knn_iris.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bb19d2fb",
   "metadata": {},
   "source": [
    "# Penerapan Machine Learning dengan Pendekatan Supervised Studi Kasus Implementasi Klasifikasi 3 Jenis Bunga Iris (Bunga Pelangi) {iris tectorum} dengan Metode K-Nearst Neighbor (KNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8850505c",
   "metadata": {},
   "source": [
    "## 1. Memanggil Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1ea19dc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Menanggil library pandas untuk manipulasi data, seperti: membuat tabel, mengubah dimensi data, mengecek data,dsb.\n",
    "import pandas as pd\n",
    "#Memanggil library matplotlib untuk grafik plot.\n",
    "import matplotlib.pyplot as plt\n",
    "#Memanggi library sklearn untuk import dataset\n",
    "from sklearn import datasets\n",
    "#Menaggil library sklearn untuk KKN Classifier\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc5df6df",
   "metadata": {},
   "source": [
    "## 2. Memanggil dataset dari library sklearn\n",
    "### Untuk memanggil Iris dataset, kemudian memasukkannya ke dalam variabel 'bunga'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bc26d0ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sklearn.utils.Bunch'>\n"
     ]
    }
   ],
   "source": [
    "#Memanggil IRIS datset dari obyek sklearn\n",
    "#memasukkannya ke dalam variable bunga\n",
    "\n",
    "bunga = datasets.load_iris()\n",
    "\n",
    "#Menampilkan tipe obyek dari bunga\n",
    "print(type(bunga))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1deafbc",
   "metadata": {},
   "source": [
    "### -. Kita juga bisa menampilakn dictionay keys dari dataset bunga sebagai berikut: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "459d71dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])\n"
     ]
    }
   ],
   "source": [
    "#Menampilkan dictionary keys dari data bunga \n",
    "print(bunga.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dabf13d0",
   "metadata": {},
   "source": [
    "### -. Perintah dibawah berikut ini digunakan untuk menampilkan tipe dari attribut yang ada:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a5519ea6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'> <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "#Menampilkan tipe obyek dari atribut yang ada\n",
    "print(type(bunga.data),type(bunga.target))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f7069e7",
   "metadata": {},
   "source": [
    "### -. Perintah dibawah berikut ini digunakan untuk menampilkan jumlah baris dan kolom  dari dataset Iris."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8d0ae051",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n"
     ]
    }
   ],
   "source": [
    "#Menampilkan jumlah baris dan kolom dari dataset iris\n",
    "print(bunga.data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff1af2bf",
   "metadata": {},
   "source": [
    "### -. Perintah dibawah berikut ini digunakan untuk menampilkan target set dari data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cbc71c66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "#Menampilkan target set dari data\n",
    "print(bunga.target_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "017b3464",
   "metadata": {},
   "source": [
    "## 3. Konversi Dataset ke datam Tipe DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "50f5d06a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
      "0                5.1               3.5                1.4               0.2\n",
      "1                4.9               3.0                1.4               0.2\n",
      "2                4.7               3.2                1.3               0.2\n",
      "3                4.6               3.1                1.5               0.2\n",
      "4                5.0               3.6                1.4               0.2\n"
     ]
    }
   ],
   "source": [
    "#Memanggil training dataset\n",
    "X = bunga.data\n",
    "\n",
    "#Memanggil target set\n",
    "Y = bunga.target\n",
    "\n",
    "#Melakukan konversi tipe datasets' ke dalam tipe dataframe\n",
    "df = pd.DataFrame(X, columns=bunga.feature_names)\n",
    "\n",
    "#Mencetak 5 data pertama dari dataframe\n",
    "print(df.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ed04cc6",
   "metadata": {},
   "source": [
    "## 4. Memanggil KNN Classifier dari Sklearn dan kemudian melakukan fiting modeng dengan data training serta target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7821af2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(metric='euclidean', n_neighbors=6)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Memanggil KNN Classsifier\n",
    "knn = KNeighborsClassifier(n_neighbors=6,\n",
    "                           weights='uniform',\n",
    "                           algorithm='auto',\n",
    "                          metric='euclidean')\n",
    "\n",
    "#Fitting model dengan training data dan target\n",
    "X_train = bunga['data']\n",
    "y_train = bunga['target']\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "891bf066",
   "metadata": {},
   "source": [
    "## 5. Melakukan Prediksi berdasarkan data yang diinputkan "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9da3b730",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hasil Prediksi : Jenis bunga  [2]\n"
     ]
    }
   ],
   "source": [
    "# Data yang akan diprediksi\n",
    "Data = [[6.2, 1.5, 4.2, 2.6]]\n",
    "\n",
    "# Melakukan prediksi berdasarkan \n",
    "Y_pred = knn.predict(Data)\n",
    "\n",
    "# Mencetak hasil prediksi\n",
    "# Hasil 0  adalah Setosa\n",
    "# Hasil 1  adalah Versicolor\n",
    "# Hasil 2  adalah Virginica\n",
    "print(\"Hasil Prediksi : Jenis bunga \", Y_pred)\n",
    "      "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfed3489",
   "metadata": {},
   "source": [
    "#### Contoh diatas menunjukkan prediksi dari data bunga dengan input sebagai berkut:\n",
    "#### -. Panjang sepal : 6.2 cm.\n",
    "#### -. Lebar sepal     : 1.5 cm.\n",
    "#### -. Panjang petal : 4.2 cm.\n",
    "#### -. Lebar petal     : 2.6 cm.\n",
    "\n",
    "### Hasil Perdiksi menunjukkan bahwa jenis bunga denga kondisi tersebut adalah [2] atau versicolor."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e8aeff2",
   "metadata": {},
   "source": [
    "### -. Kita juga dapat/bisa memprediksi secara langsung beberapa buah bunga masuk ke dalam array atau list sebagai berikut."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b57d9092",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hasil  Prediksi : Jenis Bunga  [2 2 1]\n"
     ]
    }
   ],
   "source": [
    "# Data yang akan diprediksi\n",
    "Data = [[6.2, 1.5, 4.2, 2.6],\n",
    "        [4.4, 1.2, 2.7, 7.5],\n",
    "        [6.4, 2.5, 4.7, 1.5]\n",
    "       ]\n",
    "\n",
    "Y_pred = knn.predict(Data)\n",
    "print(\"Hasil  Prediksi : Jenis Bunga \", Y_pred)\n",
    "\n",
    "# Mencetak hasil prediksi\n",
    "# Hasil 0  adalah Setosa\n",
    "# Hasil 1  adalah Versicolor\n",
    "# Hasil 2  adalah Virginica"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91a4c649",
   "metadata": {},
   "source": [
    "## 6. Visualisasi Data Klasifikasi Bunga Iris Menggunakan KNN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f63b6573",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Memanggil Library \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn import neighbors, datasets\n",
    "\n",
    "# Memanggil dataset\n",
    "bunga = datasets.load_iris()\n",
    "\n",
    "# Slicing menggunakan datasset 2 dimensi\n",
    "x = bunga.data[:, :2]\n",
    "y = bunga.target\n",
    "\n",
    "# Mengatur warna\n",
    "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
    "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
    "\n",
    "# Memanggil KNN dan fitting data\n",
    "knn = neighbors.KNeighborsClassifier(n_neighbors=6, weights='uniform')\n",
    "\n",
    "knn.fit(X,y)\n",
    "\n",
    "# Plot the decision boundary.\n",
    "x_min, x_max = x[:, 0].min() - 1, x[:, 0].max()+1\n",
    "y_min, y_max = x[:, 1].min() - 1, x[:, 1].max()+1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                    np.arange(y_min, y_max, h))\n",
    "\n",
    "z = knn.predict(np.c_[xx.ravel(),yy.ravel()])\n",
    "\n",
    "# Memasukkan hasil ke dalam plot color\n",
    "z = z.reshape(xx.shape)\n",
    "plt.figure()\n",
    "plt.pcolormesh(xx, yy, z, cmap=cmap_light, shading='auto')\n",
    "\n",
    "# Plot training points\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor='k', s=20)\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "plt.title(\"Klasifikasi Bunga Iris\")\n",
    "\n",
    "# Menampilkan Plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9d4a2ab1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Memanggil Library \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn import neighbors, datasets\n",
    "\n",
    "# Memanggil dataset\n",
    "bunga = datasets.load_iris()\n",
    "\n",
    "# Slicing menggunakan dataset 2 dimensi\n",
    "X = bunga.data[:, :2]\n",
    "y = bunga.target\n",
    "\n",
    "# Mengatur warna\n",
    "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
    "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
    "\n",
    "# Memanggil KNN dan fitting data\n",
    "knn = neighbors.KNeighborsClassifier(n_neighbors=6, weights='uniform')\n",
    "\n",
    "knn.fit(X, y)\n",
    "\n",
    "# Mengatur step size pada meshgrid\n",
    "h = 0.02  # Bisa diubah sesuai kebutuhan, ini menentukan resolusi grid\n",
    "\n",
    "# Plot the decision boundary.\n",
    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                    np.arange(y_min, y_max, h))\n",
    "\n",
    "z = knn.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Memasukkan hasil ke dalam plot color\n",
    "z = z.reshape(xx.shape)\n",
    "plt.figure()\n",
    "plt.pcolormesh(xx, yy, z, cmap=cmap_light, shading='gouraud')\n",
    "\n",
    "# Plot training points\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor='k', s=20)\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "plt.title(\"Klasifikasi Bunga Iris\")\n",
    "\n",
    "# Menampilkan Plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e1f8887",
   "metadata": {},
   "source": [
    "Mengatasi Peringatan:\n",
    "\n",
    "Untuk mengatasi peringatan ini, Anda bisa memperbaiki kode dengan menggunakan salah satu dari tiga opsi shading berikut ini:\n",
    "\n",
    "    shading='auto': Secara otomatis memilih shading yang tepat berdasarkan data.\n",
    "    shading='nearest': Menyesuaikan shading ke grid terdekat.\n",
    "    shading='gouraud': Menggunakan interpolasi Gouraud untuk shading yang lebih halus.\n",
    " \n",
    "Penjelasan Pilihan shading:\n",
    "\n",
    "    auto: Secara otomatis memilih shading terbaik berdasarkan dimensi dari X, Y, dan Z.\n",
    "    nearest: Shading diambil dari nilai terdekat dalam grid, yang biasanya lebih cepat dihitung tetapi tidak menghasilkan interpolasi halus.\n",
    "    gouraud: Interpolasi halus yang biasanya lebih lambat tetapi menghasilkan visualisasi yang lebih mulus.\n",
    "\n",
    "Dengan mengubah parameter shading menjadi auto, kode Anda akan lebih kompatibel dengan versi Matplotlib yang lebih baru dan menghindari kesalahan di masa mendatang."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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