hoax-train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_excel(\"dataset/train/Data Latih BDC.xlsx\")\n",
    "df = df.drop(['ID','tanggal','nama file gambar'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>judul</th>\n",
       "      <th>narasi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Pemakaian Masker Menyebabkan Penyakit Legionna...</td>\n",
       "      <td>A caller to a radio talk show recently shared ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Instruksi Gubernur Jateng tentang penilangan  ...</td>\n",
       "      <td>Yth.Seluruh Anggota Grup Sesuai Instruksi Gube...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Foto Jim Rohn: Jokowi adalah presiden terbaik ...</td>\n",
       "      <td>Jokowi adalah presiden terbaik dlm sejarah ban...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>ini bukan politik, tapi kenyataan Pak Jokowi b...</td>\n",
       "      <td>Maaf Mas2 dan Mbak2, ini bukan politik, tapi k...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Foto Kadrun kalo lihat foto ini panas dingin</td>\n",
       "      <td>Kadrun kalo lihat foto ini panas dingin . .</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4226</th>\n",
       "      <td>1</td>\n",
       "      <td>: KPK Dilarang Membawa Brimob Bersenjata Masuk...</td>\n",
       "      <td>KPK Dilarang Membawa Brimob Bersenjata Masuk G...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4227</th>\n",
       "      <td>1</td>\n",
       "      <td>Foto pejabat keuangan dibawah palu arit</td>\n",
       "      <td>Jangan mau dialihkan kepada pakaian adat… FOKU...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4228</th>\n",
       "      <td>1</td>\n",
       "      <td>Gambar Denny Siregar Musuh Warga Tasikmalaya d...</td>\n",
       "      <td>Akhir nya bisa terkenal bang denny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4229</th>\n",
       "      <td>1</td>\n",
       "      <td>Kaesang: Bapak Saya dengan Kesederhaan Bisa Ni...</td>\n",
       "      <td>Bapak Saya dengan Kesederhaan Bisa Nipu Rakyat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4230</th>\n",
       "      <td>1</td>\n",
       "      <td>Laser dari termometer gun akan merusak struktu...</td>\n",
       "      <td>IN: Saya nolak. Kalau Anda mau periksa, bukan ...</td>\n",
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      "text/plain": [
       "      label                                              judul  \\\n",
       "0         1  Pemakaian Masker Menyebabkan Penyakit Legionna...   \n",
       "1         1  Instruksi Gubernur Jateng tentang penilangan  ...   \n",
       "2         1  Foto Jim Rohn: Jokowi adalah presiden terbaik ...   \n",
       "3         1  ini bukan politik, tapi kenyataan Pak Jokowi b...   \n",
       "4         1       Foto Kadrun kalo lihat foto ini panas dingin   \n",
       "...     ...                                                ...   \n",
       "4226      1  : KPK Dilarang Membawa Brimob Bersenjata Masuk...   \n",
       "4227      1            Foto pejabat keuangan dibawah palu arit   \n",
       "4228      1  Gambar Denny Siregar Musuh Warga Tasikmalaya d...   \n",
       "4229      1  Kaesang: Bapak Saya dengan Kesederhaan Bisa Ni...   \n",
       "4230      1  Laser dari termometer gun akan merusak struktu...   \n",
       "\n",
       "                                                 narasi  \n",
       "0     A caller to a radio talk show recently shared ...  \n",
       "1     Yth.Seluruh Anggota Grup Sesuai Instruksi Gube...  \n",
       "2     Jokowi adalah presiden terbaik dlm sejarah ban...  \n",
       "3     Maaf Mas2 dan Mbak2, ini bukan politik, tapi k...  \n",
       "4           Kadrun kalo lihat foto ini panas dingin . .  \n",
       "...                                                 ...  \n",
       "4226  KPK Dilarang Membawa Brimob Bersenjata Masuk G...  \n",
       "4227  Jangan mau dialihkan kepada pakaian adat… FOKU...  \n",
       "4228                 Akhir nya bisa terkenal bang denny  \n",
       "4229  Bapak Saya dengan Kesederhaan Bisa Nipu Rakyat...  \n",
       "4230  IN: Saya nolak. Kalau Anda mau periksa, bukan ...  \n",
       "\n",
       "[4231 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4231, 2)\n",
      "(4231,)\n"
     ]
    }
   ],
   "source": [
    "#input variabel [judul, narasi]\n",
    "X = df.iloc[:,1:3].values\n",
    "\n",
    "#Target Variabel [label]\n",
    "y = df.iloc[:,0].values\n",
    "\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "from gensim.parsing.preprocessing import remove_stopwords\n",
    "import re\n",
    "from Sastrawi.StopWordRemover.StopWordRemoverFactory import StopWordRemoverFactory\n",
    "\n",
    "#remove stopword\n",
    "factory = StopWordRemoverFactory()\n",
    "stopwords_indo = factory.create_stop_word_remover()\n",
    "rows, cols = X.shape\n",
    "for i in range(0, cols):\n",
    "  for j in range(0, rows):\n",
    "    review = re.sub('[^a-zA-Z]',' ',X[j,i])\n",
    "    review = review.lower()\n",
    "    review = review.translate(str.maketrans('','',string.punctuation)).lower()\n",
    "    review = stopwords_indo.remove(review)\n",
    "\n",
    "    review = remove_stopwords(review)\n",
    "    X[j,i] = review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "# number of times a word appears in the document but is counterbalanced by the number of documents in which it is present.\n",
    "\n",
    "transform = TfidfVectorizer(max_features=5000, ngram_range=(1,3))\n",
    "tf_judul = transform.fit_transform(X[:,0]).todense()\n",
    "tf_narasi = transform.fit_transform(X[:,1]).todense()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4231, 10000)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "X_mat = np.hstack((tf_judul, tf_narasi))\n",
    "X_mat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(847, 10000)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_mat, y, test_size=0.2, random_state=0)\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\Porto Data\\hoax-detection\\env\\lib\\site-packages\\sklearn\\utils\\validation.py:727: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n",
      "  warnings.warn(\n",
      "e:\\Porto Data\\hoax-detection\\env\\lib\\site-packages\\sklearn\\utils\\validation.py:727: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import pickle\n",
    "model = DecisionTreeClassifier(criterion='entropy')\n",
    "model.fit(X_train, y_train)\n",
    "y_predict = model.predict(X_test)\n",
    "\n",
    "# filename = 'model/model.sav'\n",
    "# pickle.dump(model, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8720514653323803\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "#calculate performance metrics\n",
    "F1 = f1_score(y_pred=y_predict, y_true=y_test)\n",
    "print(F1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\Porto Data\\hoax-detection\\env\\lib\\site-packages\\sklearn\\utils\\validation.py:727: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "test_predict = model.predict(X_test[10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 0. 0. 0.]]\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print(test_predict[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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