
    "vectorizer = pipeline.named_steps['tfidf']\n",
    "feature_names = vectorizer.get_feature_names_out()\n",
    "tfidf_matrix = vectorizer.transform(data_train['stemmed_text'])\n",
    "tfidf_scores = tfidf_matrix.sum(axis=0).A1  # Sum TF-IDF scores across all documents\n",
    "\n",
    "# Membuat DataFrame dari fitur dan skor TF-IDF\n",
    "tfidf_df = pd.DataFrame({'Term': feature_names, 'TF-IDF': tfidf_scores})\n",
    "\n",
    "# Menyimpan DataFrame ke dalam file CSV\n",
    "tfidf_df.to_csv('tfidf_features.csv', index=False)\n",
    "\n",
    "print(\"Fitur TF-IDF telah disimpan dalam 'tfidf_features.csv'\")\n",
    "\n",
    "# Menggabungkan teks berdasarkan prediksi sentimen\n",
    "netral_text = \" \".join(data_test[data_test['label_text'] == 'netral']['stemmed_text'])\n",
    "positif_text = \" \".join(data_test[data_test['label_text'] == 'positif']['stemmed_text'])\n",
    "negatif_text = \" \".join(data_test[data_test['label_text'] == 'negatif']['stemmed_text'])\n",
    "\n",
    "# Fungsi untuk menghitung bobot TF-IDF dan membuat word cloud\n",
    "def generate_tfidf_wordcloud(texts, title, colormap, filename):\n",
    "    vectorizer = TfidfVectorizer(max_features=10000, ngram_range=(1, 1))  # Menggunakan unigrams saja\n",
    "    tfidf_matrix = vectorizer.fit_transform(texts)\n",
    "    tfidf_scores = dict(zip(vectorizer.get_feature_names_out(), tfidf_matrix.sum(axis=0).tolist()[0]))\n",
    "    wordcloud = WordCloud(width=800, height=400, background_color='white', colormap=colormap, collocations=False).generate_from_frequencies(tfidf_scores)\n",
    "    plt.imshow(wordcloud, interpolation='bilinear')\n",
    "    plt.axis('off')\n",
    "    plt.title(title)\n",
    "    plt.savefig(f\"{filename}.png\")\n",
    "    plt.clf()\n",
    "\n",
    "# Menyimpan word cloud untuk setiap sentimen sebagai file terpisah\n",
    "generate_tfidf_wordcloud([netral_text], 'Word Cloud untuk Sentimen Netral', 'viridis', 'wordcloud_netral')\n",
    "generate_tfidf_wordcloud([positif_text], 'Word Cloud untuk Sentimen Positif', 'plasma', 'wordcloud_positif')\n",
    "generate_tfidf_wordcloud([negatif_text], 'Word Cloud untuk Sentimen Negatif', 'inferno', 'wordcloud_negatif')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "59deabbe-d901-4a35-bb4f-8cbf286549b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah data dengan sentimen:\n",
      "Netral: 96\n",
      "Positif: 87\n",
      "Negatif: 330\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Membaca data dari file CSV\n",
    "data = pd.read_csv(\"data_test_with_predictions_higher_accuracy.csv\")\n",
    "\n",
    "# Menghitung jumlah data dengan masing-masing sentimen\n",
    "sentiment_counts = data['label_text'].value_counts()\n",
    "\n",
    "# Mencetak jumlah data dengan masing-masing sentimen\n",
    "print(\"Jumlah data dengan sentimen:\")\n",
    "print(\"Netral:\", sentiment_counts['netral'])\n",
    "print(\"Positif:\", sentiment_counts['positif'])\n",
    "print(\"Negatif:\", sentiment_counts['negatif'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "a8a3533c-bc44-4aed-afdf-373a87d3a092",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data telah digabungkan dan disimpan ke dalam 'combined_data.csv'\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Membaca data dari file CSV\n",
    "labeled_data = pd.read_csv(\"labeled_data_updated.csv\")\n",
    "predicted_data = pd.read_csv(\"data_test_with_predictions_higher_accuracy.csv\")\n",
    "\n",
    "# Menggabungkan data dengan menambahkan data dari file kedua di bawah data dari file pertama\n",
    "combined_data = pd.concat([labeled_data, predicted_data], ignore_index=True)\n",
    "\n",
    "# Menyimpan data gabungan ke dalam file CSV baru\n",
    "combined_data.to_csv(\"combined_data.csv\", index=False)\n",
    "\n",
    "print(\"Data telah digabungkan dan disimpan ke dalam 'combined_data.csv'\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "07ab4476-8a71-4c8b-bcb7-05b7d643ca95",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rizqi\\AppData\\Local\\Temp\\ipykernel_1452\\1594887086.py:17: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=sentiments, y=counts, palette='viridis')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Membaca data dari file CSV\n",
    "data = pd.read_csv(\"combined_data.csv\")\n",
    "\n",
    "# Menghitung jumlah data dengan masing-masing sentimen\n",
    "sentiment_counts = data['label_text'].value_counts()\n",
    "\n",
    "# Menyiapkan data untuk visualisasi\n",
    "sentiments = ['netral', 'positif', 'negatif']\n",
    "counts = [sentiment_counts['netral'], sentiment_counts['positif'], sentiment_counts['negatif']]\n",
    "\n",
    "# Bar Chart\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.barplot(x=sentiments, y=counts, palette='viridis')\n",
    "plt.title('Distribusi Prediksi Sentimen - Bar Chart')\n",
    "plt.xlabel('Sentimen')\n",
    "plt.ylabel('Jumlah')\n",
    "plt.show()\n",
    "\n",
    "# Pie Chart\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.pie(counts, labels=sentiments, autopct='%1.1f%%', startangle=140, colors=sns.color_palette('viridis'))\n",
    "plt.title('Distribusi Prediksi Sentimen - Pie Chart')\n",
    "plt.show()\n",
    "\n",
    "# Histogram\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.histplot(data['label_text'], bins=3, kde=False, color='purple')\n",
    "plt.title('Distribusi Prediksi Sentimen - Histogram')\n",
    "plt.xlabel('Sentimen')\n",
    "plt.ylabel('Jumlah')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "f275d06e-220a-4efb-8da8-a6b3afe3d8ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Menggabungkan teks berdasarkan prediksi sentimen\n",
    "netral_text = \" \".join(data[data['label_text'] == 'netral']['full_text'])\n",
    "positif_text = \" \".join(data[data['label_text'] == 'positif']['full_text'])\n",
    "negatif_text = \" \".join(data[data['label_text'] == 'negatif']['full_text'])\n",
    "\n",
    "# Membuat fungsi untuk menampilkan word cloud dan menyimpannya sebagai file PNG\n",
    "def generate_wordcloud(text, title, colormap, filename):\n",
    "    wordcloud = WordCloud(width=800, height=400, background_color='white', colormap=colormap, collocations=False).generate(text)\n",
    "    plt.imshow(wordcloud, interpolation='bilinear')\n",
    "    plt.axis('off')\n",
    "    plt.title(title)\n",
    "    plt.savefig(f\"{filename}.png\")\n",
    "    plt.clf()\n",
    "\n",
    "# Menyimpan word cloud untuk setiap sentimen sebagai file terpisah\n",
    "generate_wordcloud(netral_text, 'Word Cloud untuk Sentimen Netral', 'viridis', 'wordcloud_netral')\n",
    "generate_wordcloud(positif_text, 'Word Cloud untuk Sentimen Positif', 'plasma', 'wordcloud_positif')\n",
    "generate_wordcloud(negatif_text, 'Word Cloud untuk Sentimen Negatif', 'inferno', 'wordcloud_negatif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "a5a51e45-bfc3-4f8e-a9de-2b517d77916e",
   "metadata": {},
   "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>created_at</th>\n",
       "      <th>full_text</th>\n",
       "      <th>user_id_str</th>\n",
       "      <th>stemmed_text</th>\n",
       "      <th>label_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sat Feb 10 23:59:55 +0000 2024</td>\n",
       "      <td>tahu partai nya gabener di pilih hadeh</td>\n",
       "      <td>1.489140e+18</td>\n",
       "      <td>tahu partai nya gabener di pilih hadeh</td>\n",
       "      <td>negatif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sat Feb 10 23:59:51 +0000 2024</td>\n",
       "      <td>bukan tahun 2000an awal orang2 Partai Keadilan...</td>\n",
       "      <td>1.358430e+18</td>\n",
       "      <td>bukan tahun 2000an awal orang2 partai adil sej...</td>\n",
       "      <td>negatif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sat Feb 10 23:59:46 +0000 2024</td>\n",
       "      <td>imagine invalidating someones fear and calling...</td>\n",
       "      <td>1.334680e+18</td>\n",
       "      <td>imagine invalidating someones fear and calling...</td>\n",
       "      <td>netral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sat Feb 10 23:59:34 +0000 2024</td>\n",
       "      <td>jangan lupa yah teman-teman 2024 pilih partai ...</td>\n",
       "      <td>1.679190e+18</td>\n",
       "      <td>jangan lupa yah teman 2024 pilih partai ummat ...</td>\n",
       "      <td>positif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sat Feb 10 23:59:28 +0000 2024</td>\n",
       "      <td>h3 pemilihan umum heran seakan2 jokowi dosanya...</td>\n",
       "      <td>1.710507e+08</td>\n",
       "      <td>h3 pilih umum heran seakan2 jokowi dosa paling...</td>\n",
       "      <td>positif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1812</th>\n",
       "      <td>Fri Feb 09 23:11:32 +0000 2024</td>\n",
       "      <td>bukan hebat culas licik angkat nol smpai berku...</td>\n",
       "      <td>1.538716e+18</td>\n",
       "      <td>bukan hebat culas licik angkat nol smpai kuasa...</td>\n",
       "      <td>negatif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1813</th>\n",
       "      <td>Fri Feb 09 23:11:21 +0000 2024</td>\n",
       "      <td>kalau melalui proses pemilihan umum langsung s...</td>\n",
       "      <td>1.466980e+18</td>\n",
       "      <td>kalau lalu proses pilih umum langsung saya per...</td>\n",
       "      <td>negatif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1814</th>\n",
       "      <td>Fri Feb 09 23:10:00 +0000 2024</td>\n",
       "      <td>kenalan yuk calon legislatif dewan perwakilan ...</td>\n",
       "      <td>2.848309e+08</td>\n",
       "      <td>kenal yuk calon legislatif dewan wakil rakyat ...</td>\n",
       "      <td>positif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1815</th>\n",
       "      <td>Fri Feb 09 23:09:17 +0000 2024</td>\n",
       "      <td>aku tempelin sticker besar sticker partai</td>\n",
       "      <td>1.742568e+18</td>\n",
       "      <td>aku tempelin sticker besar sticker partai</td>\n",
       "      <td>netral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1816</th>\n",
       "      <td>Fri Feb 09 23:08:42 +0000 2024</td>\n",
       "      <td>hebat jokowi bener2 serius buat bangsa beliyau...</td>\n",
       "      <td>1.492511e+18</td>\n",
       "      <td>hebat jokowi bener2 serius buat bangsa beliyau...</td>\n",
       "      <td>negatif</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1817 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          created_at  \\\n",
       "0     Sat Feb 10 23:59:55 +0000 2024   \n",
       "1     Sat Feb 10 23:59:51 +0000 2024   \n",
       "2     Sat Feb 10 23:59:46 +0000 2024   \n",
       "3     Sat Feb 10 23:59:34 +0000 2024   \n",
       "4     Sat Feb 10 23:59:28 +0000 2024   \n",
       "...                              ...   \n",
       "1812  Fri Feb 09 23:11:32 +0000 2024   \n",
       "1813  Fri Feb 09 23:11:21 +0000 2024   \n",
       "1814  Fri Feb 09 23:10:00 +0000 2024   \n",
       "1815  Fri Feb 09 23:09:17 +0000 2024   \n",
       "1816  Fri Feb 09 23:08:42 +0000 2024   \n",
       "\n",
       "                                              full_text   user_id_str  \\\n",
       "0                tahu partai nya gabener di pilih hadeh  1.489140e+18   \n",
       "1     bukan tahun 2000an awal orang2 Partai Keadilan...  1.358430e+18   \n",
       "2     imagine invalidating someones fear and calling...  1.334680e+18   \n",
       "3     jangan lupa yah teman-teman 2024 pilih partai ...  1.679190e+18   \n",
       "4     h3 pemilihan umum heran seakan2 jokowi dosanya...  1.710507e+08   \n",
       "...                                                 ...           ...   \n",
       "1812  bukan hebat culas licik angkat nol smpai berku...  1.538716e+18   \n",
       "1813  kalau melalui proses pemilihan umum langsung s...  1.466980e+18   \n",
       "1814  kenalan yuk calon legislatif dewan perwakilan ...  2.848309e+08   \n",
       "1815          aku tempelin sticker besar sticker partai  1.742568e+18   \n",
       "1816  hebat jokowi bener2 serius buat bangsa beliyau...  1.492511e+18   \n",
       "\n",
       "                                           stemmed_text label_text  \n",
       "0                tahu partai nya gabener di pilih hadeh    negatif  \n",
       "1     bukan tahun 2000an awal orang2 partai adil sej...    negatif  \n",
       "2     imagine invalidating someones fear and calling...     netral  \n",
       "3     jangan lupa yah teman 2024 pilih partai ummat ...    positif  \n",
       "4     h3 pilih umum heran seakan2 jokowi dosa paling...    positif  \n",
       "...                                                 ...        ...  \n",
       "1812  bukan hebat culas licik angkat nol smpai kuasa...    negatif  \n",
       "1813  kalau lalu proses pilih umum langsung saya per...    negatif  \n",
       "1814  kenal yuk calon legislatif dewan wakil rakyat ...    positif  \n",
       "1815          aku tempelin sticker besar sticker partai     netral  \n",
       "1816  hebat jokowi bener2 serius buat bangsa beliyau...    negatif  \n",
       "\n",
       "[1817 rows x 5 columns]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "b04835df-33ff-4714-bcd8-94924656c974",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah Buzzer Positif: 8\n",
      "Jumlah Buzzer Negatif: 9\n",
      "Jumlah Non Buzzer: 1800\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Membaca data dari file CSV\n",
    "df = pd.read_csv(\"combined_data.csv\")\n",
    "\n",
    "# Membuat variabel untuk menyimpan hasil labeling\n",
    "buzzer_types = []\n",
    "\n",
    "# Iterasi melalui setiap baris data\n",
    "for index, row in df.iterrows():\n",
    "    # Menghitung jumlah kemunculan 'user_id_str' dan 'label_text' yang sama\n",
    "    count = df[(df['user_id_str'] == row['user_id_str']) & (df['label_text'] == row['label_text'])].shape[0]\n",
    "    \n",
    "    # Menentukan buzzer type\n",
    "    if count >= 4:\n",
    "        if row['label_text'] == 'negatif':\n",
    "            buzzer_type = 'buzzer negatif'\n",
    "        elif row['label_text'] == 'positif':\n",
    "            buzzer_type = 'buzzer positif'\n",
    "    else:\n",
    "        buzzer_type = 'non-buzzer'\n",
    "    \n",
    "    # Menambahkan buzzer type ke dalam list\n",
    "    buzzer_types.append(buzzer_type)\n",
    "\n",
    "# Menambahkan kolom 'buzzer_type' ke DataFrame\n",
    "df['buzzer_type'] = buzzer_types\n",
    "\n",
    "# Menyimpan DataFrame ke file CSV\n",
    "df.to_csv('combined_data_with_buzzer_type.csv', index=False)\n",
    "\n",
    "# Membaca data dari file CSV yang sudah diberi labael\n",
    "labeled_df = pd.read_csv(\"combined_data_with_buzzer_type.csv\")\n",
    "\n",
    "# Menghitung jumlah 'buzzer positif', 'buzzer negatif', dan 'non-buzzer'\n",
    "buzzer_count = labeled_df['buzzer_type'].value_counts()\n",
    "\n",
    "# Menampilkan jumlah buzzer positif, negatif, dan non-buzzer\n",
    "print(\"Jumlah Buzzer Positif:\", buzzer_count.get('buzzer positif', 0))\n",
    "print(\"Jumlah Buzzer Negatif:\", buzzer_count.get('buzzer negatif', 0))\n",
    "print(\"Jumlah Non Buzzer:\", buzzer_count.get('non-buzzer', 0))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "89bf26f6-6197-4b4d-98e7-4ead87a0ef50",
   "metadata": {},
   "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>created_at</th>\n",
       "      <th>full_text</th>\n",
       "      <th>user_id_str</th>\n",
       "      <th>stemmed_text</th>\n",
       "      <th>label_text</th>\n",
       "      <th>buzzer_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sat Feb 10 23:59:55 +0000 2024</td>\n",
       "      <td>tahu partai nya gabener di pilih hadeh</td>\n",
       "      <td>1.489140e+18</td>\n",
       "      <td>tahu partai nya gabener di pilih hadeh</td>\n",
       "      <td>negatif</td>\n",
       "      <td>non-buzzer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sat Feb 10 23:59:51 +0000 2024</td>\n",
       "      <td>bukan tahun 2000an awal orang2 Partai Keadilan...</td>\n",
       "      <td>1.358430e+18</td>\n",
       "      <td>bukan tahun 2000an awal orang2 partai adil sej...</td>\n",
       "      <td>negatif</td>\n",
       "      <td>non-buzzer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sat Feb 10 23:59:46 +0000 2024</td>\n",
       "      <td>imagine invalidating someones fear and calling...</td>\n",
       "      <td>1.334680e+18</td>\n",
       "      <td>imagine invalidating someones fear and calling...</td>\n",
       "      <td>netral</td>\n",
       "      <td>non-buzzer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sat Feb 10 23:59:34 +0000 2024</td>\n",
       "      <td>jangan lupa yah teman-teman 2024 pilih partai ...</td>\n",
       "      <td>1.679190e+18</td>\n",
       "      <td>jangan lupa yah teman 2024 pilih partai ummat ...</td>\n",
       "      <td>positif</td>\n",
       "      <td>non-buzzer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sat Feb 10 23:59:28 +0000 2024</td>\n",
       "      <td>h3 pemilihan umum heran seakan2 jokowi dosanya...</td>\n",
       "      <td>1.710507e+08</td>\n",
       "      <td>h3 pilih umum heran seakan2 jokowi dosa paling...</td>\n",
       "      <td>positif</td>\n",
       "      <td>non-buzzer</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>1812</th>\n",
       "      <td>Fri Feb 09 23:11:32 +0000 2024</td>\n",
       "      <td>bukan hebat culas licik angkat nol smpai berku...</td>\n",
       "      <td>1.538716e+18</td>\n",
       "      <td>bukan hebat culas licik angkat nol smpai kuasa...</td>\n",
       "      <td>negatif</td>\n",
       "      <td>non-buzzer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1813</th>\n",
       "      <td>Fri Feb 09 23:11:21 +0000 2024</td>\n",
       "      <td>kalau melalui proses pemilihan umum langsung s...</td>\n",
       "      <td>1.466980e+18</td>\n",
       "      <td>kalau lalu proses pilih umum langsung saya per...</td>\n",
       "      <td>negatif</td>\n",
       "      <td>non-buzzer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1814</th>\n",
       "      <td>Fri Feb 09 23:10:00 +0000 2024</td>\n",
       "      <td>kenalan yuk calon legislatif dewan perwakilan ...</td>\n",
       "      <td>2.848309e+08</td>\n",
       "      <td>kenal yuk calon legislatif dewan wakil rakyat ...</td>\n",
       "      <td>positif</td>\n",
       "      <td>buzzer positif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1815</th>\n",
       "      <td>Fri Feb 09 23:09:17 +0000 2024</td>\n",
       "      <td>aku tempelin sticker besar sticker partai</td>\n",
       "      <td>1.742568e+18</td>\n",
       "      <td>aku tempelin sticker besar sticker partai</td>\n",
       "      <td>netral</td>\n",
       "      <td>non-buzzer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1816</th>\n",
       "      <td>Fri Feb 09 23:08:42 +0000 2024</td>\n",
       "      <td>hebat jokowi bener2 serius buat bangsa beliyau...</td>\n",
       "      <td>1.492511e+18</td>\n",
       "      <td>hebat jokowi bener2 serius buat bangsa beliyau...</td>\n",
       "      <td>negatif</td>\n",
       "      <td>non-buzzer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1817 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          created_at  \\\n",
       "0     Sat Feb 10 23:59:55 +0000 2024   \n",
       "1     Sat Feb 10 23:59:51 +0000 2024   \n",
       "2     Sat Feb 10 23:59:46 +0000 2024   \n",
       "3     Sat Feb 10 23:59:34 +0000 2024   \n",
       "4     Sat Feb 10 23:59:28 +0000 2024   \n",
       "...                              ...   \n",
       "1812  Fri Feb 09 23:11:32 +0000 2024   \n",
       "1813  Fri Feb 09 23:11:21 +0000 2024   \n",
       "1814  Fri Feb 09 23:10:00 +0000 2024   \n",
       "1815  Fri Feb 09 23:09:17 +0000 2024   \n",
       "1816  Fri Feb 09 23:08:42 +0000 2024   \n",
       "\n",
       "                                              full_text   user_id_str  \\\n",
       "0                tahu partai nya gabener di pilih hadeh  1.489140e+18   \n",
       "1     bukan tahun 2000an awal orang2 Partai Keadilan...  1.358430e+18   \n",
       "2     imagine invalidating someones fear and calling...  1.334680e+18   \n",
       "3     jangan lupa yah teman-teman 2024 pilih partai ...  1.679190e+18   \n",
       "4     h3 pemilihan umum heran seakan2 jokowi dosanya...  1.710507e+08   \n",
       "...                                                 ...           ...   \n",
       "1812  bukan hebat culas licik angkat nol smpai berku...  1.538716e+18   \n",
       "1813  kalau melalui proses pemilihan umum langsung s...  1.466980e+18   \n",
       "1814  kenalan yuk calon legislatif dewan perwakilan ...  2.848309e+08   \n",
       "1815          aku tempelin sticker besar sticker partai  1.742568e+18   \n",
       "1816  hebat jokowi bener2 serius buat bangsa beliyau...  1.492511e+18   \n",
       "\n",
       "                                           stemmed_text label_text  \\\n",
       "0                tahu partai nya gabener di pilih hadeh    negatif   \n",
       "1     bukan tahun 2000an awal orang2 partai adil sej...    negatif   \n",
       "2     imagine invalidating someones fear and calling...     netral   \n",
       "3     jangan lupa yah teman 2024 pilih partai ummat ...    positif   \n",
       "4     h3 pilih umum heran seakan2 jokowi dosa paling...    positif   \n",
       "...                                                 ...        ...   \n",
       "1812  bukan hebat culas licik angkat nol smpai kuasa...    negatif   \n",
       "1813  kalau lalu proses pilih umum langsung saya per...    negatif   \n",
       "1814  kenal yuk calon legislatif dewan wakil rakyat ...    positif   \n",
       "1815          aku tempelin sticker besar sticker partai     netral   \n",
       "1816  hebat jokowi bener2 serius buat bangsa beliyau...    negatif   \n",
       "\n",
       "         buzzer_type  \n",
       "0         non-buzzer  \n",
       "1         non-buzzer  \n",
       "2         non-buzzer  \n",
       "3         non-buzzer  \n",
       "4         non-buzzer  \n",
       "...              ...  \n",
       "1812      non-buzzer  \n",
       "1813      non-buzzer  \n",
       "1814  buzzer positif  \n",
       "1815      non-buzzer  \n",
       "1816      non-buzzer  \n",
       "\n",
       "[1817 rows x 6 columns]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "fd0caf23-2b67-449b-bef9-33b5d218eb37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Membaca data dari file CSV yang sudah diberi label\n",
    "labeled_df = pd.read_csv(\"combined_data_with_buzzer_type.csv\")\n",
    "\n",
    "# Menghitung jumlah 'buzzer positif', 'buzzer negatif', dan 'non-buzzer'\n",
    "buzzer_count = labeled_df['buzzer_type'].value_counts()\n",
    "\n",
    "# Membuat data untuk visualisasi\n",
    "labels = ['Buzzer Positif', 'Buzzer Negatif', 'Non-Buzzer']\n",
    "counts = [\n",
    "    buzzer_count.get('buzzer positif', 0),\n",
    "    buzzer_count.get('buzzer negatif', 0),\n",
    "    buzzer_count.get('non-buzzer', 0)\n",
    "]\n",
    "\n",
    "# Warna yang lebih lembut\n",
    "colors = ['#a3e4d7', '#f5b7b1', '#d6eaf8']\n",
    "\n",
    "# Membuat figure dengan 3 subplots (dalam satu baris)\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n",
    "\n",
    "# Diagram Batang\n",
    "axes[0].bar(labels, counts, color=colors)\n",
    "axes[0].set_xlabel('Jenis Buzzer')\n",
    "axes[0].set_ylabel('Jumlah')\n",
    "axes[0].set_title('Jumlah Buzzer Positif, Negatif, dan Non-Buzzer')\n",
    "axes[0].tick_params(axis='x', rotation=45)\n",
    "\n",
    "# Diagram Pie\n",
    "axes[1].pie(counts, labels=labels, autopct='%1.1f%%', colors=colors)\n",
    "axes[1].set_title('Proporsi Buzzer Positif, Negatif, dan Non-Buzzer')\n",
    "\n",
    "# Diagram Garis\n",
    "axes[2].plot(labels, counts, marker='o', linestyle='-', color='#85c1e9')\n",
    "axes[2].set_xlabel('Jenis Buzzer')\n",
    "axes[2].set_ylabel('Jumlah')\n",
    "axes[2].set_title('Jumlah Buzzer Positif, Negatif, dan Non-Buzzer')\n",
    "axes[2].grid(True)\n",
    "\n",
    "# Menampilkan plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "378d4188-2b07-4703-a882-df094295fe3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Membaca data dari file CSV yang sudah diberi label\n",
    "labeled_df = pd.read_csv(\"combined_data_with_buzzer_type.csv\")\n",
    "\n",
    "# Menghitung jumlah tiap label_text\n",
    "label_count = labeled_df['label_text'].value_counts()\n",
    "\n",
    "# Membuat data untuk visualisasi\n",
    "labels = label_count.index.tolist()\n",
    "counts = label_count.values.tolist()\n",
    "\n",
    "# Warna yang lebih lembut\n",
    "colors = ['#a3e4d7', '#f5b7b1', '#d6eaf8', '#f7d6e0']\n",
    "\n",
    "# Membuat figure dengan 3 subplots (dalam satu baris)\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n",
    "\n",
    "# Diagram Batang\n",
    "axes[0].bar(labels, counts, color=colors)\n",
    "axes[0].set_xlabel('Label Text')\n",
    "axes[0].set_ylabel('Jumlah')\n",
    "axes[0].set_title('Jumlah Tiap Label Text')\n",
    "axes[0].tick_params(axis='x', rotation=45)\n",
    "\n",
    "# Diagram Pie\n",
    "axes[1].pie(counts, labels=labels, autopct='%1.1f%%', colors=colors)\n",
    "axes[1].set_title('Proporsi Tiap Label Text')\n",
    "\n",
    "# Diagram Garis\n",
    "axes[2].plot(labels, counts, marker='o', linestyle='-', color='#85c1e9')\n",
    "axes[2].set_xlabel('Label Text')\n",
    "axes[2].set_ylabel('Jumlah')\n",
    "axes[2].set_title('Jumlah Tiap Label Text')\n",
    "axes[2].grid(True)\n",
    "\n",
    "# Menampilkan plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e23f8c01-3e68-4c7c-998a-b7b9929d2bf6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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",
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