1861 lines
129 KiB
Plaintext
1861 lines
129 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0fedd17f",
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"metadata": {},
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"source": [
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"# Metode Fuzzy\n",
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"### import library numpy(untuk mengolah list dan angka) dan panda(untuk membaca dataset)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 371,
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"id": "721c3e36",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import asyncio"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1181afa6",
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"metadata": {},
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"source": [
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"### read dataset kemudian menampilkan dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 372,
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"id": "e7835698",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Usia</th>\n",
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" <th>Berat</th>\n",
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" <th>Keliling</th>\n",
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" <th>Ukuran_batang</th>\n",
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" <th>Jarak_duri</th>\n",
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" <th>Keterangan</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>14</td>\n",
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" <td>753</td>\n",
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" <td>44.0</td>\n",
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" <td>4.5</td>\n",
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" <td>10.9</td>\n",
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" <td>Matang</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>18</td>\n",
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" <td>505</td>\n",
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" <td>42.0</td>\n",
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" <td>4.0</td>\n",
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" <td>10.0</td>\n",
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" <td>Matang</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>17</td>\n",
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" <td>750</td>\n",
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" <td>43.0</td>\n",
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" <td>3.0</td>\n",
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" <td>10.0</td>\n",
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" <td>Matang</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>15</td>\n",
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" <td>755</td>\n",
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" <td>44.0</td>\n",
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" <td>5.0</td>\n",
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" <td>10.0</td>\n",
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" <td>Matang</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>14</td>\n",
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" <td>815</td>\n",
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" <td>45.0</td>\n",
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" <td>4.2</td>\n",
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" <td>10.0</td>\n",
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" <td>Matang</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>17</td>\n",
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" <td>660</td>\n",
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" <td>41.5</td>\n",
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" <td>4.5</td>\n",
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" <td>10.0</td>\n",
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" <td>Matang</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>15</td>\n",
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" <td>700</td>\n",
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" <td>42.0</td>\n",
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" <td>3.0</td>\n",
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" <td>10.0</td>\n",
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" <td>Matang</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>16</td>\n",
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" <td>820</td>\n",
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" <td>46.0</td>\n",
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" <td>4.0</td>\n",
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" <td>10.0</td>\n",
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" <td>Matang</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>12</td>\n",
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" <td>850</td>\n",
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" <td>44.0</td>\n",
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" <td>4.5</td>\n",
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" <td>10.0</td>\n",
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" <td>Mentah</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>11</td>\n",
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" <td>830</td>\n",
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" <td>42.0</td>\n",
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" <td>4.2</td>\n",
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" <td>10.0</td>\n",
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" <td>Mentah</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>10</th>\n",
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" <td>10</td>\n",
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" <td>900</td>\n",
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" <td>42.0</td>\n",
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" <td>3.0</td>\n",
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" <td>8.5</td>\n",
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" <td>Mentah</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>11</th>\n",
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" <td>9</td>\n",
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" <td>900</td>\n",
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" <td>30.0</td>\n",
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" <td>2.5</td>\n",
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" <td>7.5</td>\n",
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" <td>Mentah</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>12</th>\n",
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" <td>9</td>\n",
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" <td>850</td>\n",
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" <td>35.0</td>\n",
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" <td>3.0</td>\n",
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" <td>7.5</td>\n",
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" <td>Mentah</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>13</th>\n",
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" <td>13</td>\n",
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" <td>800</td>\n",
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" <td>40.0</td>\n",
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" <td>3.9</td>\n",
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" <td>9.0</td>\n",
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" <td>Mentah</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>14</th>\n",
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" <td>11</td>\n",
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" <td>950</td>\n",
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" <td>41.0</td>\n",
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" <td>3.8</td>\n",
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" <td>8.5</td>\n",
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" <td>Mentah</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>15</th>\n",
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" <td>13</td>\n",
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" <td>860</td>\n",
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" <td>40.0</td>\n",
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" <td>4.0</td>\n",
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" <td>9.5</td>\n",
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" <td>Mentah</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Usia Berat Keliling Ukuran_batang Jarak_duri Keterangan\n",
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"0 14 753 44.0 4.5 10.9 Matang\n",
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"1 18 505 42.0 4.0 10.0 Matang\n",
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"2 17 750 43.0 3.0 10.0 Matang\n",
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"3 15 755 44.0 5.0 10.0 Matang\n",
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"4 14 815 45.0 4.2 10.0 Matang\n",
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"5 17 660 41.5 4.5 10.0 Matang\n",
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"6 15 700 42.0 3.0 10.0 Matang\n",
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"7 16 820 46.0 4.0 10.0 Matang\n",
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"8 12 850 44.0 4.5 10.0 Mentah\n",
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"9 11 830 42.0 4.2 10.0 Mentah\n",
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"10 10 900 42.0 3.0 8.5 Mentah\n",
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"11 9 900 30.0 2.5 7.5 Mentah\n",
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"12 9 850 35.0 3.0 7.5 Mentah\n",
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"13 13 800 40.0 3.9 9.0 Mentah\n",
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"14 11 950 41.0 3.8 8.5 Mentah\n",
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"15 13 860 40.0 4.0 9.5 Mentah"
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]
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},
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"execution_count": 372,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data_durian = pd.read_csv(\"dataset/dataset.csv\")\n",
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"data_durian"
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]
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},
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{
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"cell_type": "markdown",
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"id": "990c4cac",
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"metadata": {},
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"source": [
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"### menghitung field usia untuk mencari nilai semesta pembicaraan\n",
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"### berdasarkan minimal nilai dan maksimal nilai\n",
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"### mid_usia sebagai domain untuk fuzzy antara output masak @ mentah"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 373,
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"id": "f0789ec2",
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_average(min,max) :\n",
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" return (min + max) / 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 374,
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"id": "f3f362e1",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[14, 18, 17, 15, 14, 17, 15, 16, 12, 11, 10, 9, 9, 13, 11, 13]\n",
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"13.5\n"
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]
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}
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],
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"source": [
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"data_usia = pd.DataFrame(data_durian)\n",
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"data_usia = data_usia['Usia'].tolist()\n",
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"_data_usia = data_usia\n",
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"print(_data_usia)\n",
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"min_usia = min(data_usia)\n",
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"max_usia = max(data_usia)\n",
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"# mid_usia = get_average(min_usia,max_usia) #13.5\n",
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"mid_usia =np.median(data_usia)\n",
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"print(mid_usia)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 375,
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"id": "9290d188",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"817.5\n"
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]
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}
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],
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"source": [
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"data_berat = pd.DataFrame(data_durian)\n",
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"data_berat = data_berat['Berat'].tolist()\n",
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"_data_berat = data_berat\n",
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"min_berat = min(data_berat)\n",
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"max_berat = max(data_berat)\n",
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"# mid_berat = get_average(min_berat,max_berat) #817.5\n",
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"mid_berat =np.median(data_berat)\n",
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"print(mid_berat)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 376,
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"id": "16e441bc",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"42.0\n"
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]
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}
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],
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"source": [
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"data_keliling = pd.DataFrame(data_durian)\n",
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"data_keliling = data_keliling['Keliling'].tolist()\n",
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"_data_keliling = data_keliling\n",
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"min_keliling = min(data_keliling)\n",
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"max_keliling = max(data_keliling)\n",
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"# mid_keliling = get_average(min_keliling,max_keliling) #42.0\n",
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"mid_keliling =np.median(data_keliling)\n",
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"print(mid_keliling)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 377,
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"id": "d51fab67",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"4.0\n"
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]
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}
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],
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"source": [
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"data_ukuran_batang = pd.DataFrame(data_durian)\n",
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"data_ukuran_batang = data_ukuran_batang['Ukuran_batang'].tolist()\n",
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"_data_ukuran_batang = data_ukuran_batang\n",
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"min_ukuran_batang = min(data_ukuran_batang)\n",
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"max_ukuran_batang = max(data_ukuran_batang)\n",
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"# mid_ukuran_batang = get_average(min_ukuran_batang,max_ukuran_batang) #4.0\n",
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"mid_ukuran_batang = np.median(data_ukuran_batang) #4.0\n",
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"print(mid_ukuran_batang)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 378,
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"id": "54b442e1",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"10.9\n"
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]
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}
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],
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"source": [
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"data_jarak_duri = pd.DataFrame(data_durian)\n",
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"data_jarak_duri = data_jarak_duri['Jarak_duri'].tolist()\n",
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"_data_jarak_duri = data_jarak_duri\n",
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"# print(data_jarak_duri)\n",
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"min_jarak_duri = min(data_jarak_duri)\n",
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"max_jarak_duri = max(data_jarak_duri)\n",
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"mid_jarak_duri = get_average(min_jarak_duri,max_jarak_duri)#1.0\n",
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"# mid_jarak_duri = np.median(data_jarak_duri)\n",
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"print(max_jarak_duri)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 379,
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"id": "53f0a713",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<h3></h3>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th>Fungsi</th>\n",
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" <th>Nama Variabel</th>\n",
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" <th>Semesta Pembicaraan</th>\n",
|
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" <th>Himpunan Fuzzy</th>\n",
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" <th>Domain</th>\n",
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" </tr>\n",
|
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" </thead>\n",
|
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" <tbody>\n",
|
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" <tr>\n",
|
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" <td>Input</td>\n",
|
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" <td>Usia</td>\n",
|
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" <td>[ 9 , 18 ]</td>\n",
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" <td>Rendah</td>\n",
|
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" <td>[ 9 , 14.0 ]</td>\n",
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" </tr>\n",
|
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" <tr>\n",
|
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" <td></td>\n",
|
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" <td></td>\n",
|
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" <td></td>\n",
|
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" <td>Tinggi</td>\n",
|
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" <td>[ 13.0 , 18 ]</td>\n",
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" </tr>\n",
|
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" <tr>\n",
|
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" <td></td>\n",
|
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" <td>Berat</td>\n",
|
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" <td>[ 505 , 950 ]</td>\n",
|
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" <td>Rendah</td>\n",
|
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" <td>[ 505 , 827.5 ]</td>\n",
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" </tr>\n",
|
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" <tr>\n",
|
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" <td></td>\n",
|
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" <td></td>\n",
|
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" <td></td>\n",
|
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" <td>Tinggi</td>\n",
|
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" <td>[ 807.5 , 950 ]</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <td></td>\n",
|
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" <td>Keliling</td>\n",
|
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" <td>[ 30.0 , 46.0 ]</td>\n",
|
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" <td>Rendah</td>\n",
|
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" <td>[ 30.0 , 43.0 ]</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <td></td>\n",
|
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" <td></td>\n",
|
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" <td></td>\n",
|
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" <td>Tinggi</td>\n",
|
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" <td>[ 41.0 , 46.0 ]</td>\n",
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" </tr>\n",
|
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" <tr>\n",
|
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" <td></td>\n",
|
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" <td>Ukurang Batang</td>\n",
|
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" <td>[ 2.5 , 5.0 ]</td>\n",
|
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" <td>Rendah</td>\n",
|
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" <td>[ 2.5 , 4.5 ]</td>\n",
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" </tr>\n",
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" <tr>\n",
|
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" <td></td>\n",
|
|
" <td></td>\n",
|
|
" <td></td>\n",
|
|
" <td>Tinggi</td>\n",
|
|
" <td>[ 3.5 , 5.0 ]</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td></td>\n",
|
|
" <td>Jarak Duri</td>\n",
|
|
" <td>[ 7.5 , 10.9 ]</td>\n",
|
|
" <td>Rendah</td>\n",
|
|
" <td>[ 7.5 , 9.95 ]</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td></td>\n",
|
|
" <td></td>\n",
|
|
" <td></td>\n",
|
|
" <td>Tinggi</td>\n",
|
|
" <td>[ 8.95 , 10.9 ]</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>Output</td>\n",
|
|
" <td>Keterangan</td>\n",
|
|
" <td>[ 0 , 1 ]</td>\n",
|
|
" <td>Rendah (Mentah)</td>\n",
|
|
" <td>[ 0 , 0.5 ]</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td></td>\n",
|
|
" <td></td>\n",
|
|
" <td></td>\n",
|
|
" <td>Tinggi (Masak)</td>\n",
|
|
" <td>[ 0.5 , 1 ]</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.HTML object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"himpunan_fuzzy = pd.read_csv(\"dataset/himpunan_fuzzy.csv\")\n",
|
|
"\n",
|
|
"# Fuzzykasi Usia\n",
|
|
"himpunan_fuzzy.loc[0, 'Semesta Pembicaraan'] = f\"[ {min_usia} , {max_usia} ]\" # Semesta pembicaraan (Rendah) Usia\n",
|
|
"himpunan_fuzzy.loc[1, 'Semesta Pembicaraan'] = f\"[ {min_usia} , {max_usia} ]\" # Semesta pembicaraan (Tinggi) Usia\n",
|
|
"himpunan_fuzzy.loc[0, 'Domain'] = f\"[ {min_usia} , {mid_usia + 0.5} ]\" # Domain (Rendah) Usia\n",
|
|
"himpunan_fuzzy.loc[1, 'Domain'] = f\"[ {mid_usia - 0.5} , {max_usia} ]\" # Domain pembicaraan (Tinggi) Usia\n",
|
|
"\n",
|
|
"# Fuzzykasi Berat\n",
|
|
"himpunan_fuzzy.loc[2, 'Semesta Pembicaraan'] = f\"[ {min_berat} , {max_berat} ]\" # Semesta pembicaraan (Rendah) Berat\n",
|
|
"himpunan_fuzzy.loc[3, 'Semesta Pembicaraan'] = f\"[ {min_berat} , {max_berat} ]\" # Semesta pembicaraan (Tinggi) Berat\n",
|
|
"himpunan_fuzzy.loc[2, 'Domain'] = f\"[ {min_berat} , {mid_berat + 10} ]\" # Domain (Rendah) Berat\n",
|
|
"himpunan_fuzzy.loc[3, 'Domain'] = f\"[ {mid_berat - 10} , {max_berat} ]\" # Domain pembicaraan (Tinggi) Berat\n",
|
|
"\n",
|
|
"# Fuzzykasi Keliling\n",
|
|
"himpunan_fuzzy.loc[4, 'Semesta Pembicaraan'] = f\"[ {min_keliling} , {max_keliling} ]\" # Semesta pembicaraan (Rendah) Keliling\n",
|
|
"himpunan_fuzzy.loc[5, 'Semesta Pembicaraan'] = f\"[ {min_keliling} , {max_keliling} ]\" # Semesta pembicaraan (Tinggi) Keliling\n",
|
|
"himpunan_fuzzy.loc[4, 'Domain'] = f\"[ {min_keliling} , {mid_keliling + 1} ]\" # Domain (Rendah) Keliling\n",
|
|
"himpunan_fuzzy.loc[5, 'Domain'] = f\"[ {mid_keliling - 1} , {max_keliling} ]\" # Domain pembicaraan (Tinggi) Keliling\n",
|
|
"\n",
|
|
"# Fuzzykasi Keliling\n",
|
|
"himpunan_fuzzy.loc[4, 'Semesta Pembicaraan'] = f\"[ {min_keliling} , {max_keliling} ]\" # Semesta pembicaraan (Rendah) Keliling\n",
|
|
"himpunan_fuzzy.loc[5, 'Semesta Pembicaraan'] = f\"[ {min_keliling} , {max_keliling} ]\" # Semesta pembicaraan (Tinggi) Keliling\n",
|
|
"himpunan_fuzzy.loc[4, 'Domain'] = f\"[ {min_keliling} , {mid_keliling + 1} ]\" # Domain (Rendah) Keliling\n",
|
|
"himpunan_fuzzy.loc[5, 'Domain'] = f\"[ {mid_keliling - 1} , {max_keliling} ]\" # Domain pembicaraan (Tinggi) Keliling\n",
|
|
"\n",
|
|
"# Fuzzykasi Ukuran Batang\n",
|
|
"himpunan_fuzzy.loc[6, 'Semesta Pembicaraan'] = f\"[ {min_ukuran_batang} , {max_ukuran_batang} ]\" # Semesta pembicaraan (Rendah) Ukuran Batang\n",
|
|
"himpunan_fuzzy.loc[7, 'Semesta Pembicaraan'] = f\"[ {min_ukuran_batang} , {max_ukuran_batang} ]\" # Semesta pembicaraan (Tinggi) Ukuran Batang\n",
|
|
"himpunan_fuzzy.loc[6, 'Domain'] = f\"[ {min_ukuran_batang} , {mid_ukuran_batang + 0.5} ]\" # Domain (Rendah) Ukuran Batang\n",
|
|
"himpunan_fuzzy.loc[7, 'Domain'] = f\"[ {mid_ukuran_batang - 0.5} , {max_ukuran_batang} ]\" # Domain pembicaraan (Tinggi) Ukuran Batang\n",
|
|
"\n",
|
|
"# Fuzzykasi Jarak Duri\n",
|
|
"himpunan_fuzzy.loc[8, 'Semesta Pembicaraan'] = f\"[ {min_jarak_duri} , {max_jarak_duri} ]\" # Semesta pembicaraan (Rendah) Jarak Duri\n",
|
|
"himpunan_fuzzy.loc[9, 'Semesta Pembicaraan'] = f\"[ {min_jarak_duri} , {max_jarak_duri} ]\" # Semesta pembicaraan (Tinggi) Jarak Duri\n",
|
|
"himpunan_fuzzy.loc[8, 'Domain'] = f\"[ {min_jarak_duri} , {mid_jarak_duri + 0.75} ]\" # Domain (Rendah) Jarak Duri\n",
|
|
"himpunan_fuzzy.loc[9, 'Domain'] = f\"[ {mid_jarak_duri - 0.25} , {max_jarak_duri} ]\" # Domain pembicaraan (Tinggi) Jarak Duri\n",
|
|
"\n",
|
|
"# Fuzzykasi Keterangan\n",
|
|
"himpunan_fuzzy.loc[10, 'Semesta Pembicaraan'] = f\"[ 0 , 1 ]\" # Semesta pembicaraan (Rendah) Keterangan\n",
|
|
"himpunan_fuzzy.loc[11, 'Semesta Pembicaraan'] = f\"[ 0 , 1 ]\" # Semesta pembicaraan (Tinggi) Keterangan\n",
|
|
"himpunan_fuzzy.loc[10, 'Domain'] = f\"[ 0 , 0.5 ]\" # Domain (Rendah) Keterangan\n",
|
|
"himpunan_fuzzy.loc[11, 'Domain'] = f\"[ 0.5 , 1 ]\" # Domain pembicaraan (Tinggi) Keterangan\n",
|
|
"\n",
|
|
"himpunan_fuzzy.loc[himpunan_fuzzy.duplicated(subset=['Fungsi']),['Fungsi']]=''\n",
|
|
"himpunan_fuzzy.loc[himpunan_fuzzy.duplicated(subset=['Nama Variabel']),['Nama Variabel']]=''\n",
|
|
"himpunan_fuzzy.loc[himpunan_fuzzy.duplicated(subset=['Semesta Pembicaraan']),['Semesta Pembicaraan']]=''\n",
|
|
"\n",
|
|
"\n",
|
|
"# himpunan_fuzzy.to_html(index=False)\n",
|
|
"\n",
|
|
"from IPython.display import display, HTML\n",
|
|
"display(HTML(\"<h3></h3>\"))\n",
|
|
"display(HTML(himpunan_fuzzy.to_html(index=False)))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e4ac8e31",
|
|
"metadata": {},
|
|
"source": [
|
|
"### import librart skfuzzy & matplotlib untuk graph fuzzy\n",
|
|
"### fungsi menampilkan fuzzy"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 380,
|
|
"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=(16,3))\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": 381,
|
|
"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": 382,
|
|
"id": "1f123edb",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1152x216 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+10],\n",
|
|
" [mid_berat,mid_berat+10,max_berat,max_berat]\n",
|
|
"])\n",
|
|
"\n",
|
|
"lo_berat , hi_berat = FuzzyShow1(r_berat , x_berat, 'Berat (kg)')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 383,
|
|
"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": 384,
|
|
"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": 385,
|
|
"id": "d3c1bed7",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
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"text/plain": [
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"<Figure size 504x216 with 1 Axes>"
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]
|
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},
|
|
"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": 386,
|
|
"id": "879f64ec",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[(0.0, 1.0), (0.0, 0.0), (0.0, 1.0), (0.0, 1.0), (0.0, 1.0), (0.0, 1.0), (0.0, 1.0), (0.0, 1.0), (1.0, 0.0), (1.0, 0.0), (1.0, 0.0), (1.0, 0.0), (1.0, 0.0), (1.0, 0.0), (1.0, 0.0), (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",
|
|
"anggota_usia = []\n",
|
|
"for usia in _data_usia:\n",
|
|
" ini_dia = FungsiKeanggotaan(x_usia,lo_usia,hi_usia,usia)\n",
|
|
" \n",
|
|
" anggota_usia.append(ini_dia)\n",
|
|
" i = i+1\n",
|
|
"# print(ini_dia) \n",
|
|
"print(anggota_usia)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 387,
|
|
"id": "d6c3a52d",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[(1.0, 0.0), (1.0, 0.0), (1.0, 0.0), (1.0, 0.0), (1.0, 0.0), (1.0, 0.0), (1.0, 0.0), (0.75, 0.25), (0.0, 1.0), (0.0, 1.0), (0.0, 1.0), (0.0, 1.0), (0.0, 1.0), (1.0, 0.0), (0.0, 0.0), (0.0, 1.0)]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"anggota_berat = []\n",
|
|
"for berat in _data_berat:\n",
|
|
" ini_dia = FungsiKeanggotaan(x_berat,lo_berat,hi_berat,berat)\n",
|
|
" anggota_berat.append(ini_dia)\n",
|
|
" \n",
|
|
"print(anggota_berat)\n",
|
|
" "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 445,
|
|
"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",
|
|
"[(0.0, 1.0), (1.0, 1.0), (0.0, 1.0), (0.0, 1.0), (0.0, 1.0), (1.0, 0.5), (1.0, 1.0), (0.0, 0.0), (0.0, 1.0), (1.0, 1.0), (1.0, 1.0), (1.0, 0.0), (1.0, 0.0), (1.0, 0.0), (1.0, 0.0), (1.0, 0.0)]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"anggota_keliling = []\n",
|
|
"for keliling in _data_keliling:\n",
|
|
" ini_dia = FungsiKeanggotaan(x_keliling,lo_keliling,hi_keliling,keliling)\n",
|
|
" anggota_keliling.append(ini_dia)\n",
|
|
" print(ini_dia)\n",
|
|
"print(anggota_keliling) "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 444,
|
|
"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",
|
|
"[(0.0, 1.0), (0.5, 0.5), (1.0, 0.0), (0.0, 0.0), (0.2999999999999998, 0.7000000000000002), (0.0, 1.0), (1.0, 0.0), (0.5, 0.5), (0.0, 1.0), (0.2999999999999998, 0.7000000000000002), (1.0, 0.0), (1.0, 0.0), (1.0, 0.0), (0.6000000000000001, 0.3999999999999999), (0.7000000000000002, 0.2999999999999998), (0.5, 0.5)]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"anggota_ukuran_batang = []\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)\n",
|
|
" anggota_ukuran_batang.append(ini_dia)\n",
|
|
"print(anggota_ukuran_batang)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 443,
|
|
"id": "5e3c20cf",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(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",
|
|
"(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",
|
|
"(0.5, 0.5)\n",
|
|
"(1.0, 0.0)\n",
|
|
"(0.0, 1.0)\n",
|
|
"[(0.0, 0.0), (0.0, 1.0), (0.0, 1.0), (0.0, 1.0), (0.0, 1.0), (0.0, 1.0), (0.0, 1.0), (0.0, 1.0), (0.0, 1.0), (0.0, 1.0), (1.0, 0.0), (1.0, 0.0), (1.0, 0.0), (0.5, 0.5), (1.0, 0.0), (0.0, 1.0)]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"anggota_jarak_duri = []\n",
|
|
"for jarak_duri in _data_jarak_duri:\n",
|
|
" ini_dia = FungsiKeanggotaan(x_jarak_duri,lo_jarak_duri,hi_jarak_duri,jarak_duri)\n",
|
|
" anggota_jarak_duri.append(ini_dia)\n",
|
|
" print(ini_dia)\n",
|
|
"print(anggota_jarak_duri)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 403,
|
|
"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",
|
|
"# df = data_rule\n",
|
|
"\n",
|
|
"# df.to_csv('out.csv')\n",
|
|
"# Creates DataFrame. \n",
|
|
"# df = pd.DataFrame(data_rule) \n",
|
|
"# df.to_csv('out.csv',index=False)\n",
|
|
"# df.to_excel(\"output.xlsx\")\n",
|
|
"df = pd.read_csv(\"dataset/rule.csv\")\n",
|
|
"# vals = df.values\n",
|
|
"rule_length = len(df.index)\n",
|
|
"# list_rule = vals.tolist()\n",
|
|
"list_rule = df\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": 392,
|
|
"id": "0cc2f82f",
|
|
"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>Fungsi</th>\n",
|
|
" <th>Nama Variabel</th>\n",
|
|
" <th>Semesta Pembicaraan</th>\n",
|
|
" <th>Himpunan Fuzzy</th>\n",
|
|
" <th>Domain</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <td>Input</td>\n",
|
|
" <td>Usia</td>\n",
|
|
" <td>[ 9 , 18 ]</td>\n",
|
|
" <td>Rendah</td>\n",
|
|
" <td>[ 9 , 14.0 ]</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td></td>\n",
|
|
" <td></td>\n",
|
|
" <td></td>\n",
|
|
" <td>Tinggi</td>\n",
|
|
" <td>[ 13.0 , 18 ]</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td></td>\n",
|
|
" <td>Berat</td>\n",
|
|
" <td>[ 505 , 950 ]</td>\n",
|
|
" <td>Rendah</td>\n",
|
|
" <td>[ 505 , 827.5 ]</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td></td>\n",
|
|
" <td></td>\n",
|
|
" <td></td>\n",
|
|
" <td>Tinggi</td>\n",
|
|
" <td>[ 807.5 , 950 ]</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td></td>\n",
|
|
" <td>Keliling</td>\n",
|
|
" <td>[ 30.0 , 46.0 ]</td>\n",
|
|
" <td>Rendah</td>\n",
|
|
" <td>[ 30.0 , 43.0 ]</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td></td>\n",
|
|
" <td></td>\n",
|
|
" <td></td>\n",
|
|
" <td>Tinggi</td>\n",
|
|
" <td>[ 41.0 , 46.0 ]</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td></td>\n",
|
|
" <td>Ukurang Batang</td>\n",
|
|
" <td>[ 2.5 , 5.0 ]</td>\n",
|
|
" <td>Rendah</td>\n",
|
|
" <td>[ 2.5 , 4.5 ]</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td></td>\n",
|
|
" <td></td>\n",
|
|
" <td></td>\n",
|
|
" <td>Tinggi</td>\n",
|
|
" <td>[ 3.5 , 5.0 ]</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td></td>\n",
|
|
" <td>Jarak Duri</td>\n",
|
|
" <td>[ 7.5 , 10.9 ]</td>\n",
|
|
" <td>Rendah</td>\n",
|
|
" <td>[ 7.5 , 9.95 ]</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td></td>\n",
|
|
" <td></td>\n",
|
|
" <td></td>\n",
|
|
" <td>Tinggi</td>\n",
|
|
" <td>[ 8.95 , 10.9 ]</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>Output</td>\n",
|
|
" <td>Keterangan</td>\n",
|
|
" <td>[ 0 , 1 ]</td>\n",
|
|
" <td>Rendah (Mentah)</td>\n",
|
|
" <td>[ 0 , 0.5 ]</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td></td>\n",
|
|
" <td></td>\n",
|
|
" <td></td>\n",
|
|
" <td>Tinggi (Masak)</td>\n",
|
|
" <td>[ 0.5 , 1 ]</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.HTML object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"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",
|
|
"from IPython.display import display, HTML\n",
|
|
"display(HTML(\"<h3></h3>\"))\n",
|
|
"display(HTML(himpunan_fuzzy.to_html(index=False)))\n"
|
|
]
|
|
},
|
|
{
|
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"cell_type": "code",
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"execution_count": 455,
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"id": "dc3379a4",
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"metadata": {},
|
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"outputs": [
|
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1\n",
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"0\n",
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"1\n",
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"1\n",
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"1\n",
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"1\n",
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"1\n",
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"1\n",
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"0\n",
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"0\n",
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"0\n",
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"0\n",
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"0\n",
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"0\n"
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]
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},
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{
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"data": {
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"text/html": [
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th>No</th>\n",
|
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" <th>Usia</th>\n",
|
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" <th>Berat</th>\n",
|
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" <th>Keliling</th>\n",
|
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" <th>Ukuran Batang</th>\n",
|
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" <th>Jarak Duri</th>\n",
|
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" <th>Variable Linguistic</th>\n",
|
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" <th>Fuzzy Tsukamoto</th>\n",
|
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" </tr>\n",
|
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" </thead>\n",
|
|
" <tbody>\n",
|
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" <tr>\n",
|
|
" <td>0</td>\n",
|
|
" <td>Tinggi</td>\n",
|
|
" <td>Rendah</td>\n",
|
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" <td>Tinggi</td>\n",
|
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" <td>Tinggi</td>\n",
|
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" <td>Rendah</td>\n",
|
|
" <td>Masak</td>\n",
|
|
" <td>()</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>1</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",
|
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" <td>()</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>2</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",
|
|
" <td>()</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>3</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",
|
|
" <td>()</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>4</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",
|
|
" <td>()</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>5</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",
|
|
" <td>()</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>6</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",
|
|
" <td>()</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>7</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",
|
|
" <td>()</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>8</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",
|
|
" <td>()</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>9</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",
|
|
" <td>()</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>10</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",
|
|
" <td>()</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>11</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",
|
|
" <td>()</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>12</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",
|
|
" <td>()</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>13</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",
|
|
" <td>()</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>14</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",
|
|
" <td>()</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td>15</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",
|
|
" <td>()</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.HTML object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# print(str(list_rule.iloc[[0]]['Usia']))\n",
|
|
"# print(list_rule.at[0,'Usia'])\n",
|
|
"# haha = \"ngam\" if str(list_rule.at[0,'Usia']) == 'Tinggi' else \"tidak\"\n",
|
|
"# print(haha)\n",
|
|
"hitungan_fuzzy_tsukamoto = []\n",
|
|
"# ii = 1\n",
|
|
"for e in range(i):\n",
|
|
" ket_usia = \"Tinggi\" if anggota_usia[e][0] < anggota_usia[e][1] else \"Rendah\"\n",
|
|
" ket_berat = \"Tinggi\" if anggota_berat[e][0] < anggota_berat[e][1] else \"Rendah\" \n",
|
|
" ket_keliling = \"Tinggi\" if anggota_keliling[e][0] <= anggota_keliling[e][1] else \"Rendah\"\n",
|
|
" ket_ukuran_batang = \"Tinggi\" if anggota_ukuran_batang[e][0] <= anggota_ukuran_batang[e][1] else \"Rendah\"\n",
|
|
" ket_jarak_duri = \"Tinggi\" if anggota_jarak_duri[e][0] < anggota_jarak_duri[e][1] else \"Rendah\"\n",
|
|
" \n",
|
|
" data_usia = 1 if anggota_usia[e][0] < anggota_usia[e][1] else 0\n",
|
|
" data_berat = 1 if anggota_berat[e][0] < anggota_berat[e][1] else 0\n",
|
|
" data_keliling = 1 if anggota_keliling[e][0] <= anggota_keliling[e][1] else 0\n",
|
|
" data_ukuran_batang = 1 if anggota_ukuran_batang[e][0] <= anggota_ukuran_batang[e][1] else 0\n",
|
|
" data_jarak_duri = 1 if anggota_jarak_duri[e][0] < anggota_jarak_duri[e][1] else 0\n",
|
|
" \n",
|
|
" print(data_usia)\n",
|
|
" \n",
|
|
" keterangan = None\n",
|
|
" for n in range(rule_length):\n",
|
|
" if str(list_rule.at[n,'Usia']) == ket_usia and str(list_rule.at[n,'Berat']) == ket_berat and str(list_rule.at[n,'Keliling']) == ket_keliling and str(list_rule.at[n,'Ukuran Batang']) == ket_ukuran_batang and str(list_rule.at[n,'Jarak Duri']) == ket_jarak_duri:\n",
|
|
" keterangan = str(list_rule.at[n,'Keterangan'])\n",
|
|
" \n",
|
|
"# print(keterangan)\n",
|
|
" \n",
|
|
"# res = next((sub for sub in test_list if sub['is'] == 7), None)\n",
|
|
" \n",
|
|
" \n",
|
|
" data = {\"No\" : e, \"Usia\" : ket_usia, \"Berat\" : ket_berat, \"Keliling\" : ket_keliling, \n",
|
|
" \"Ukuran Batang\" : ket_ukuran_batang, \"Jarak Duri\" : ket_jarak_duri ,\"Variable Linguistic\" : keterangan,\n",
|
|
" \"Fuzzy Tsukamoto\" : ()\n",
|
|
" }\n",
|
|
" hitungan_fuzzy_tsukamoto.append(data)\n",
|
|
" \n",
|
|
" \n",
|
|
"# ii = ii +1\n",
|
|
"# print(hitungan_fuzzy_tsukamoto)\n",
|
|
"df = pd.DataFrame(hitungan_fuzzy_tsukamoto) \n",
|
|
"display(HTML(df.to_html(index=False)))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "25fc43bc",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"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
|
|
}
|