Gender Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Memprediksi jenis kelamin dari nama bahasa Indonesia menggunakan Machine Learning\n",
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Loading dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd                 # pandas is a dataframe library\n",
    "df = pd.read_csv(\"./data/data-pemilih-kpu.csv\", encoding = 'utf-8-sig')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12950, 2)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dimensi dataset terdiri dari 13137 baris dan 2 kolom\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nama</th>\n",
       "      <th>jenis_kelamin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ERWIN TJAHJONO</td>\n",
       "      <td>Laki-Laki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DAVIANDRIE ANDIKA BAHROENY</td>\n",
       "      <td>Laki-Laki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ELAN KURNIAWAN</td>\n",
       "      <td>Laki-Laki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AYU DWI CAHYANING MUKTI</td>\n",
       "      <td>Perempuan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WAHYOEDIN</td>\n",
       "      <td>Laki-Laki</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         nama jenis_kelamin\n",
       "0              ERWIN TJAHJONO     Laki-Laki\n",
       "1  DAVIANDRIE ANDIKA BAHROENY     Laki-Laki\n",
       "2              ELAN KURNIAWAN     Laki-Laki\n",
       "3     AYU DWI CAHYANING MUKTI     Perempuan\n",
       "4                   WAHYOEDIN     Laki-Laki"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#melihat 5 baris pertama dataset\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nama</th>\n",
       "      <th>jenis_kelamin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13132</th>\n",
       "      <td>HERMANSYAH</td>\n",
       "      <td>Laki-Laki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13133</th>\n",
       "      <td>SITA.HJ</td>\n",
       "      <td>Perempuan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13134</th>\n",
       "      <td>MASNI TAMBUNAN</td>\n",
       "      <td>Perempuan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13135</th>\n",
       "      <td>MARJANEDI</td>\n",
       "      <td>Laki-Laki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13136</th>\n",
       "      <td>NGALIMAN</td>\n",
       "      <td>Laki-Laki</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 nama jenis_kelamin\n",
       "13132      HERMANSYAH     Laki-Laki\n",
       "13133         SITA.HJ     Perempuan\n",
       "13134  MASNI TAMBUNAN     Perempuan\n",
       "13135       MARJANEDI     Laki-Laki\n",
       "13136        NGALIMAN     Laki-Laki"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#melihat 5 baris terakhir dataset\n",
    "df.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Cleansing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mengecek apakah ada data yang berisi null\n",
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "187"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mengecek jumlah baris data yang berisi null\n",
    "len(df[pd.isnull(df).any(axis=1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# menghapus baris null dan recheck kembali\n",
    "df = df.dropna(how='all')\n",
    "len(df[pd.isnull(df).any(axis=1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12950, 2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mengecek dimensi dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# mengubah isi kolom jenis kelamin dari text menjadi integer (Laki-laki = 1; Perempuan= 0)\n",
    "jk_map = {\"Laki-Laki\" : 1, \"Perempuan\" : 0}\n",
    "df[\"jenis_kelamin\"] = df[\"jenis_kelamin\"].map(jk_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nama</th>\n",
       "      <th>jenis_kelamin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ERWIN TJAHJONO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DAVIANDRIE ANDIKA BAHROENY</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ELAN KURNIAWAN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AYU DWI CAHYANING MUKTI</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WAHYOEDIN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         nama  jenis_kelamin\n",
       "0              ERWIN TJAHJONO              1\n",
       "1  DAVIANDRIE ANDIKA BAHROENY              1\n",
       "2              ELAN KURNIAWAN              1\n",
       "3     AYU DWI CAHYANING MUKTI              0\n",
       "4                   WAHYOEDIN              1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cek kembali data apakah telah berubah\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah Pria:  6162 (47.58%)\n",
      "Jumlah Wanita: 6788 (52.42%)\n"
     ]
    }
   ],
   "source": [
    "# Mengecek distribusi jenis kelamin pada dataset\n",
    "\n",
    "num_obs = len(df)\n",
    "num_true = len(df.loc[df['jenis_kelamin'] == 1])\n",
    "num_false = len(df.loc[df['jenis_kelamin'] == 0])\n",
    "print(\"Jumlah Pria:  {0} ({1:2.2f}%)\".format(num_true, (num_true/num_obs) * 100))\n",
    "print(\"Jumlah Wanita: {0} ({1:2.2f}%)\".format(num_false, (num_false/num_obs) * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Split Dataset\n",
    "Dataset yang adalah akan dipecah menjadi dua bagian, 70% data akan digunakan sebagai data training untuk melatih mesin. Kemudian 30% sisanya akan digunakan sebagai data testing untuk mengevaluasi akurasi predisksi machine learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "feature_col_names = [\"nama\"]\n",
    "predicted_class_names = [\"jenis_kelamin\"]\n",
    "\n",
    "X = df[feature_col_names].values     \n",
    "y = df[predicted_class_names].values\n",
    "split_test_size = 0.30\n",
    "\n",
    "text_train, text_test, y_train, y_test = train_test_split(X, y, test_size=split_test_size, stratify=y, random_state=42) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Dataset telah dipecah menjadi 2 bagian, mari kita cek distribusi nya."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Asli Pria       : 6162 (47.58%)\n",
      "Dataset Asli Wanita     : 6788 (52.42%)\n",
      "\n",
      "Dataset Training Pria   : 4313 (47.58%)\n",
      "Dataset Training Wanita : 4752 (52.42%)\n",
      "\n",
      "Dataset Test Pria       : 1849 (47.59%)\n",
      "Dataset Test Wanita     : 2036 (52.41%)\n"
     ]
    }
   ],
   "source": [
    "print(\"Dataset Asli Pria       : {0} ({1:0.2f}%)\".format(len(df.loc[df['jenis_kelamin'] == 1]), (len(df.loc[df['jenis_kelamin'] == 1])/len(df.index)) * 100.0))\n",
    "print(\"Dataset Asli Wanita     : {0} ({1:0.2f}%)\".format(len(df.loc[df['jenis_kelamin'] == 0]), (len(df.loc[df['jenis_kelamin'] == 0])/len(df.index)) * 100.0))\n",
    "print(\"\")\n",
    "print(\"Dataset Training Pria   : {0} ({1:0.2f}%)\".format(len(y_train[y_train[:] == 1]), (len(y_train[y_train[:] == 1])/len(y_train) * 100.0)))\n",
    "print(\"Dataset Training Wanita : {0} ({1:0.2f}%)\".format(len(y_train[y_train[:] == 0]), (len(y_train[y_train[:] == 0])/len(y_train) * 100.0)))\n",
    "print(\"\")\n",
    "print(\"Dataset Test Pria       : {0} ({1:0.2f}%)\".format(len(y_test[y_test[:] == 1]), (len(y_test[y_test[:] == 1])/len(y_test) * 100.0)))\n",
    "print(\"Dataset Test Wanita     : {0} ({1:0.2f}%)\".format(len(y_test[y_test[:] == 0]), (len(y_test[y_test[:] == 0])/len(y_test) * 100.0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Terlihat hasilnya, dataset yang telah dipecah dua tetap dapat mempertahankan persentase distribusi jenis kelamin seperti pada dataset asli."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Features Extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Proses features extraction, berpengaruh terhadap hasil akurasi yang didapatkan nantinya. Disini saya kan menggunakan metode simple yaitu [CountVectorizer](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html) yang akan membuat matrix frekwensi kemunculan dari suatu karakter di tiap nama yang diberikan, dengan opsi analisa ngram_range 2 - 6 hanya di dalam satu kata saja.\n",
    "Misal Muhammad Irfani Sahnur, menghasilkan n-gram :\n",
    "* mu\n",
    "* ham\n",
    "* mad\n",
    "* nur\n",
    "* dst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "vectorizer = CountVectorizer(analyzer = 'char_wb', ngram_range=(2,6))\n",
    "vectorizer.fit(text_train.ravel())\n",
    "\n",
    "X_train = vectorizer.transform(text_train.ravel())\n",
    "X_test = vectorizer.transform(text_test.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Logistic Regression\n",
    "Percobaan pertama menggunakan algoritma Logistic Regression. Data hasil feature extraction akan diinput sebagai data training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "clf = LogisticRegression()\n",
    "clf.fit(X_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Akurasi prediksi menggunakan data test yang didapat cukup lumayan berada pada tingkat **93.6%**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.996690568119\n",
      "0.936422136422\n"
     ]
    }
   ],
   "source": [
    "# dataset training\n",
    "print(clf.score(X_train, y_train))\n",
    "\n",
    "# dataset test\n",
    "print(clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Detail akurasi metriks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9364\n",
      "[[1727  122]\n",
      " [ 125 1911]]\n",
      "\n",
      "Classification Report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.93      0.93      0.93      1849\n",
      "          0       0.94      0.94      0.94      2036\n",
      "\n",
      "avg / total       0.94      0.94      0.94      3885\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "clf_predict = clf.predict(X_test)\n",
    "\n",
    "# training metrics\n",
    "print(\"Accuracy: {0:.4f}\".format(metrics.accuracy_score(y_test, clf_predict)))\n",
    "print(metrics.confusion_matrix(y_test, clf_predict, labels=[1, 0]) )\n",
    "print(\"\")\n",
    "print(\"Classification Report\")\n",
    "print(metrics.classification_report(y_test, clf_predict, labels=[1,0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing prediksi jenis kelamin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perempuan\n"
     ]
    }
   ],
   "source": [
    "jk_label = {1:\"Laki-Laki\", 0:\"Perempuan\"}\n",
    "\n",
    "test_predict = vectorizer.transform([\"niky felina\"])\n",
    "res = clf.predict(test_predict)\n",
    "\n",
    "print(jk_label[int(res)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Menggunakan Pipeline\n",
    "\n",
    "Scikit memiliki fitur untuk memudahkan proses diatas dengan mengguanakan [Pipeline](scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html). Penulisan kode jadi lebih simple dan rapih, berikut konversi kode diatas jika menggunakan Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93642213642213645"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "clf_lg = Pipeline([('vect', CountVectorizer(analyzer = 'char_wb', ngram_range=(2,6))),\n",
    "                     ('clf', LogisticRegression()),\n",
    "])\n",
    "_ = clf_lg.fit(text_train.ravel(), y_train.ravel())\n",
    "predicted = clf_lg.predict(text_test.ravel())\n",
    "np.mean(predicted == y_test.ravel()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tingkat akurasi persis sama, dan lebih mudah dalam penulisan kode nya.\n",
    "Mari kita lakukan kembali testing prediksi jenis kelamin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Laki-Laki\n"
     ]
    }
   ],
   "source": [
    "result = clf_lg.predict([\"muhammad irfani sahnur\"])\n",
    "print(jk_label[result[0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes\n",
    "\n",
    "Algoritma berikutnya yang akan digunakan adalah [Naive Bayes](http://scikit-learn.org/stable/modules/naive_bayes.html). Lansung saja kita coba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93307593307593306"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "clf_nb = Pipeline([('vect', CountVectorizer(analyzer = 'char_wb', ngram_range=(2,6))),\n",
    "                     ('clf', MultinomialNB()),\n",
    "])\n",
    "\n",
    "clf_nb = clf_nb.fit(text_train.ravel(), y_train.ravel())\n",
    "predicted = clf_nb.predict(text_test.ravel())\n",
    "np.mean(predicted == y_test.ravel())  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Dengan algoritman Naive Bayes, tingkat akurasi yang didapatkan sedikit saja lebih rendah dari Logistic Regression yaitu **93.3%**. Mari kita lakukan kembali testing prediksi jenis kelamin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perempuan\n"
     ]
    }
   ],
   "source": [
    "result = clf_nb.predict([\"Alifah Rahmah\"])\n",
    "print(jk_label[result[0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Random Forest\n",
    "\n",
    "Algoritma terakhir yang akan digunakan adalah [Random Forest](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html). Lansung saja kita coba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93127413127413128"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf_rf = Pipeline([('vect', CountVectorizer(analyzer = 'char_wb', ngram_range=(2,6))),\n",
    "                     ('clf', RandomForestClassifier(n_estimators=90, n_jobs=-1)),\n",
    "])\n",
    "\n",
    "clf_rf = clf_rf.fit(text_train.ravel(), y_train.ravel())\n",
    "predicted = clf_rf.predict(text_test.ravel())\n",
    "np.mean(predicted == y_test.ravel())  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Dengan algoritman Random Forest, tingkat akurasi yang didapatkan lebih rendah dari dua algoritma sebelumnya, itu sebesar **93.12%**. \n",
    "Algoritma ini juga mempunyai kekurangan, yaitu performance yang yang lebih lambat.\n",
    "Ok, Mari kita lakukan kembali testing prediksi jenis kelamin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Laki-Laki\n"
     ]
    }
   ],
   "source": [
    "result = clf_rf.predict([\"Yuni ahmad\"])\n",
    "print(jk_label[result[0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Github Repository\n",
    "\n",
    "Saya telah membuat implementasi aplikasi prediksi jenis kelamin, silahkan cek di [github](https://github.com/irfani/Jenis-Kelamin)."
   ]
  }
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