KNN.ipynb

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        "GLCM WITH KNN"
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        "id": "DoOqvPv1lnk_"
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              "   Unnamed: 0  dissimilarity  correlation  homogeneity    contrast       ASM  \\\n",
              "0           0      19.524584     0.400429     0.073640  771.373208  0.000250   \n",
              "1           1      16.873976     0.638779     0.074152  566.026084  0.000189   \n",
              "2           2      17.314821     0.475669     0.087051  648.010860  0.000274   \n",
              "3           3      15.335422     0.645979     0.112097  602.474760  0.000393   \n",
              "4           4      16.767608     0.609280     0.083202  597.947561  0.000238   \n",
              "5           5      14.432973     0.658341     0.090737  452.004424  0.000275   \n",
              "6           6      14.215538     0.649303     0.095181  432.236564  0.000265   \n",
              "7           7      15.323906     0.466964     0.086335  485.800894  0.000338   \n",
              "8           8      14.132077     0.558247     0.089153  390.865295  0.000292   \n",
              "9           9      13.657411     0.641893     0.090611  380.240483  0.000308   \n",
              "\n",
              "     energy label  \n",
              "0  0.015820  SAPI  \n",
              "1  0.013754  SAPI  \n",
              "2  0.016559  SAPI  \n",
              "3  0.019816  SAPI  \n",
              "4  0.015412  SAPI  \n",
              "5  0.016588  SAPI  \n",
              "6  0.016274  SAPI  \n",
              "7  0.018372  SAPI  \n",
              "8  0.017075  SAPI  \n",
              "9  0.017553  SAPI  "
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              "      <th>Unnamed: 0</th>\n",
              "      <th>dissimilarity</th>\n",
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              "      <td>771.373208</td>\n",
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              "      <td>0.015820</td>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>16.873976</td>\n",
              "      <td>0.638779</td>\n",
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              "      <th>2</th>\n",
              "      <td>2</td>\n",
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              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>15.335422</td>\n",
              "      <td>0.645979</td>\n",
              "      <td>0.112097</td>\n",
              "      <td>602.474760</td>\n",
              "      <td>0.000393</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>16.767608</td>\n",
              "      <td>0.609280</td>\n",
              "      <td>0.083202</td>\n",
              "      <td>597.947561</td>\n",
              "      <td>0.000238</td>\n",
              "      <td>0.015412</td>\n",
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              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>14.432973</td>\n",
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              "      <td>0.090737</td>\n",
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              "      <td>0.000275</td>\n",
              "      <td>0.016588</td>\n",
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              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>14.215538</td>\n",
              "      <td>0.649303</td>\n",
              "      <td>0.095181</td>\n",
              "      <td>432.236564</td>\n",
              "      <td>0.000265</td>\n",
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              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>15.323906</td>\n",
              "      <td>0.466964</td>\n",
              "      <td>0.086335</td>\n",
              "      <td>485.800894</td>\n",
              "      <td>0.000338</td>\n",
              "      <td>0.018372</td>\n",
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              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>14.132077</td>\n",
              "      <td>0.558247</td>\n",
              "      <td>0.089153</td>\n",
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              "      <td>0.000292</td>\n",
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              "      <td>9</td>\n",
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          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('/content/Dataset_glcm.csv')\n",
        "df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
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        "id": "tKSV0edA5RS7",
        "outputId": "73360a41-78da-445f-81ed-7c88e2bc83d0"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 230 entries, 0 to 229\n",
            "Data columns (total 8 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Unnamed: 0     230 non-null    int64  \n",
            " 1   dissimilarity  230 non-null    float64\n",
            " 2   correlation    230 non-null    float64\n",
            " 3   homogeneity    230 non-null    float64\n",
            " 4   contrast       230 non-null    float64\n",
            " 5   ASM            230 non-null    float64\n",
            " 6   energy         230 non-null    float64\n",
            " 7   label          230 non-null    object \n",
            "dtypes: float64(6), int64(1), object(1)\n",
            "memory usage: 14.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# hapus atribut yang tidak berguna\n",
        "df.drop('Unnamed: 0',axis=1,inplace=True)\n",
        "df.head()"
      ],
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        "colab": {
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          "height": 206
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        "id": "ov70dzK65j4Z",
        "outputId": "446ed592-7ddf-4919-997a-7f447f0568c2"
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            "text/plain": [
              "   dissimilarity  correlation  homogeneity    contrast       ASM    energy  \\\n",
              "0      19.524584     0.400429     0.073640  771.373208  0.000250  0.015820   \n",
              "1      16.873976     0.638779     0.074152  566.026084  0.000189  0.013754   \n",
              "2      17.314821     0.475669     0.087051  648.010860  0.000274  0.016559   \n",
              "3      15.335422     0.645979     0.112097  602.474760  0.000393  0.019816   \n",
              "4      16.767608     0.609280     0.083202  597.947561  0.000238  0.015412   \n",
              "\n",
              "  label  \n",
              "0  SAPI  \n",
              "1  SAPI  \n",
              "2  SAPI  \n",
              "3  SAPI  \n",
              "4  SAPI  "
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              "      <th>dissimilarity</th>\n",
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              "      <th>1</th>\n",
              "      <td>16.873976</td>\n",
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              "      <td>0.475669</td>\n",
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              "      <th>3</th>\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-389745be-1d80-48b6-ae15-43c1270c3495');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# memisahkan atribut dan label\n",
        "X = df[['dissimilarity', 'correlation', 'homogeneity', 'contrast', 'ASM', 'energy' ]]\n",
        "y = df['label']\n",
        "# Membagi dataset menjadi data latih & data uji\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)\n",
        "print(y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TJP_anE159Lv",
        "outputId": "38afbc3a-c57e-459a-dfe0-238c23e2863d"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "52     SAPI\n",
            "10     SAPI\n",
            "206    BABI\n",
            "91     SAPI\n",
            "26     SAPI\n",
            "       ... \n",
            "98     SAPI\n",
            "220    BABI\n",
            "66     SAPI\n",
            "126    BABI\n",
            "109    SAPI\n",
            "Name: label, Length: 184, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tn5Fsr0h9vFx",
        "outputId": "6c6dec6c-15fb-405b-e1b4-9c0c76c2cc05"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     dissimilarity  correlation  homogeneity     contrast       ASM    energy\n",
            "184      16.182028     0.559277     0.107182   565.141870  0.000472  0.021736\n",
            "148       4.116235     0.873961     0.262647    45.582255  0.001627  0.040337\n",
            "179      17.197922     0.422061     0.073053   500.507879  0.000284  0.016860\n",
            "210       6.728120     0.891495     0.164055    89.050759  0.000634  0.025186\n",
            "81       18.483436     0.587661     0.078411   714.752920  0.000211  0.014517\n",
            "89       17.567609     0.609985     0.097766   753.249809  0.000283  0.016819\n",
            "120      11.028603     0.774950     0.131268   303.956640  0.000513  0.022650\n",
            "80       12.064726     0.407804     0.103602   329.257697  0.000591  0.024301\n",
            "199       6.882687     0.712012     0.143136    80.495920  0.000917  0.030279\n",
            "20       15.573975     0.572387     0.086397   492.763688  0.000284  0.016852\n",
            "42       13.257855     0.690077     0.092588   383.775188  0.000359  0.018956\n",
            "130       9.689189     0.505650     0.168322   266.235322  0.000849  0.029142\n",
            "85       14.396089     0.628820     0.094796   473.930870  0.000313  0.017687\n",
            "178       6.909522     0.910240     0.167259   115.271280  0.000523  0.022859\n",
            "24       12.368831     0.475483     0.115718   354.244753  0.000591  0.024303\n",
            "219       7.404838     0.517875     0.187072   137.723696  0.001597  0.039962\n",
            "19       11.818911     0.494523     0.130336   372.082200  0.000869  0.029483\n",
            "53       21.928077     0.378184     0.071834  1050.663691  0.000237  0.015409\n",
            "108      15.243749     0.667889     0.089215   498.294353  0.000244  0.015632\n",
            "167      10.545297     0.388143     0.150599   316.404650  0.000817  0.028584\n",
            "171       9.950840     0.583775     0.107862   177.911202  0.000587  0.024236\n",
            "122       8.217041     0.647594     0.131231   125.100845  0.000823  0.028690\n",
            "79       16.039666     0.518215     0.074186   504.647764  0.000253  0.015914\n",
            "82       18.545606     0.616139     0.079710   786.792592  0.000210  0.014503\n",
            "190      13.480123     0.447013     0.183926   672.310646  0.001844  0.042946\n",
            "213      10.019364     0.736709     0.175234   265.802295  0.000690  0.026269\n",
            "127       9.943833     0.356576     0.167123   300.446500  0.001186  0.034439\n",
            "33       15.737030     0.580776     0.105048   658.805037  0.000424  0.020585\n",
            "211       8.631024     0.552013     0.119398   127.723823  0.000793  0.028167\n",
            "31       17.209761     0.501102     0.074744   594.169416  0.000252  0.015869\n",
            "71       10.521332     0.746281     0.127955   251.190721  0.000562  0.023708\n",
            "182      14.641961     0.534887     0.100612   495.060261  0.000487  0.022065\n",
            "194       6.215234     0.892597     0.166760    70.467200  0.000699  0.026431\n",
            "203      11.851375     0.910060     0.151165   351.437298  0.000388  0.019689\n",
            "133       9.665210     0.414726     0.154455   221.014753  0.001203  0.034683\n",
            "4        16.767608     0.609280     0.083202   597.947561  0.000238  0.015412\n",
            "37       13.824450     0.631875     0.115657   514.739160  0.000393  0.019816\n",
            "72       19.129945     0.577358     0.075765   795.518055  0.000207  0.014378\n",
            "21       15.474372     0.598685     0.077599   459.704979  0.000247  0.015701\n",
            "212       7.531881     0.632259     0.171774   138.459699  0.001220  0.034931\n",
            "207       7.256854     0.464993     0.162155   103.079496  0.001614  0.040170\n",
            "177       4.456140     0.650845     0.235796    44.981911  0.002873  0.053596\n",
            "157      10.524194     0.920394     0.120574   234.358837  0.000295  0.017187\n",
            "175       6.672053     0.592675     0.188130   121.896974  0.001312  0.036228\n",
            "23       13.961657     0.647162     0.112407   478.634781  0.000438  0.020939\n",
            "61       12.881055     0.590061     0.116827   402.322767  0.000423  0.020562\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "52idXhFAz5JG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ALGORITMA KNN**"
      ],
      "metadata": {
        "id": "kM--0Fu7654R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "jkTCB1BZ65O7"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_KNN = KNeighborsClassifier(5)"
      ],
      "metadata": {
        "id": "md6RpGPo6aSi"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        " \n",
        "# mengevaluasi performa model dengan cross_val_score\n",
        "scores = cross_val_score(model_KNN, X, y, cv=5)\n",
        "scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HwP8jsUf7NJw",
        "outputId": "124aa3f1-2105-46cb-f7ea-4cea7e8d244a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.84782609, 0.80434783, 0.82608696, 0.7826087 , 0.89130435])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_KNN.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q4u3lc-c7yDB",
        "outputId": "20f159e6-c9c6-42f3-f8e0-72098d035f02"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn.metrics as metrics\n",
        "#data train accuracy\n",
        "y_pred = model_KNN.predict(X_train)\n",
        "print(metrics.accuracy_score(y_train, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "du_1Pqgy71-D",
        "outputId": "43dc8e88-64e4-4bdc-a667-71cc15affc44"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.875\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#data test accuracy\n",
        "y_pred = model_KNN.predict(X_test)\n",
        "print(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "saYKsXnO8bJt",
        "outputId": "1c180bc1-e1a7-4047-f7a3-2c4990fc7bec"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8043478260869565\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model_KNN.predict([[12.511137, 0.576175, 0.107229, 352.602871, 0.000396,0.019911]])[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "quAv2lVG9jEG",
        "outputId": "7af702aa-35e9-4a6b-ef49-91f9c88eacca"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SAPI\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save the model to disk\n",
        "import pickle\n",
        "filename = 'KNN Model.sav'\n",
        "pickle.dump(model_KNN, open(filename, 'wb'))"
      ],
      "metadata": {
        "id": "eb_-MrfB8oZ9"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "LBP WITH KNN"
      ],
      "metadata": {
        "id": "vFmfWehrl1UF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df2 = pd.read_csv('/content/Dataset_LBP.csv')\n",
        "df2.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "XcXGZvrIlz41",
        "outputId": "1c3e7301-aa99-4b64-b101-acb8b0b3a01c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Uniformity    Energy   Entropy Label\n",
              "0         0.0  0.233118  0.744894  SAPI\n",
              "1         0.0  0.314254  0.252407  SAPI\n",
              "2         0.0  0.187299  0.758315  SAPI\n",
              "3         0.0  0.194237  0.344809  SAPI\n",
              "4         0.0  0.321262  0.561610  SAPI\n",
              "5         0.0  0.353561  0.389196  SAPI\n",
              "6         0.0  0.444464  0.355075  SAPI\n",
              "7         0.0  0.308632  0.620955  SAPI\n",
              "8         0.0  0.254161  0.244014  SAPI\n",
              "9         0.0  0.210585  0.572214  SAPI"
            ],
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Uniformity</th>\n",
              "      <th>Energy</th>\n",
              "      <th>Entropy</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
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              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.252407</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.344809</td>\n",
              "      <td>SAPI</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.561610</td>\n",
              "      <td>SAPI</td>\n",
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              "    <tr>\n",
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              "      <td>0.389196</td>\n",
              "      <td>SAPI</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.355075</td>\n",
              "      <td>SAPI</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.0</td>\n",
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              "      <td>SAPI</td>\n",
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              "    <tr>\n",
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              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JfSNexXUmJQo",
        "outputId": "d3e45afa-540d-4b6d-f3ce-5ed654807e84"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 230 entries, 0 to 229\n",
            "Data columns (total 4 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Uniformity  230 non-null    float64\n",
            " 1   Energy      230 non-null    float64\n",
            " 2   Entropy     230 non-null    float64\n",
            " 3   Label       230 non-null    object \n",
            "dtypes: float64(3), object(1)\n",
            "memory usage: 7.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# memisahkan atribut dan label\n",
        "X = df2[['Uniformity', 'Energy', 'Entropy' ]]\n",
        "y = df2['Label']\n",
        "# Membagi dataset menjadi data latih & data uji\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)\n",
        "print(y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8meJBTC3mP2T",
        "outputId": "9089b00e-5280-4a6a-dc09-935d747c2148"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "52     SAPI\n",
            "10     SAPI\n",
            "206    BABI\n",
            "91     SAPI\n",
            "26     SAPI\n",
            "       ... \n",
            "98     SAPI\n",
            "220    BABI\n",
            "66     SAPI\n",
            "126    BABI\n",
            "109    SAPI\n",
            "Name: Label, Length: 184, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q-O1L3V4ogc5",
        "outputId": "deb2c495-9a15-4b25-c566-9b8df5e6a665"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "184    BABI\n",
            "148    BABI\n",
            "179    BABI\n",
            "210    BABI\n",
            "81     SAPI\n",
            "89     SAPI\n",
            "120    BABI\n",
            "80     SAPI\n",
            "199    BABI\n",
            "20     SAPI\n",
            "42     SAPI\n",
            "130    BABI\n",
            "85     SAPI\n",
            "178    BABI\n",
            "24     SAPI\n",
            "219    BABI\n",
            "19     SAPI\n",
            "53     SAPI\n",
            "108    SAPI\n",
            "167    BABI\n",
            "171    BABI\n",
            "122    BABI\n",
            "79     SAPI\n",
            "82     SAPI\n",
            "190    BABI\n",
            "213    BABI\n",
            "127    BABI\n",
            "33     SAPI\n",
            "211    BABI\n",
            "31     SAPI\n",
            "71     SAPI\n",
            "182    BABI\n",
            "194    BABI\n",
            "203    BABI\n",
            "133    BABI\n",
            "4      SAPI\n",
            "37     SAPI\n",
            "72     SAPI\n",
            "21     SAPI\n",
            "212    BABI\n",
            "207    BABI\n",
            "177    BABI\n",
            "157    BABI\n",
            "175    BABI\n",
            "23     SAPI\n",
            "61     SAPI\n",
            "Name: Label, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p08M1xVtoluA",
        "outputId": "aac0d270-51d7-4c06-d972-44b976bffbc9"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Uniformity    Energy   Entropy\n",
            "184         0.0  0.004601  0.964865\n",
            "148         0.0  0.007683  0.895133\n",
            "179         0.0  0.007780  0.965313\n",
            "210         0.0  0.040091  0.938636\n",
            "81          0.0  0.301163  0.317992\n",
            "89          0.0  0.096762  0.465923\n",
            "120         0.0  0.005247  0.994994\n",
            "80          0.0  0.121663  0.780420\n",
            "199         0.0  0.026225  0.956168\n",
            "20          0.0  0.077855  0.480640\n",
            "42          0.0  0.227989  0.450221\n",
            "130         0.0  0.009995  0.979122\n",
            "85          0.0  0.204813  0.251066\n",
            "178         0.0  0.009871  0.938239\n",
            "24          0.0  0.343056  0.438873\n",
            "219         0.0  0.026759  0.954130\n",
            "19          0.0  0.517164  0.439781\n",
            "53          0.0  0.236255  0.392626\n",
            "108         0.0  0.294802  0.695052\n",
            "167         0.0  0.035821  0.942253\n",
            "171         0.0  0.025566  0.980388\n",
            "122         0.0  0.025217  0.977573\n",
            "79          0.0  0.086832  0.659033\n",
            "82          0.0  0.860633  0.283010\n",
            "190         0.0  0.015989  0.991594\n",
            "213         0.0  0.003635  0.974131\n",
            "127         0.0  0.001502  0.978300\n",
            "33          0.0  0.630738  0.467313\n",
            "211         0.0  0.027273  0.973355\n",
            "31          0.0  0.108369  0.643999\n",
            "71          0.0  0.085404  0.676943\n",
            "182         0.0  0.002905  0.982218\n",
            "194         0.0  0.014485  0.875608\n",
            "203         0.0  0.034642  0.967742\n",
            "133         0.0  0.023437  0.972175\n",
            "4           0.0  0.321262  0.561610\n",
            "37          0.0  0.103653  0.694009\n",
            "72          0.0  0.314920  0.599718\n",
            "21          0.0  0.175705  0.523344\n",
            "212         0.0  0.019023  0.961824\n",
            "207         0.0  0.014573  0.947922\n",
            "177         0.0  0.021738  0.875081\n",
            "157         0.0  0.013464  0.965863\n",
            "175         0.0  0.006803  0.941827\n",
            "23          0.0  0.143212  0.521575\n",
            "61          0.0  0.275632  0.457617\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ALGORITMA KNN"
      ],
      "metadata": {
        "id": "7JrO35KRmuab"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "-13jjrQomiRw"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_KNN2 = KNeighborsClassifier(5)"
      ],
      "metadata": {
        "id": "0FWDW03pmxwG"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        " \n",
        "# mengevaluasi performa model dengan cross_val_score\n",
        "scores = cross_val_score(model_KNN2, X, y, cv=5)\n",
        "scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5GrTzdS5nFIa",
        "outputId": "421b696e-4f90-45a4-c30a-6c3fab2c2c1e"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.        , 1.        , 1.        , 1.        , 0.97826087])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_KNN2.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AsHenCQonhX6",
        "outputId": "2a59b6dd-4204-4761-af87-e9709908079c"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn.metrics as metrics\n",
        "#data train accuracy\n",
        "y_pred = model_KNN2.predict(X_train)\n",
        "print(metrics.accuracy_score(y_train, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DuSIq7I0ntPj",
        "outputId": "9670fe3b-8f2d-4eb1-ce99-d7031426e95b"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9945652173913043\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#data test accuracy\n",
        "y_pred = model_KNN2.predict(X_test)\n",
        "print(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gm6oQMC-n5jr",
        "outputId": "714d73da-488d-421a-d9e1-33b7bf8490e5"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model_KNN2.predict([[0.0,  0.007683, 0.895133]])[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZGoMrDScoZSQ",
        "outputId": "3552f4d3-b4b6-40b6-aac2-bbdb81173856"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BABI\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save the model to disk\n",
        "import pickle\n",
        "filename = 'KNN Model2.sav'\n",
        "pickle.dump(model_KNN2, open(filename, 'wb'))"
      ],
      "metadata": {
        "id": "LmPDhACto4h8"
      },
      "execution_count": 37,
      "outputs": []
    }
  ]
}