From 20b1ba974ce5b01f392f2779edd8e42dba4259a5 Mon Sep 17 00:00:00 2001
From: kicap1992 <kicap.karan92@gmail.com>
Date: Mon, 28 Mar 2022 03:22:41 +0800
Subject: [PATCH] first commit

---
 .../pengujian_fuzzy-checkpoint.ipynb          | 1330 +++++++++++++++++
 app.py                                        |    1 +
 dataset/dataset.csv                           |   17 +
 dataset/rule.csv                              |   33 +
 pengujian_fuzzy.ipynb                         | 1330 +++++++++++++++++
 requirements.txt                              |  Bin 0 -> 2862 bytes
 6 files changed, 2711 insertions(+)
 create mode 100644 .ipynb_checkpoints/pengujian_fuzzy-checkpoint.ipynb
 create mode 100644 app.py
 create mode 100644 dataset/dataset.csv
 create mode 100644 dataset/rule.csv
 create mode 100644 pengujian_fuzzy.ipynb
 create mode 100644 requirements.txt

diff --git a/.ipynb_checkpoints/pengujian_fuzzy-checkpoint.ipynb b/.ipynb_checkpoints/pengujian_fuzzy-checkpoint.ipynb
new file mode 100644
index 0000000..af63302
--- /dev/null
+++ b/.ipynb_checkpoints/pengujian_fuzzy-checkpoint.ipynb
@@ -0,0 +1,1330 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0fedd17f",
+   "metadata": {},
+   "source": [
+    "# Metode Fuzzy\n",
+    "### import library numpy(untuk mengolah list dan angka) dan panda(untuk membaca dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "721c3e36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1181afa6",
+   "metadata": {},
+   "source": [
+    "### read dataset kemudian menampilkan dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "e7835698",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Usia</th>\n",
+       "      <th>Berat</th>\n",
+       "      <th>Keliling</th>\n",
+       "      <th>Ukuran_batang</th>\n",
+       "      <th>Jarak_duri</th>\n",
+       "      <th>Keterangan</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>14</td>\n",
+       "      <td>753</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Matang</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>18</td>\n",
+       "      <td>505</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Matang</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17</td>\n",
+       "      <td>750</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Matang</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15</td>\n",
+       "      <td>755</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Matang</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>14</td>\n",
+       "      <td>815</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Matang</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>17</td>\n",
+       "      <td>660</td>\n",
+       "      <td>41.5</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Matang</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>15</td>\n",
+       "      <td>700</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Matang</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>16</td>\n",
+       "      <td>820</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Matang</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>12</td>\n",
+       "      <td>850</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>11</td>\n",
+       "      <td>830</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>900</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>9</td>\n",
+       "      <td>900</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>9</td>\n",
+       "      <td>850</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13</td>\n",
+       "      <td>800</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>11</td>\n",
+       "      <td>950</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>13</td>\n",
+       "      <td>860</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Usia  Berat  Keliling  Ukuran_batang  Jarak_duri Keterangan\n",
+       "0     14    753      44.0            4.5        10.0     Matang\n",
+       "1     18    505      42.0            4.0        10.0     Matang\n",
+       "2     17    750      43.0            3.0        10.0     Matang\n",
+       "3     15    755      44.0            5.0        10.0     Matang\n",
+       "4     14    815      45.0            4.2        10.0     Matang\n",
+       "5     17    660      41.5            4.5        10.0     Matang\n",
+       "6     15    700      42.0            3.0        10.0     Matang\n",
+       "7     16    820      46.0            4.0        10.0     Matang\n",
+       "8     12    850      44.0            4.5        10.0     Mentah\n",
+       "9     11    830      42.0            4.2        10.0     Mentah\n",
+       "10    10    900      42.0            3.0         8.5     Mentah\n",
+       "11     9    900      30.0            2.5         7.5     Mentah\n",
+       "12     9    850      35.0            3.0         7.5     Mentah\n",
+       "13    13    800      40.0            3.9         9.0     Mentah\n",
+       "14    11    950      41.0            3.8         8.5     Mentah\n",
+       "15    13    860      40.0            4.0         9.5     Mentah"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_durian = pd.read_csv(\"dataset/dataset.csv\")\n",
+    "data_durian"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "990c4cac",
+   "metadata": {},
+   "source": [
+    "### menghitung field usia untuk mencari nilai semesta pembicaraan\n",
+    "### berdasarkan minimal nilai dan maksimal nilai\n",
+    "### mid_usia sebagai domain untuk fuzzy antara output masak @ mentah"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "f0789ec2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_average(min,max) :\n",
+    "    return (min + max) / 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "f3f362e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[14, 18, 17, 15, 14, 17, 15, 16, 12, 11, 10, 9, 9, 13, 11, 13]\n",
+      "13.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_usia = pd.DataFrame(data_durian)\n",
+    "data_usia = data_usia['Usia'].tolist()\n",
+    "_data_usia = data_usia\n",
+    "print(_data_usia)\n",
+    "min_usia = min(data_usia)\n",
+    "max_usia = max(data_usia)\n",
+    "# mid_usia = get_average(min_usia,max_usia) #13.5\n",
+    "mid_usia =np.median(data_usia)\n",
+    "print(mid_usia)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "9290d188",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "817.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_berat = pd.DataFrame(data_durian)\n",
+    "data_berat = data_berat['Berat'].tolist()\n",
+    "_data_berat = data_berat\n",
+    "min_berat = min(data_berat)\n",
+    "max_berat = max(data_berat)\n",
+    "# mid_berat =  get_average(min_berat,max_berat) #817.5\n",
+    "mid_berat =np.median(data_berat)\n",
+    "print(mid_berat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "16e441bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "42.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_keliling = pd.DataFrame(data_durian)\n",
+    "data_keliling = data_keliling['Keliling'].tolist()\n",
+    "_data_keliling = data_keliling\n",
+    "min_keliling = min(data_keliling)\n",
+    "max_keliling = max(data_keliling)\n",
+    "# mid_keliling = get_average(min_keliling,max_keliling) #42.0\n",
+    "mid_keliling =np.median(data_keliling)\n",
+    "print(mid_keliling)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "d51fab67",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_ukuran_batang = pd.DataFrame(data_durian)\n",
+    "data_ukuran_batang = data_ukuran_batang['Ukuran_batang'].tolist()\n",
+    "_data_ukuran_batang = data_ukuran_batang\n",
+    "min_ukuran_batang = min(data_ukuran_batang)\n",
+    "max_ukuran_batang = max(data_ukuran_batang)\n",
+    "# mid_ukuran_batang =   get_average(min_ukuran_batang,max_ukuran_batang) #4.0\n",
+    "mid_ukuran_batang =  np.median(data_ukuran_batang) #4.0\n",
+    "print(mid_ukuran_batang)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "54b442e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_jarak_duri = pd.DataFrame(data_durian)\n",
+    "data_jarak_duri = data_jarak_duri['Jarak_duri'].tolist()\n",
+    "_data_jarak_duri = data_jarak_duri\n",
+    "# print(data_jarak_duri)\n",
+    "min_jarak_duri = min(data_jarak_duri)\n",
+    "max_jarak_duri = max(data_jarak_duri)\n",
+    "mid_jarak_duri =  get_average(min_jarak_duri,max_jarak_duri)#1.0\n",
+    "# mid_jarak_duri =  np.median(data_jarak_duri)\n",
+    "print(max_jarak_duri)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4ac8e31",
+   "metadata": {},
+   "source": [
+    "### import librart skfuzzy & matplotlib untuk graph fuzzy\n",
+    "### fungsi menampilkan fuzzy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "4d525445",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import skfuzzy as fuzz\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def RangeSubjektif(_low, _high, _step):\n",
+    "    subjektif = np.arange(_low, _high , _step)\n",
+    "    return subjektif\n",
+    "\n",
+    "def FuzzyShow(_rule, _range_subjektif, _title):\n",
+    "    lo = fuzz.trapmf(_range_subjektif, _rule[0])\n",
+    "    hi = fuzz.trapmf(_range_subjektif, _rule[1])\n",
+    "    \n",
+    "    fig,ax = plt.subplots(nrows=1, figsize=(7,3))\n",
+    "    ax.plot(_range_subjektif, lo, 'g' , linewidth = 1.5 , label= \"Mentah\")\n",
+    "    ax.plot(_range_subjektif, hi, 'r' , linewidth = 1.5 , label= \"Masak\")\n",
+    "    \n",
+    "    ax.set_title(_title)\n",
+    "    ax.legend()\n",
+    "    \n",
+    "    ax.spines['top'].set_visible(False)\n",
+    "    ax.spines['right'].set_visible(False)\n",
+    "    ax.get_xaxis().tick_bottom()\n",
+    "    ax.get_yaxis().tick_left()\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "    \n",
+    "    return lo, hi\n",
+    "\n",
+    "def FuzzyShow1(_rule, _range_subjektif, _title):\n",
+    "    lo = fuzz.trapmf(_range_subjektif, _rule[0])\n",
+    "    hi = fuzz.trapmf(_range_subjektif, _rule[1])\n",
+    "    \n",
+    "    fig,ax = plt.subplots(nrows=1, figsize=(10,3))\n",
+    "    ax.plot(_range_subjektif, lo, 'r' , linewidth = 1.5 , label= \"Masak\")\n",
+    "    ax.plot(_range_subjektif, hi, 'g' , linewidth = 1.5 , label= \"Mentah\")\n",
+    "    \n",
+    "    ax.set_title(_title)\n",
+    "    ax.legend()\n",
+    "    \n",
+    "    ax.spines['top'].set_visible(False)\n",
+    "    ax.spines['right'].set_visible(False)\n",
+    "    ax.get_xaxis().tick_bottom()\n",
+    "    ax.get_yaxis().tick_left()\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "    \n",
+    "    return lo, hi\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e48d1f4",
+   "metadata": {},
+   "source": [
+    "### fuzzy untuk field usia"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "aeab6802",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_usia = RangeSubjektif(min_usia , max_usia , 1)\n",
+    "r_usia = np.array([\n",
+    "    [min_usia,min_usia,mid_usia,mid_usia],\n",
+    "    [mid_usia,mid_usia,max_usia,max_usia]\n",
+    "])\n",
+    "\n",
+    "lo_usia , hi_usia = FuzzyShow(r_usia , x_usia, 'Umur (minggu)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "1f123edb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_berat = RangeSubjektif(min_berat , max_berat , 1)\n",
+    "r_berat = np.array([\n",
+    "    [min_berat,min_berat,mid_berat,mid_berat],\n",
+    "    [mid_berat,mid_berat,max_berat,max_berat]\n",
+    "])\n",
+    "\n",
+    "lo_berat , hi_berat = FuzzyShow1(r_berat , x_berat, 'Berat (kg)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "84aaee1a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_keliling = RangeSubjektif(min_keliling , max_keliling , 1)\n",
+    "r_keliling = np.array([\n",
+    "    [min_keliling,min_keliling,mid_keliling,mid_keliling],\n",
+    "    [mid_keliling,mid_keliling,max_keliling,max_keliling]\n",
+    "])\n",
+    "\n",
+    "lo_keliling , hi_keliling = FuzzyShow(r_keliling , x_keliling, 'Keliling (cm)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "deeec0dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_ukuran_batang = RangeSubjektif(min_ukuran_batang , max_ukuran_batang , 1)\n",
+    "r_ukuran_batang = np.array([\n",
+    "    [min_ukuran_batang,min_ukuran_batang,mid_ukuran_batang,mid_ukuran_batang],\n",
+    "    [mid_ukuran_batang,mid_ukuran_batang,max_ukuran_batang,max_ukuran_batang]\n",
+    "])\n",
+    "\n",
+    "lo_ukuran_batang , hi_ukuran_batang = FuzzyShow(r_ukuran_batang , x_ukuran_batang, 'Keliling (cm)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "d3c1bed7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_jarak_duri = RangeSubjektif(min_jarak_duri , max_jarak_duri , 1)\n",
+    "r_jarak_duri = np.array([\n",
+    "    [min_jarak_duri,min_jarak_duri,mid_jarak_duri,mid_jarak_duri],\n",
+    "    [mid_jarak_duri,mid_jarak_duri,max_jarak_duri,max_jarak_duri]\n",
+    "])\n",
+    "\n",
+    "lo_jarak_duri , hi_jarak_duri = FuzzyShow(r_jarak_duri , x_jarak_duri, 'Jarak Duri (mm)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "879f64ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.0, 1.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "def FungsiKeanggotaan(_range, _min , _hi, _nilai):\n",
+    "    mini = fuzz.interp_membership(_range,_min,_nilai)\n",
+    "    hi = fuzz.interp_membership(_range,_hi,_nilai)\n",
+    "    return mini , hi\n",
+    "    \n",
+    "i = 0\n",
+    "for usia in _data_usia:\n",
+    "    ini_dia = FungsiKeanggotaan(x_usia,lo_usia,hi_usia,usia)\n",
+    "    print(ini_dia)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "d6c3a52d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 0.0)\n",
+      "(1.0, 0.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = 0\n",
+    "for berat in _data_berat:\n",
+    "    ini_dia = FungsiKeanggotaan(x_berat,hi_berat,lo_berat,berat)\n",
+    "    print(ini_dia)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "bd724cff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.0, 1.0)\n",
+      "(1.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(1.0, 0.5)\n",
+      "(1.0, 1.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.0, 1.0)\n",
+      "(1.0, 1.0)\n",
+      "(1.0, 1.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = 0\n",
+    "for keliling in _data_keliling:\n",
+    "    ini_dia = FungsiKeanggotaan(x_keliling,lo_keliling,hi_keliling,keliling)\n",
+    "    print(ini_dia)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "0370f3ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.0, 1.0)\n",
+      "(0.5, 0.5)\n",
+      "(1.0, 0.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.2999999999999998, 0.7000000000000002)\n",
+      "(0.0, 1.0)\n",
+      "(1.0, 0.0)\n",
+      "(0.5, 0.5)\n",
+      "(0.0, 1.0)\n",
+      "(0.2999999999999998, 0.7000000000000002)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(0.6000000000000001, 0.3999999999999999)\n",
+      "(0.7000000000000002, 0.2999999999999998)\n",
+      "(0.5, 0.5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = 0\n",
+    "for ukuran_batang in _data_ukuran_batang:\n",
+    "    ini_dia = FungsiKeanggotaan(x_ukuran_batang,lo_ukuran_batang,hi_ukuran_batang,ukuran_batang)\n",
+    "    print(ini_dia)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "5e3c20cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.0, 0.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(0.5, 0.5)\n",
+      "(1.0, 0.0)\n",
+      "(0.0, 1.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = 0\n",
+    "for jarak_duri in _data_jarak_duri:\n",
+    "    ini_dia = FungsiKeanggotaan(x_jarak_duri,lo_jarak_duri,hi_jarak_duri,jarak_duri)\n",
+    "    print(ini_dia)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "aa25537e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3></h3>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>No</th>\n",
+       "      <th>Rule</th>\n",
+       "      <th>Usia</th>\n",
+       "      <th>Berat</th>\n",
+       "      <th>Keliling</th>\n",
+       "      <th>Ukuran Batang</th>\n",
+       "      <th>Jarak Duri</th>\n",
+       "      <th>Keterangan</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>R1</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>R2</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>R3</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>R4</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>R5</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>R6</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>R7</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>R8</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>R9</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>R10</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>R11</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td>R12</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>13</td>\n",
+       "      <td>R13</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>14</td>\n",
+       "      <td>R14</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>15</td>\n",
+       "      <td>R15</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>16</td>\n",
+       "      <td>R16</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>17</td>\n",
+       "      <td>R17</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>18</td>\n",
+       "      <td>R18</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>19</td>\n",
+       "      <td>R19</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>20</td>\n",
+       "      <td>R20</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>21</td>\n",
+       "      <td>R21</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>22</td>\n",
+       "      <td>R22</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>23</td>\n",
+       "      <td>R23</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>24</td>\n",
+       "      <td>R24</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>25</td>\n",
+       "      <td>R25</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>26</td>\n",
+       "      <td>R26</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>27</td>\n",
+       "      <td>R27</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>28</td>\n",
+       "      <td>R28</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>29</td>\n",
+       "      <td>R29</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>30</td>\n",
+       "      <td>R30</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>31</td>\n",
+       "      <td>R31</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>32</td>\n",
+       "      <td>R32</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# data_rule = [\n",
+    "#     {'No': '1', 'Rule': 'R1', 'Usia': 'Tinggi','Berat':'Tinggi','Keliling':'Tinggi','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '2', 'Rule': 'R2', 'Usia': 'Tinggi','Berat':'Tinggi','Keliling':'Tinggi','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '3', 'Rule': 'R3', 'Usia': 'Tinggi','Berat':'Tinggi','Keliling':'Tinggi','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '4', 'Rule': 'R4', 'Usia': 'Tinggi','Berat':'Tinggi','Keliling':'Tinggi','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '5', 'Rule': 'R5', 'Usia': 'Tinggi','Berat':'Tinggi','Keliling':'Rendah','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '6', 'Rule': 'R6', 'Usia': 'Tinggi','Berat':'Tinggi','Keliling':'Rendah','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '7', 'Rule': 'R7', 'Usia': 'Tinggi','Berat':'Tinggi','Keliling':'Rendah','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '8', 'Rule': 'R8', 'Usia': 'Tinggi','Berat':'Tinggi','Keliling':'Rendah','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '9', 'Rule': 'R9', 'Usia': 'Tinggi','Berat':'Rendah','Keliling':'Tinggi','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '10', 'Rule': 'R10', 'Usia': 'Tinggi','Berat':'Rendah','Keliling':'Tinggi','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '11', 'Rule': 'R11', 'Usia': 'Tinggi','Berat':'Rendah','Keliling':'Tinggi','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '12', 'Rule': 'R12', 'Usia': 'Tinggi','Berat':'Rendah','Keliling':'Tinggi','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '13', 'Rule': 'R13', 'Usia': 'Tinggi','Berat':'Rendah','Keliling':'Rendah','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '14', 'Rule': 'R14', 'Usia': 'Tinggi','Berat':'Rendah','Keliling':'Rendah','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '15', 'Rule': 'R15', 'Usia': 'Tinggi','Berat':'Rendah','Keliling':'Rendah','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '16', 'Rule': 'R16', 'Usia': 'Tinggi','Berat':'Rendah','Keliling':'Rendah','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '17', 'Rule': 'R17', 'Usia': 'Rendah','Berat':'Tinggi','Keliling':'Tinggi','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '18', 'Rule': 'R18', 'Usia': 'Rendah','Berat':'Tinggi','Keliling':'Tinggi','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '19', 'Rule': 'R19', 'Usia': 'Rendah','Berat':'Tinggi','Keliling':'Tinggi','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '20', 'Rule': 'R20', 'Usia': 'Rendah','Berat':'Tinggi','Keliling':'Tinggi','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '21', 'Rule': 'R21', 'Usia': 'Rendah','Berat':'Tinggi','Keliling':'Rendah','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '22', 'Rule': 'R22', 'Usia': 'Rendah','Berat':'Tinggi','Keliling':'Rendah','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '23', 'Rule': 'R23', 'Usia': 'Rendah','Berat':'Tinggi','Keliling':'Rendah','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '24', 'Rule': 'R24', 'Usia': 'Rendah','Berat':'Tinggi','Keliling':'Rendah','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '25', 'Rule': 'R25', 'Usia': 'Rendah','Berat':'Rendah','Keliling':'Tinggi','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '26', 'Rule': 'R26', 'Usia': 'Rendah','Berat':'Rendah','Keliling':'Tinggi','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '27', 'Rule': 'R27', 'Usia': 'Rendah','Berat':'Rendah','Keliling':'Tinggi','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '28', 'Rule': 'R28', 'Usia': 'Rendah','Berat':'Rendah','Keliling':'Tinggi','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '29', 'Rule': 'R29', 'Usia': 'Rendah','Berat':'Rendah','Keliling':'Rendah','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '30', 'Rule': 'R30', 'Usia': 'Rendah','Berat':'Rendah','Keliling':'Rendah','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '31', 'Rule': 'R31', 'Usia': 'Rendah','Berat':'Rendah','Keliling':'Rendah','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '32', 'Rule': 'R32', 'Usia': 'Rendah','Berat':'Rendah','Keliling':'Rendah','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Mentah'},    \n",
+    "# ] \n",
+    "\n",
+    "# Creates DataFrame. \n",
+    "# df = pd.DataFrame(data_rule) \n",
+    "# df.to_excel(\"output.xlsx\")\n",
+    "df = pd.read_csv(\"dataset/rule.csv\")\n",
+    "    \n",
+    "# Print the data \n",
+    "# df\n",
+    "from IPython.display import display, HTML\n",
+    "display(HTML(\"<h3></h3>\"))\n",
+    "display(HTML(df.to_html(index=False)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "b4a51b4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('R', 'R', 'R', 'R', 'R')\n",
+      "('R', 'R', 'R', 'R', 'T')\n",
+      "('R', 'R', 'R', 'T', 'R')\n",
+      "('R', 'R', 'R', 'T', 'T')\n",
+      "('R', 'R', 'T', 'R', 'R')\n",
+      "('R', 'R', 'T', 'R', 'T')\n",
+      "('R', 'R', 'T', 'T', 'R')\n",
+      "('R', 'R', 'T', 'T', 'T')\n",
+      "('R', 'T', 'R', 'R', 'R')\n",
+      "('R', 'T', 'R', 'R', 'T')\n",
+      "('R', 'T', 'R', 'T', 'R')\n",
+      "('R', 'T', 'R', 'T', 'T')\n",
+      "('R', 'T', 'T', 'R', 'R')\n",
+      "('R', 'T', 'T', 'R', 'T')\n",
+      "('R', 'T', 'T', 'T', 'R')\n",
+      "('R', 'T', 'T', 'T', 'T')\n",
+      "('T', 'R', 'R', 'R', 'R')\n",
+      "('T', 'R', 'R', 'R', 'T')\n",
+      "('T', 'R', 'R', 'T', 'R')\n",
+      "('T', 'R', 'R', 'T', 'T')\n",
+      "('T', 'R', 'T', 'R', 'R')\n",
+      "('T', 'R', 'T', 'R', 'T')\n",
+      "('T', 'R', 'T', 'T', 'R')\n",
+      "('T', 'R', 'T', 'T', 'T')\n",
+      "('T', 'T', 'R', 'R', 'R')\n",
+      "('T', 'T', 'R', 'R', 'T')\n",
+      "('T', 'T', 'R', 'T', 'R')\n",
+      "('T', 'T', 'R', 'T', 'T')\n",
+      "('T', 'T', 'T', 'R', 'R')\n",
+      "('T', 'T', 'T', 'R', 'T')\n",
+      "('T', 'T', 'T', 'T', 'R')\n",
+      "('T', 'T', 'T', 'T', 'T')\n",
+      "32\n"
+     ]
+    }
+   ],
+   "source": [
+    "import itertools as it\n",
+    "\n",
+    "\n",
+    "my_dict={'Usia':['R','T'],'Berat':['R','T'],'Keliling':['R','T'],'Ukuran Batang':['R','T'],'Jarak Duri':['R','T']}\n",
+    "allNames = sorted(my_dict)\n",
+    "combinations = it.product(*(my_dict[Name] for Name in allNames))\n",
+    "counter = 0\n",
+    "for combi in combinations:\n",
+    "    print(combi)\n",
+    "    counter = counter + 1\n",
+    "print(counter)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/app.py b/app.py
new file mode 100644
index 0000000..ce47b77
--- /dev/null
+++ b/app.py
@@ -0,0 +1 @@
+print("hello")
\ No newline at end of file
diff --git a/dataset/dataset.csv b/dataset/dataset.csv
new file mode 100644
index 0000000..ef938cc
--- /dev/null
+++ b/dataset/dataset.csv
@@ -0,0 +1,17 @@
+Usia,Berat,Keliling,Ukuran_batang,Jarak_duri,Keterangan
+14,753,44,4.5,10,Matang
+18,505,42,4,10,Matang
+17,750,43,3,10,Matang
+15,755,44,5,10,Matang
+14,815,45,4.2,10,Matang
+17,660,41.5,4.5,10,Matang
+15,700,42,3,10,Matang
+16,820,46,4,10,Matang
+12,850,44,4.5,10,Mentah
+11,830,42,4.2,10,Mentah
+10,900,42,3,8.5,Mentah
+9,900,30,2.5,7.5,Mentah
+9,850,35,3,7.5,Mentah
+13,800,40,3.9,9,Mentah
+11,950,41,3.8,8.5,Mentah
+13,860,40,4,9.5,Mentah
diff --git a/dataset/rule.csv b/dataset/rule.csv
new file mode 100644
index 0000000..f1a5a13
--- /dev/null
+++ b/dataset/rule.csv
@@ -0,0 +1,33 @@
+No,Rule,Usia,Berat,Keliling,Ukuran Batang,Jarak Duri,Keterangan
+1,R1,Tinggi,Tinggi,Tinggi,Tinggi,Tinggi,Masak
+2,R2,Tinggi,Tinggi,Tinggi,Tinggi,Rendah,Masak
+3,R3,Tinggi,Tinggi,Tinggi,Rendah,Tinggi,Masak
+4,R4,Tinggi,Tinggi,Tinggi,Rendah,Rendah,Masak
+5,R5,Tinggi,Tinggi,Rendah,Tinggi,Tinggi,Masak
+6,R6,Tinggi,Tinggi,Rendah,Tinggi,Rendah,Masak
+7,R7,Tinggi,Tinggi,Rendah,Rendah,Tinggi,Masak
+8,R8,Tinggi,Tinggi,Rendah,Rendah,Rendah,Mentah
+9,R9,Tinggi,Rendah,Tinggi,Tinggi,Tinggi,Masak
+10,R10,Tinggi,Rendah,Tinggi,Tinggi,Rendah,Masak
+11,R11,Tinggi,Rendah,Tinggi,Rendah,Tinggi,Masak
+12,R12,Tinggi,Rendah,Tinggi,Rendah,Rendah,Masak
+13,R13,Tinggi,Rendah,Rendah,Tinggi,Tinggi,Masak
+14,R14,Tinggi,Rendah,Rendah,Tinggi,Rendah,Masak
+15,R15,Tinggi,Rendah,Rendah,Rendah,Tinggi,Masak
+16,R16,Tinggi,Rendah,Rendah,Rendah,Rendah,Masak
+17,R17,Rendah,Tinggi,Tinggi,Tinggi,Tinggi,Mentah
+18,R18,Rendah,Tinggi,Tinggi,Tinggi,Rendah,Mentah
+19,R19,Rendah,Tinggi,Tinggi,Rendah,Tinggi,Mentah
+20,R20,Rendah,Tinggi,Tinggi,Rendah,Rendah,Mentah
+21,R21,Rendah,Tinggi,Rendah,Tinggi,Tinggi,Mentah
+22,R22,Rendah,Tinggi,Rendah,Tinggi,Rendah,Mentah
+23,R23,Rendah,Tinggi,Rendah,Rendah,Tinggi,Mentah
+24,R24,Rendah,Tinggi,Rendah,Rendah,Rendah,Mentah
+25,R25,Rendah,Rendah,Tinggi,Tinggi,Tinggi,Masak
+26,R26,Rendah,Rendah,Tinggi,Tinggi,Rendah,Mentah
+27,R27,Rendah,Rendah,Tinggi,Rendah,Tinggi,Mentah
+28,R28,Rendah,Rendah,Tinggi,Rendah,Rendah,Mentah
+29,R29,Rendah,Rendah,Rendah,Tinggi,Tinggi,Mentah
+30,R30,Rendah,Rendah,Rendah,Tinggi,Rendah,Mentah
+31,R31,Rendah,Rendah,Rendah,Rendah,Tinggi,Mentah
+32,R32,Rendah,Rendah,Rendah,Rendah,Rendah,Mentah
diff --git a/pengujian_fuzzy.ipynb b/pengujian_fuzzy.ipynb
new file mode 100644
index 0000000..a937b0f
--- /dev/null
+++ b/pengujian_fuzzy.ipynb
@@ -0,0 +1,1330 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0fedd17f",
+   "metadata": {},
+   "source": [
+    "# Metode Fuzzy\n",
+    "### import library numpy(untuk mengolah list dan angka) dan panda(untuk membaca dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "721c3e36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1181afa6",
+   "metadata": {},
+   "source": [
+    "### read dataset kemudian menampilkan dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "e7835698",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Usia</th>\n",
+       "      <th>Berat</th>\n",
+       "      <th>Keliling</th>\n",
+       "      <th>Ukuran_batang</th>\n",
+       "      <th>Jarak_duri</th>\n",
+       "      <th>Keterangan</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>14</td>\n",
+       "      <td>753</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Matang</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>18</td>\n",
+       "      <td>505</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Matang</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17</td>\n",
+       "      <td>750</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Matang</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15</td>\n",
+       "      <td>755</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Matang</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>14</td>\n",
+       "      <td>815</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Matang</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>17</td>\n",
+       "      <td>660</td>\n",
+       "      <td>41.5</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Matang</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>15</td>\n",
+       "      <td>700</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Matang</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>16</td>\n",
+       "      <td>820</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Matang</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>12</td>\n",
+       "      <td>850</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>11</td>\n",
+       "      <td>830</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>900</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>9</td>\n",
+       "      <td>900</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>9</td>\n",
+       "      <td>850</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13</td>\n",
+       "      <td>800</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>11</td>\n",
+       "      <td>950</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>13</td>\n",
+       "      <td>860</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Usia  Berat  Keliling  Ukuran_batang  Jarak_duri Keterangan\n",
+       "0     14    753      44.0            4.5        10.0     Matang\n",
+       "1     18    505      42.0            4.0        10.0     Matang\n",
+       "2     17    750      43.0            3.0        10.0     Matang\n",
+       "3     15    755      44.0            5.0        10.0     Matang\n",
+       "4     14    815      45.0            4.2        10.0     Matang\n",
+       "5     17    660      41.5            4.5        10.0     Matang\n",
+       "6     15    700      42.0            3.0        10.0     Matang\n",
+       "7     16    820      46.0            4.0        10.0     Matang\n",
+       "8     12    850      44.0            4.5        10.0     Mentah\n",
+       "9     11    830      42.0            4.2        10.0     Mentah\n",
+       "10    10    900      42.0            3.0         8.5     Mentah\n",
+       "11     9    900      30.0            2.5         7.5     Mentah\n",
+       "12     9    850      35.0            3.0         7.5     Mentah\n",
+       "13    13    800      40.0            3.9         9.0     Mentah\n",
+       "14    11    950      41.0            3.8         8.5     Mentah\n",
+       "15    13    860      40.0            4.0         9.5     Mentah"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_durian = pd.read_csv(\"dataset/dataset.csv\")\n",
+    "data_durian"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "990c4cac",
+   "metadata": {},
+   "source": [
+    "### menghitung field usia untuk mencari nilai semesta pembicaraan\n",
+    "### berdasarkan minimal nilai dan maksimal nilai\n",
+    "### mid_usia sebagai domain untuk fuzzy antara output masak @ mentah"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "f0789ec2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_average(min,max) :\n",
+    "    return (min + max) / 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "f3f362e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[14, 18, 17, 15, 14, 17, 15, 16, 12, 11, 10, 9, 9, 13, 11, 13]\n",
+      "13.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_usia = pd.DataFrame(data_durian)\n",
+    "data_usia = data_usia['Usia'].tolist()\n",
+    "_data_usia = data_usia\n",
+    "print(_data_usia)\n",
+    "min_usia = min(data_usia)\n",
+    "max_usia = max(data_usia)\n",
+    "# mid_usia = get_average(min_usia,max_usia) #13.5\n",
+    "mid_usia =np.median(data_usia)\n",
+    "print(mid_usia)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "9290d188",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "817.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_berat = pd.DataFrame(data_durian)\n",
+    "data_berat = data_berat['Berat'].tolist()\n",
+    "_data_berat = data_berat\n",
+    "min_berat = min(data_berat)\n",
+    "max_berat = max(data_berat)\n",
+    "# mid_berat =  get_average(min_berat,max_berat) #817.5\n",
+    "mid_berat =np.median(data_berat)\n",
+    "print(mid_berat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "16e441bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "42.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_keliling = pd.DataFrame(data_durian)\n",
+    "data_keliling = data_keliling['Keliling'].tolist()\n",
+    "_data_keliling = data_keliling\n",
+    "min_keliling = min(data_keliling)\n",
+    "max_keliling = max(data_keliling)\n",
+    "# mid_keliling = get_average(min_keliling,max_keliling) #42.0\n",
+    "mid_keliling =np.median(data_keliling)\n",
+    "print(mid_keliling)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "d51fab67",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_ukuran_batang = pd.DataFrame(data_durian)\n",
+    "data_ukuran_batang = data_ukuran_batang['Ukuran_batang'].tolist()\n",
+    "_data_ukuran_batang = data_ukuran_batang\n",
+    "min_ukuran_batang = min(data_ukuran_batang)\n",
+    "max_ukuran_batang = max(data_ukuran_batang)\n",
+    "# mid_ukuran_batang =   get_average(min_ukuran_batang,max_ukuran_batang) #4.0\n",
+    "mid_ukuran_batang =  np.median(data_ukuran_batang) #4.0\n",
+    "print(mid_ukuran_batang)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "54b442e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_jarak_duri = pd.DataFrame(data_durian)\n",
+    "data_jarak_duri = data_jarak_duri['Jarak_duri'].tolist()\n",
+    "_data_jarak_duri = data_jarak_duri\n",
+    "# print(data_jarak_duri)\n",
+    "min_jarak_duri = min(data_jarak_duri)\n",
+    "max_jarak_duri = max(data_jarak_duri)\n",
+    "mid_jarak_duri =  get_average(min_jarak_duri,max_jarak_duri)#1.0\n",
+    "# mid_jarak_duri =  np.median(data_jarak_duri)\n",
+    "print(max_jarak_duri)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4ac8e31",
+   "metadata": {},
+   "source": [
+    "### import librart skfuzzy & matplotlib untuk graph fuzzy\n",
+    "### fungsi menampilkan fuzzy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "4d525445",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import skfuzzy as fuzz\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def RangeSubjektif(_low, _high, _step):\n",
+    "    subjektif = np.arange(_low, _high , _step)\n",
+    "    return subjektif\n",
+    "\n",
+    "def FuzzyShow(_rule, _range_subjektif, _title):\n",
+    "    lo = fuzz.trapmf(_range_subjektif, _rule[0])\n",
+    "    hi = fuzz.trapmf(_range_subjektif, _rule[1])\n",
+    "    \n",
+    "    fig,ax = plt.subplots(nrows=1, figsize=(7,3))\n",
+    "    ax.plot(_range_subjektif, lo, 'g' , linewidth = 1.5 , label= \"Mentah\")\n",
+    "    ax.plot(_range_subjektif, hi, 'r' , linewidth = 1.5 , label= \"Masak\")\n",
+    "    \n",
+    "    ax.set_title(_title)\n",
+    "    ax.legend()\n",
+    "    \n",
+    "    ax.spines['top'].set_visible(False)\n",
+    "    ax.spines['right'].set_visible(False)\n",
+    "    ax.get_xaxis().tick_bottom()\n",
+    "    ax.get_yaxis().tick_left()\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "    \n",
+    "    return lo, hi\n",
+    "\n",
+    "def FuzzyShow1(_rule, _range_subjektif, _title):\n",
+    "    lo = fuzz.trapmf(_range_subjektif, _rule[0])\n",
+    "    hi = fuzz.trapmf(_range_subjektif, _rule[1])\n",
+    "    \n",
+    "    fig,ax = plt.subplots(nrows=1, figsize=(10,3))\n",
+    "    ax.plot(_range_subjektif, lo, 'r' , linewidth = 1.5 , label= \"Masak\")\n",
+    "    ax.plot(_range_subjektif, hi, 'g' , linewidth = 1.5 , label= \"Mentah\")\n",
+    "    \n",
+    "    ax.set_title(_title)\n",
+    "    ax.legend()\n",
+    "    \n",
+    "    ax.spines['top'].set_visible(False)\n",
+    "    ax.spines['right'].set_visible(False)\n",
+    "    ax.get_xaxis().tick_bottom()\n",
+    "    ax.get_yaxis().tick_left()\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "    \n",
+    "    return lo, hi\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e48d1f4",
+   "metadata": {},
+   "source": [
+    "### fuzzy untuk field usia"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "aeab6802",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_usia = RangeSubjektif(min_usia , max_usia , 1)\n",
+    "r_usia = np.array([\n",
+    "    [min_usia,min_usia,mid_usia,mid_usia],\n",
+    "    [mid_usia,mid_usia,max_usia,max_usia]\n",
+    "])\n",
+    "\n",
+    "lo_usia , hi_usia = FuzzyShow(r_usia , x_usia, 'Umur (minggu)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "1f123edb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_berat = RangeSubjektif(min_berat , max_berat , 1)\n",
+    "r_berat = np.array([\n",
+    "    [min_berat,min_berat,mid_berat,mid_berat],\n",
+    "    [mid_berat,mid_berat,max_berat,max_berat]\n",
+    "])\n",
+    "\n",
+    "lo_berat , hi_berat = FuzzyShow1(r_berat , x_berat, 'Berat (kg)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "84aaee1a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_keliling = RangeSubjektif(min_keliling , max_keliling , 1)\n",
+    "r_keliling = np.array([\n",
+    "    [min_keliling,min_keliling,mid_keliling,mid_keliling],\n",
+    "    [mid_keliling,mid_keliling,max_keliling,max_keliling]\n",
+    "])\n",
+    "\n",
+    "lo_keliling , hi_keliling = FuzzyShow(r_keliling , x_keliling, 'Keliling (cm)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "deeec0dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_ukuran_batang = RangeSubjektif(min_ukuran_batang , max_ukuran_batang , 1)\n",
+    "r_ukuran_batang = np.array([\n",
+    "    [min_ukuran_batang,min_ukuran_batang,mid_ukuran_batang,mid_ukuran_batang],\n",
+    "    [mid_ukuran_batang,mid_ukuran_batang,max_ukuran_batang,max_ukuran_batang]\n",
+    "])\n",
+    "\n",
+    "lo_ukuran_batang , hi_ukuran_batang = FuzzyShow(r_ukuran_batang , x_ukuran_batang, 'Keliling (cm)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "d3c1bed7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_jarak_duri = RangeSubjektif(min_jarak_duri , max_jarak_duri , 1)\n",
+    "r_jarak_duri = np.array([\n",
+    "    [min_jarak_duri,min_jarak_duri,mid_jarak_duri,mid_jarak_duri],\n",
+    "    [mid_jarak_duri,mid_jarak_duri,max_jarak_duri,max_jarak_duri]\n",
+    "])\n",
+    "\n",
+    "lo_jarak_duri , hi_jarak_duri = FuzzyShow(r_jarak_duri , x_jarak_duri, 'Jarak Duri (mm)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "879f64ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.0, 1.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "def FungsiKeanggotaan(_range, _min , _hi, _nilai):\n",
+    "    mini = fuzz.interp_membership(_range,_min,_nilai)\n",
+    "    hi = fuzz.interp_membership(_range,_hi,_nilai)\n",
+    "    return mini , hi\n",
+    "    \n",
+    "i = 0\n",
+    "for usia in _data_usia:\n",
+    "    ini_dia = FungsiKeanggotaan(x_usia,lo_usia,hi_usia,usia)\n",
+    "    print(ini_dia)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "d6c3a52d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 0.0)\n",
+      "(1.0, 0.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = 0\n",
+    "for berat in _data_berat:\n",
+    "    ini_dia = FungsiKeanggotaan(x_berat,hi_berat,lo_berat,berat)\n",
+    "    print(ini_dia)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "bd724cff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.0, 1.0)\n",
+      "(1.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(0.0, 1.0)\n",
+      "(1.0, 0.5)\n",
+      "(1.0, 1.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.0, 1.0)\n",
+      "(1.0, 1.0)\n",
+      "(1.0, 1.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = 0\n",
+    "for keliling in _data_keliling:\n",
+    "    ini_dia = FungsiKeanggotaan(x_keliling,lo_keliling,hi_keliling,keliling)\n",
+    "    print(ini_dia)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "0370f3ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.0, 1.0)\n",
+      "(0.5, 0.5)\n",
+      "(1.0, 0.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.2999999999999998, 0.7000000000000002)\n",
+      "(0.0, 1.0)\n",
+      "(1.0, 0.0)\n",
+      "(0.5, 0.5)\n",
+      "(0.0, 1.0)\n",
+      "(0.2999999999999998, 0.7000000000000002)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(0.6000000000000001, 0.3999999999999999)\n",
+      "(0.7000000000000002, 0.2999999999999998)\n",
+      "(0.5, 0.5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = 0\n",
+    "for ukuran_batang in _data_ukuran_batang:\n",
+    "    ini_dia = FungsiKeanggotaan(x_ukuran_batang,lo_ukuran_batang,hi_ukuran_batang,ukuran_batang)\n",
+    "    print(ini_dia)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "5e3c20cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.0, 0.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.0, 0.0)\n",
+      "(0.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(1.0, 0.0)\n",
+      "(0.5, 0.5)\n",
+      "(1.0, 0.0)\n",
+      "(0.0, 1.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = 0\n",
+    "for jarak_duri in _data_jarak_duri:\n",
+    "    ini_dia = FungsiKeanggotaan(x_jarak_duri,lo_jarak_duri,hi_jarak_duri,jarak_duri)\n",
+    "    print(ini_dia)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "aa25537e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h3></h3>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>No</th>\n",
+       "      <th>Rule</th>\n",
+       "      <th>Usia</th>\n",
+       "      <th>Berat</th>\n",
+       "      <th>Keliling</th>\n",
+       "      <th>Ukuran Batang</th>\n",
+       "      <th>Jarak Duri</th>\n",
+       "      <th>Keterangan</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>R1</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>R2</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>R3</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>R4</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>R5</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>R6</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>R7</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>R8</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>R9</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>R10</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>R11</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td>R12</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>13</td>\n",
+       "      <td>R13</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>14</td>\n",
+       "      <td>R14</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>15</td>\n",
+       "      <td>R15</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>16</td>\n",
+       "      <td>R16</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>17</td>\n",
+       "      <td>R17</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>18</td>\n",
+       "      <td>R18</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>19</td>\n",
+       "      <td>R19</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>20</td>\n",
+       "      <td>R20</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>21</td>\n",
+       "      <td>R21</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>22</td>\n",
+       "      <td>R22</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>23</td>\n",
+       "      <td>R23</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>24</td>\n",
+       "      <td>R24</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>25</td>\n",
+       "      <td>R25</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Masak</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>26</td>\n",
+       "      <td>R26</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>27</td>\n",
+       "      <td>R27</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>28</td>\n",
+       "      <td>R28</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>29</td>\n",
+       "      <td>R29</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>30</td>\n",
+       "      <td>R30</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>31</td>\n",
+       "      <td>R31</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Tinggi</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>32</td>\n",
+       "      <td>R32</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Rendah</td>\n",
+       "      <td>Mentah</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# data_rule = [\n",
+    "#     {'No': '1', 'Rule': 'R1', 'Usia': 'Tinggi','Berat':'Tinggi','Keliling':'Tinggi','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '2', 'Rule': 'R2', 'Usia': 'Tinggi','Berat':'Tinggi','Keliling':'Tinggi','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '3', 'Rule': 'R3', 'Usia': 'Tinggi','Berat':'Tinggi','Keliling':'Tinggi','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '4', 'Rule': 'R4', 'Usia': 'Tinggi','Berat':'Tinggi','Keliling':'Tinggi','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '5', 'Rule': 'R5', 'Usia': 'Tinggi','Berat':'Tinggi','Keliling':'Rendah','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '6', 'Rule': 'R6', 'Usia': 'Tinggi','Berat':'Tinggi','Keliling':'Rendah','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '7', 'Rule': 'R7', 'Usia': 'Tinggi','Berat':'Tinggi','Keliling':'Rendah','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '8', 'Rule': 'R8', 'Usia': 'Tinggi','Berat':'Tinggi','Keliling':'Rendah','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '9', 'Rule': 'R9', 'Usia': 'Tinggi','Berat':'Rendah','Keliling':'Tinggi','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '10', 'Rule': 'R10', 'Usia': 'Tinggi','Berat':'Rendah','Keliling':'Tinggi','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '11', 'Rule': 'R11', 'Usia': 'Tinggi','Berat':'Rendah','Keliling':'Tinggi','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '12', 'Rule': 'R12', 'Usia': 'Tinggi','Berat':'Rendah','Keliling':'Tinggi','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '13', 'Rule': 'R13', 'Usia': 'Tinggi','Berat':'Rendah','Keliling':'Rendah','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '14', 'Rule': 'R14', 'Usia': 'Tinggi','Berat':'Rendah','Keliling':'Rendah','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '15', 'Rule': 'R15', 'Usia': 'Tinggi','Berat':'Rendah','Keliling':'Rendah','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '16', 'Rule': 'R16', 'Usia': 'Tinggi','Berat':'Rendah','Keliling':'Rendah','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '17', 'Rule': 'R17', 'Usia': 'Rendah','Berat':'Tinggi','Keliling':'Tinggi','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '18', 'Rule': 'R18', 'Usia': 'Rendah','Berat':'Tinggi','Keliling':'Tinggi','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '19', 'Rule': 'R19', 'Usia': 'Rendah','Berat':'Tinggi','Keliling':'Tinggi','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '20', 'Rule': 'R20', 'Usia': 'Rendah','Berat':'Tinggi','Keliling':'Tinggi','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '21', 'Rule': 'R21', 'Usia': 'Rendah','Berat':'Tinggi','Keliling':'Rendah','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '22', 'Rule': 'R22', 'Usia': 'Rendah','Berat':'Tinggi','Keliling':'Rendah','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '23', 'Rule': 'R23', 'Usia': 'Rendah','Berat':'Tinggi','Keliling':'Rendah','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '24', 'Rule': 'R24', 'Usia': 'Rendah','Berat':'Tinggi','Keliling':'Rendah','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '25', 'Rule': 'R25', 'Usia': 'Rendah','Berat':'Rendah','Keliling':'Tinggi','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Masak'},\n",
+    "#     {'No': '26', 'Rule': 'R26', 'Usia': 'Rendah','Berat':'Rendah','Keliling':'Tinggi','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '27', 'Rule': 'R27', 'Usia': 'Rendah','Berat':'Rendah','Keliling':'Tinggi','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '28', 'Rule': 'R28', 'Usia': 'Rendah','Berat':'Rendah','Keliling':'Tinggi','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '29', 'Rule': 'R29', 'Usia': 'Rendah','Berat':'Rendah','Keliling':'Rendah','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '30', 'Rule': 'R30', 'Usia': 'Rendah','Berat':'Rendah','Keliling':'Rendah','Ukuran Batang':'Tinggi',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '31', 'Rule': 'R31', 'Usia': 'Rendah','Berat':'Rendah','Keliling':'Rendah','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Tinggi', 'Keterangan' : 'Mentah'},\n",
+    "#     {'No': '32', 'Rule': 'R32', 'Usia': 'Rendah','Berat':'Rendah','Keliling':'Rendah','Ukuran Batang':'Rendah',\n",
+    "#      'Jarak Duri' : 'Rendah', 'Keterangan' : 'Mentah'},    \n",
+    "# ] \n",
+    "\n",
+    "# Creates DataFrame. \n",
+    "# df = pd.DataFrame(data_rule) \n",
+    "# df.to_excel(\"output.xlsx\")\n",
+    "df = pd.read_csv(\"dataset/rule.csv\")\n",
+    "    \n",
+    "# Print the data \n",
+    "# df\n",
+    "from IPython.display import display, HTML\n",
+    "display(HTML(\"<h3></h3>\"))\n",
+    "display(HTML(df.to_html(index=False)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "0cc2f82f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('R', 'R', 'R', 'R', 'R')\n",
+      "('R', 'R', 'R', 'R', 'T')\n",
+      "('R', 'R', 'R', 'T', 'R')\n",
+      "('R', 'R', 'R', 'T', 'T')\n",
+      "('R', 'R', 'T', 'R', 'R')\n",
+      "('R', 'R', 'T', 'R', 'T')\n",
+      "('R', 'R', 'T', 'T', 'R')\n",
+      "('R', 'R', 'T', 'T', 'T')\n",
+      "('R', 'T', 'R', 'R', 'R')\n",
+      "('R', 'T', 'R', 'R', 'T')\n",
+      "('R', 'T', 'R', 'T', 'R')\n",
+      "('R', 'T', 'R', 'T', 'T')\n",
+      "('R', 'T', 'T', 'R', 'R')\n",
+      "('R', 'T', 'T', 'R', 'T')\n",
+      "('R', 'T', 'T', 'T', 'R')\n",
+      "('R', 'T', 'T', 'T', 'T')\n",
+      "('T', 'R', 'R', 'R', 'R')\n",
+      "('T', 'R', 'R', 'R', 'T')\n",
+      "('T', 'R', 'R', 'T', 'R')\n",
+      "('T', 'R', 'R', 'T', 'T')\n",
+      "('T', 'R', 'T', 'R', 'R')\n",
+      "('T', 'R', 'T', 'R', 'T')\n",
+      "('T', 'R', 'T', 'T', 'R')\n",
+      "('T', 'R', 'T', 'T', 'T')\n",
+      "('T', 'T', 'R', 'R', 'R')\n",
+      "('T', 'T', 'R', 'R', 'T')\n",
+      "('T', 'T', 'R', 'T', 'R')\n",
+      "('T', 'T', 'R', 'T', 'T')\n",
+      "('T', 'T', 'T', 'R', 'R')\n",
+      "('T', 'T', 'T', 'R', 'T')\n",
+      "('T', 'T', 'T', 'T', 'R')\n",
+      "('T', 'T', 'T', 'T', 'T')\n",
+      "32\n"
+     ]
+    }
+   ],
+   "source": [
+    "import itertools as it\n",
+    "\n",
+    "\n",
+    "my_dict={'Usia':['R','T'],'Berat':['R','T'],'Keliling':['R','T'],'Ukuran Batang':['R','T'],'Jarak Duri':['R','T']}\n",
+    "allNames = sorted(my_dict)\n",
+    "combinations = it.product(*(my_dict[Name] for Name in allNames))\n",
+    "counter = 0\n",
+    "for combi in combinations:\n",
+    "    print(combi)\n",
+    "    counter = counter + 1\n",
+    "print(counter)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/requirements.txt b/requirements.txt
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