Stunting Detection.ipynb

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       "      <th>Umur (bulan)</th>\n",
       "      <th>Jenis Kelamin</th>\n",
       "      <th>Tinggi Badan (cm)</th>\n",
       "      <th>Status Gizi</th>\n",
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       "      <td>laki-laki</td>\n",
       "      <td>44.591973</td>\n",
       "      <td>stunted</td>\n",
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       "      <td>0</td>\n",
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       "      <td>tinggi</td>\n",
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       "      <td>0</td>\n",
       "      <td>laki-laki</td>\n",
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       "      <td>normal</td>\n",
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       "      <td>0</td>\n",
       "      <td>laki-laki</td>\n",
       "      <td>47.508026</td>\n",
       "      <td>normal</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>laki-laki</td>\n",
       "      <td>42.743494</td>\n",
       "      <td>severely stunted</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>perempuan</td>\n",
       "      <td>100.600000</td>\n",
       "      <td>normal</td>\n",
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       "      <td>60</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>98.300000</td>\n",
       "      <td>stunted</td>\n",
       "    </tr>\n",
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       "      <td>60</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>121.300000</td>\n",
       "      <td>normal</td>\n",
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       "      <td>perempuan</td>\n",
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       "      <td>perempuan</td>\n",
       "      <td>109.800000</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120999 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "        Umur (bulan) Jenis Kelamin  Tinggi Badan (cm)       Status Gizi\n",
       "0                  0     laki-laki          44.591973           stunted\n",
       "1                  0     laki-laki          56.705203            tinggi\n",
       "2                  0     laki-laki          46.863358            normal\n",
       "3                  0     laki-laki          47.508026            normal\n",
       "4                  0     laki-laki          42.743494  severely stunted\n",
       "...              ...           ...                ...               ...\n",
       "120994            60     perempuan         100.600000            normal\n",
       "120995            60     perempuan          98.300000           stunted\n",
       "120996            60     perempuan         121.300000            normal\n",
       "120997            60     perempuan         112.200000            normal\n",
       "120998            60     perempuan         109.800000            normal\n",
       "\n",
       "[120999 rows x 4 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# membaca dataset dari file CSV\n",
    "df = pd.read_csv('data_tinggi_balita.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2e3d45be",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Umur (bulan)</th>\n",
       "      <th>Jenis Kelamin</th>\n",
       "      <th>Tinggi Badan (cm)</th>\n",
       "      <th>Status Gizi</th>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120999 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Umur (bulan)  Jenis Kelamin  Tinggi Badan (cm)  Status Gizi\n",
       "0                  0              0          44.591973            1\n",
       "1                  0              0          56.705203            3\n",
       "2                  0              0          46.863358            2\n",
       "3                  0              0          47.508026            2\n",
       "4                  0              0          42.743494            0\n",
       "...              ...            ...                ...          ...\n",
       "120994            60              1         100.600000            2\n",
       "120995            60              1          98.300000            1\n",
       "120996            60              1         121.300000            2\n",
       "120997            60              1         112.200000            2\n",
       "120998            60              1         109.800000            2\n",
       "\n",
       "[120999 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mengkodekan jenis kelamin\n",
    "df['Jenis Kelamin'] = df['Jenis Kelamin'].map({'laki-laki': 0, 'perempuan': 1})\n",
    "\n",
    "# mengkodekan status gizi\n",
    "df['Status Gizi'] = df['Status Gizi'].map({'severely stunted': 0, 'stunted': 1, 'normal': 2, 'tinggi': 3})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "15ddbe2f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Umur (bulan)</th>\n",
       "      <th>Jenis Kelamin</th>\n",
       "      <th>Tinggi Badan (cm)</th>\n",
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       "      <th>120997</th>\n",
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       "      <td>1</td>\n",
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       "    </tr>\n",
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       "      <th>120998</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>109.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120999 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Umur (bulan)  Jenis Kelamin  Tinggi Badan (cm)\n",
       "0                  0              0          44.591973\n",
       "1                  0              0          56.705203\n",
       "2                  0              0          46.863358\n",
       "3                  0              0          47.508026\n",
       "4                  0              0          42.743494\n",
       "...              ...            ...                ...\n",
       "120994            60              1         100.600000\n",
       "120995            60              1          98.300000\n",
       "120996            60              1         121.300000\n",
       "120997            60              1         112.200000\n",
       "120998            60              1         109.800000\n",
       "\n",
       "[120999 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# memisahkan fitur dan target\n",
    "X = df.iloc[:, 1:6]\n",
    "X = df.drop('Status Gizi', axis=1)\n",
    "y = df['Status Gizi']\n",
    "\n",
    "# membagi dataset menjadi data latih dan data uji\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "61f6effa",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# membuat objek KNN dengan k=3\n",
    "knn = KNeighborsClassifier(algorithm='kd_tree', n_neighbors=1)\n",
    "\n",
    "# melatih model KNN dengan data latih\n",
    "knn.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "136c852b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Akurasi model KNN: 0.9990495867768595\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# membuat prediksi dengan data uji\n",
    "y_pred = knn.predict(X_test)\n",
    "\n",
    "# menghitung akurasi model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print('Akurasi model KNN:', accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fcff326c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2a42459e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion_matrix knn:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 4129,     1,     0,     0],\n",
       "       [    6,  2782,     2,     0],\n",
       "       [    0,     3, 13372,     7],\n",
       "       [    0,     0,     4,  3894]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('confusion_matrix knn:')\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2742aa3e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      4130\n",
      "           1       1.00      1.00      1.00      2790\n",
      "           2       1.00      1.00      1.00     13382\n",
      "           3       1.00      1.00      1.00      3898\n",
      "\n",
      "    accuracy                           1.00     24200\n",
      "   macro avg       1.00      1.00      1.00     24200\n",
      "weighted avg       1.00      1.00      1.00     24200\n",
      "\n",
      "Akurasi model KNN: 0.9990495867768595\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test.values.reshape(-1, 1), y_pred))\n",
    "print('Akurasi model KNN:', accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f91f2f15",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bf091ea0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross val:  [0.99855372 0.99876033 0.99917355 0.99958678 0.99917355]\n",
      "Accuracy :  0.9990495867768594\n"
     ]
    }
   ],
   "source": [
    "print('Cross val: ', cross_val_score(knn, y_test.values.reshape(-1, 1), y_pred, cv = 5))\n",
    "print('Accuracy : ', np.mean(cross_val_score(knn, y_test.values.reshape(-1, 1), y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fbe08b13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Score = [2]\n"
     ]
    }
   ],
   "source": [
    "# test predict model\n",
    "test = pd.DataFrame({'Umur (bulan)': [20], 'Jenis Kelamin': [1], 'Tinggi Badan (cm)': [86.2]})\n",
    "\n",
    "pred = knn.predict(test)\n",
    "print(\"Predicted Score = {}\".format(np.array(pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7f2fc8a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['knn_stunting.model']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Export the model\n",
    "import joblib\n",
    "\n",
    "joblib.dump(knn, 'knn_stunting.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f1a8b52",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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