4612 lines
631 KiB
Plaintext
4612 lines
631 KiB
Plaintext
{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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{
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"cell_type": "code",
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt"
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],
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"metadata": {
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"id": "i4e7z6erwlmV"
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},
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"execution_count": 175,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"pd.set_option('display.max_rows', 100)\n",
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"pd.set_option('display.max_columns', None)\n",
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"pd.set_option('display.width', None)\n",
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"pd.set_option('display.max_colwidth', None)\n",
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"\n",
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"df = pd.read_csv(\"Klasifikasi Tingkat Kemiskinan di Indonesia.csv\", sep=';')\n",
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"\n",
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"df.head(100)\n",
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"\n",
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"\n"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 1000
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},
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"id": "kzMVZKPo0nS5",
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"outputId": "b744630f-2386-4c0f-8736-b3ef50011c4c"
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},
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"execution_count": 219,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" Provinsi Kab/Kota \\\n",
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"0 ACEH Simeulue \n",
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"1 ACEH Aceh Singkil \n",
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"2 ACEH Aceh Selatan \n",
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"3 ACEH Aceh Tenggara \n",
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"4 ACEH Aceh Timur \n",
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"5 ACEH Aceh Tengah \n",
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"6 ACEH Aceh Barat \n",
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"7 ACEH Aceh Besar \n",
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"8 ACEH Pidie \n",
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"9 ACEH Bireuen \n",
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|
"10 ACEH Aceh Utara \n",
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"11 ACEH Aceh Barat Daya \n",
|
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"12 ACEH Gayo Lues \n",
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|
"13 ACEH Aceh Tamiang \n",
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"14 ACEH Nagan Raya \n",
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"15 ACEH Aceh Jaya \n",
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"16 ACEH Bener Meriah \n",
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"17 ACEH Pidie Jaya \n",
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"18 ACEH Kota Banda Aceh \n",
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"19 ACEH Kota Sabang \n",
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"20 ACEH Kota Langsa \n",
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"21 ACEH Kota Lhokseumawe \n",
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"22 ACEH Kota Subulussalam \n",
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"23 SUMATERA UTARA Nias \n",
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"24 SUMATERA UTARA Mandailing Natal \n",
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"25 SUMATERA UTARA Tapanuli Selatan \n",
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"26 SUMATERA UTARA Tapanuli Tengah \n",
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"27 SUMATERA UTARA Tapanuli Utara \n",
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"28 SUMATERA UTARA Toba Samosir \n",
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"29 SUMATERA UTARA Labuhan Batu \n",
|
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"30 SUMATERA UTARA Asahan \n",
|
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"31 SUMATERA UTARA Simalungun \n",
|
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"32 SUMATERA UTARA Dairi \n",
|
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"33 SUMATERA UTARA Karo \n",
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"34 SUMATERA UTARA Deli Serdang \n",
|
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"35 SUMATERA UTARA Langkat \n",
|
|
"36 SUMATERA UTARA Nias Selatan \n",
|
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"37 SUMATERA UTARA Humbang Hasundutan \n",
|
|
"38 SUMATERA UTARA Pakpak Bharat \n",
|
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"39 SUMATERA UTARA Samosir \n",
|
|
"40 SUMATERA UTARA Serdang Bedagai \n",
|
|
"41 SUMATERA UTARA Batu Bara \n",
|
|
"42 SUMATERA UTARA Padang Lawas Utara \n",
|
|
"43 SUMATERA UTARA Padang Lawas \n",
|
|
"44 SUMATERA UTARA Labuhan Batu Selatan \n",
|
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"45 SUMATERA UTARA Labuhan Batu Utara \n",
|
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"46 SUMATERA UTARA Nias Utara \n",
|
|
"47 SUMATERA UTARA Nias Barat \n",
|
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"48 SUMATERA UTARA Kota Sibolga \n",
|
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"49 SUMATERA UTARA Kota Tanjung Balai \n",
|
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"50 SUMATERA UTARA Kota Pematang Siantar \n",
|
|
"51 SUMATERA UTARA Kota Tebing Tinggi \n",
|
|
"52 SUMATERA UTARA Kota Medan \n",
|
|
"53 SUMATERA UTARA Kota Binjai \n",
|
|
"54 SUMATERA UTARA Kota Padangsidimpuan \n",
|
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"55 SUMATERA UTARA Kota Gunungsitoli \n",
|
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"56 SUMATERA BARAT Kepulauan Mentawai \n",
|
|
"57 SUMATERA BARAT Pesisir Selatan \n",
|
|
"58 SUMATERA BARAT Solok \n",
|
|
"59 SUMATERA BARAT Sijunjung \n",
|
|
"60 SUMATERA BARAT Tanah Datar \n",
|
|
"61 SUMATERA BARAT Padang Pariaman \n",
|
|
"62 SUMATERA BARAT Agam \n",
|
|
"63 SUMATERA BARAT Lima Puluh Kota \n",
|
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"64 SUMATERA BARAT Pasaman \n",
|
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"65 SUMATERA BARAT Solok Selatan \n",
|
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"66 SUMATERA BARAT Dharmasraya \n",
|
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"67 SUMATERA BARAT Pasaman Barat \n",
|
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"68 SUMATERA BARAT Kota Padang \n",
|
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"69 SUMATERA BARAT Kota Solok \n",
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"70 SUMATERA BARAT Kota Sawah Lunto \n",
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"71 SUMATERA BARAT Kota Padang Panjang \n",
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"72 SUMATERA BARAT Kota Bukittinggi \n",
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"73 SUMATERA BARAT Kota Payakumbuh \n",
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"74 SUMATERA BARAT Kota Pariaman \n",
|
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"75 RIAU Kuantan Singingi \n",
|
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"76 RIAU Indragiri Hulu \n",
|
|
"77 RIAU Indragiri Hilir \n",
|
|
"78 RIAU Pelalawan \n",
|
|
"79 RIAU Siak \n",
|
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"80 RIAU Kampar \n",
|
|
"81 RIAU Rokan Hulu \n",
|
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"82 RIAU Bengkalis \n",
|
|
"83 RIAU Rokan Hilir \n",
|
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"84 RIAU Kepulauan Meranti \n",
|
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"85 RIAU Kota Pekanbaru \n",
|
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"86 RIAU Kota Dumai \n",
|
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"87 JAMBI Kerinci \n",
|
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"88 JAMBI Merangin \n",
|
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"89 JAMBI Sarolangun \n",
|
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"90 JAMBI Batang Hari \n",
|
|
"91 JAMBI Muaro Jambi \n",
|
|
"92 JAMBI Tanjung Jabung Timur \n",
|
|
"93 JAMBI Tanjung Jabung Barat \n",
|
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"94 JAMBI Tebo \n",
|
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"95 JAMBI Bungo \n",
|
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"96 JAMBI Kota Jambi \n",
|
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"97 JAMBI Kota Sungai Penuh \n",
|
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"98 SUMATERA SELATAN Ogan Komering Ulu \n",
|
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"99 SUMATERA SELATAN Ogan Komering Ilir \n",
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"\n",
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" Persentase Penduduk Miskin (P0) Menurut Kabupaten/Kota (Persen) \\\n",
|
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"0 18,98 \n",
|
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"1 20,36 \n",
|
|
"2 13,18 \n",
|
|
"3 13,41 \n",
|
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"4 14,45 \n",
|
|
"5 15,26 \n",
|
|
"6 18,81 \n",
|
|
"7 14,05 \n",
|
|
"8 19,59 \n",
|
|
"9 13,25 \n",
|
|
"10 17,43 \n",
|
|
"11 16,34 \n",
|
|
"12 19,64 \n",
|
|
"13 13,34 \n",
|
|
"14 18,23 \n",
|
|
"15 13,23 \n",
|
|
"16 19,16 \n",
|
|
"17 19,55 \n",
|
|
"18 7,61 \n",
|
|
"19 15,32 \n",
|
|
"20 10,96 \n",
|
|
"21 11,16 \n",
|
|
"22 17,65 \n",
|
|
"23 16,82 \n",
|
|
"24 9,49 \n",
|
|
"25 8,8 \n",
|
|
"26 12,67 \n",
|
|
"27 9,72 \n",
|
|
"28 8,99 \n",
|
|
"29 8,74 \n",
|
|
"30 9,35 \n",
|
|
"31 8,81 \n",
|
|
"32 8,31 \n",
|
|
"33 8,79 \n",
|
|
"34 4,01 \n",
|
|
"35 10,12 \n",
|
|
"36 16,92 \n",
|
|
"37 9,65 \n",
|
|
"38 9,35 \n",
|
|
"39 12,68 \n",
|
|
"40 8,3 \n",
|
|
"41 12,38 \n",
|
|
"42 9,92 \n",
|
|
"43 8,69 \n",
|
|
"44 8,53 \n",
|
|
"45 10,02 \n",
|
|
"46 25,66 \n",
|
|
"47 26,42 \n",
|
|
"48 12,33 \n",
|
|
"49 13,4 \n",
|
|
"50 8,52 \n",
|
|
"51 10,3 \n",
|
|
"52 8,34 \n",
|
|
"53 5,81 \n",
|
|
"54 7,53 \n",
|
|
"55 16,45 \n",
|
|
"56 14,84 \n",
|
|
"57 7,92 \n",
|
|
"58 8,01 \n",
|
|
"59 6,8 \n",
|
|
"60 4,54 \n",
|
|
"61 7,22 \n",
|
|
"62 6,85 \n",
|
|
"63 7,29 \n",
|
|
"64 7,48 \n",
|
|
"65 7,52 \n",
|
|
"66 6,67 \n",
|
|
"67 7,51 \n",
|
|
"68 4,94 \n",
|
|
"69 3,12 \n",
|
|
"70 2,38 \n",
|
|
"71 5,92 \n",
|
|
"72 5,14 \n",
|
|
"73 6,16 \n",
|
|
"74 4,38 \n",
|
|
"75 8.97 \n",
|
|
"76 6.18 \n",
|
|
"77 6,18 \n",
|
|
"78 9,63 \n",
|
|
"79 5,18 \n",
|
|
"80 7,82 \n",
|
|
"81 10,4 \n",
|
|
"82 6,64 \n",
|
|
"83 7,18 \n",
|
|
"84 25,68 \n",
|
|
"85 2,83 \n",
|
|
"86 3,42 \n",
|
|
"87 7,71 \n",
|
|
"88 9,11 \n",
|
|
"89 8,87 \n",
|
|
"90 10,05 \n",
|
|
"91 4,53 \n",
|
|
"92 11,39 \n",
|
|
"93 10,75 \n",
|
|
"94 6,68 \n",
|
|
"95 6,23 \n",
|
|
"96 9,02 \n",
|
|
"97 3,41 \n",
|
|
"98 12,62 \n",
|
|
"99 14,68 \n",
|
|
"\n",
|
|
" Rata-rata Lama Sekolah Penduduk 15+ (Tahun) \\\n",
|
|
"0 9,48 \n",
|
|
"1 8,68 \n",
|
|
"2 8,88 \n",
|
|
"3 9,67 \n",
|
|
"4 8,21 \n",
|
|
"5 9,86 \n",
|
|
"6 9,55 \n",
|
|
"7 10,33 \n",
|
|
"8 9 \n",
|
|
"9 9,29 \n",
|
|
"10 8,64 \n",
|
|
"11 8,67 \n",
|
|
"12 8,4 \n",
|
|
"13 8,91 \n",
|
|
"14 8,69 \n",
|
|
"15 8,71 \n",
|
|
"16 10 \n",
|
|
"17 9,34 \n",
|
|
"18 12,83 \n",
|
|
"19 11,18 \n",
|
|
"20 11,12 \n",
|
|
"21 11,11 \n",
|
|
"22 8,03 \n",
|
|
"23 5,64 \n",
|
|
"24 8,63 \n",
|
|
"25 9,29 \n",
|
|
"26 8,84 \n",
|
|
"27 9,99 \n",
|
|
"28 10,57 \n",
|
|
"29 9,25 \n",
|
|
"30 8,8 \n",
|
|
"31 9,61 \n",
|
|
"32 9,59 \n",
|
|
"33 10 \n",
|
|
"34 10,1 \n",
|
|
"35 8,66 \n",
|
|
"36 6,06 \n",
|
|
"37 9,71 \n",
|
|
"38 9,14 \n",
|
|
"39 9,44 \n",
|
|
"40 8,69 \n",
|
|
"41 8,07 \n",
|
|
"42 9,38 \n",
|
|
"43 9,02 \n",
|
|
"44 8,9 \n",
|
|
"45 8,41 \n",
|
|
"46 6,77 \n",
|
|
"47 6,69 \n",
|
|
"48 10,41 \n",
|
|
"49 9,45 \n",
|
|
"50 11,29 \n",
|
|
"51 10,44 \n",
|
|
"52 11,48 \n",
|
|
"53 10,94 \n",
|
|
"54 11,09 \n",
|
|
"55 8,62 \n",
|
|
"56 7,2 \n",
|
|
"57 8,27 \n",
|
|
"58 7,87 \n",
|
|
"59 8,12 \n",
|
|
"60 8,62 \n",
|
|
"61 7,88 \n",
|
|
"62 8,97 \n",
|
|
"63 8,07 \n",
|
|
"64 8,1 \n",
|
|
"65 8,32 \n",
|
|
"66 8,55 \n",
|
|
"67 8,27 \n",
|
|
"68 11,59 \n",
|
|
"69 11,04 \n",
|
|
"70 10,32 \n",
|
|
"71 11,63 \n",
|
|
"72 11.34 \n",
|
|
"73 10.81 \n",
|
|
"74 10.67 \n",
|
|
"75 8.75 \n",
|
|
"76 8.39 \n",
|
|
"77 7,24 \n",
|
|
"78 8,7 \n",
|
|
"79 9,86 \n",
|
|
"80 9,27 \n",
|
|
"81 8,54 \n",
|
|
"82 9,7 \n",
|
|
"83 8,26 \n",
|
|
"84 7,84 \n",
|
|
"85 11,92 \n",
|
|
"86 10,14 \n",
|
|
"87 8,56 \n",
|
|
"88 7,9 \n",
|
|
"89 8,04 \n",
|
|
"90 8,12 \n",
|
|
"91 8,58 \n",
|
|
"92 6,92 \n",
|
|
"93 8 \n",
|
|
"94 7,59 \n",
|
|
"95 8,28 \n",
|
|
"96 11,2 \n",
|
|
"97 10,33 \n",
|
|
"98 8,71 \n",
|
|
"99 7,05 \n",
|
|
"\n",
|
|
" Pengeluaran per Kapita Disesuaikan (Ribu Rupiah/Orang/Tahun) \\\n",
|
|
"0 7148.0 \n",
|
|
"1 8776.0 \n",
|
|
"2 8180.0 \n",
|
|
"3 8030.0 \n",
|
|
"4 8577.0 \n",
|
|
"5 10780.0 \n",
|
|
"6 9593.0 \n",
|
|
"7 9644.0 \n",
|
|
"8 9860.0 \n",
|
|
"9 8867.0 \n",
|
|
"10 8201.0 \n",
|
|
"11 8428.0 \n",
|
|
"12 8856.0 \n",
|
|
"13 8367.0 \n",
|
|
"14 8292.0 \n",
|
|
"15 9666.0 \n",
|
|
"16 11118.0 \n",
|
|
"17 10290.0 \n",
|
|
"18 16891.0 \n",
|
|
"19 11378.0 \n",
|
|
"20 12067.0 \n",
|
|
"21 11390.0 \n",
|
|
"22 7385.0 \n",
|
|
"23 6995.0 \n",
|
|
"24 9771.0 \n",
|
|
"25 11304.0 \n",
|
|
"26 10138.0 \n",
|
|
"27 11710.0 \n",
|
|
"28 12224.0 \n",
|
|
"29 11212.0 \n",
|
|
"30 11030.0 \n",
|
|
"31 11376.0 \n",
|
|
"32 10504.0 \n",
|
|
"33 12412.0 \n",
|
|
"34 12291.0 \n",
|
|
"35 11142.0 \n",
|
|
"36 7041.0 \n",
|
|
"37 8016.0 \n",
|
|
"38 8254.0 \n",
|
|
"39 8504.0 \n",
|
|
"40 11017.0 \n",
|
|
"41 10539.0 \n",
|
|
"42 10055.0 \n",
|
|
"43 8921.0 \n",
|
|
"44 11562.0 \n",
|
|
"45 11840.0 \n",
|
|
"46 6155.0 \n",
|
|
"47 5924.0 \n",
|
|
"48 11540.0 \n",
|
|
"49 11225.0 \n",
|
|
"50 12436.0 \n",
|
|
"51 12939.0 \n",
|
|
"52 14999.0 \n",
|
|
"53 11063.0 \n",
|
|
"54 10965.0 \n",
|
|
"55 8134.0 \n",
|
|
"56 6321.0 \n",
|
|
"57 9270.0 \n",
|
|
"58 10215.0 \n",
|
|
"59 10389.0 \n",
|
|
"60 10616.0 \n",
|
|
"61 11050.0 \n",
|
|
"62 9662.0 \n",
|
|
"63 9668.0 \n",
|
|
"64 8440.0 \n",
|
|
"65 10367.0 \n",
|
|
"66 11324.0 \n",
|
|
"67 9089.0 \n",
|
|
"68 14540.0 \n",
|
|
"69 12168.0 \n",
|
|
"70 10195.0 \n",
|
|
"71 10754.0 \n",
|
|
"72 13331.0 \n",
|
|
"73 13317.0 \n",
|
|
"74 12818.0 \n",
|
|
"75 10309.0 \n",
|
|
"76 10260.0 \n",
|
|
"77 9945.0 \n",
|
|
"78 11672.0 \n",
|
|
"79 11807.0 \n",
|
|
"80 10858.0 \n",
|
|
"81 9406.0 \n",
|
|
"82 11415.0 \n",
|
|
"83 9417.0 \n",
|
|
"84 7780.0 \n",
|
|
"85 14360.0 \n",
|
|
"86 11818.0 \n",
|
|
"87 10184.0 \n",
|
|
"88 10380.0 \n",
|
|
"89 11792.0 \n",
|
|
"90 10032.0 \n",
|
|
"91 8825.0 \n",
|
|
"92 9163.0 \n",
|
|
"93 9699.0 \n",
|
|
"94 10546.0 \n",
|
|
"95 11670.0 \n",
|
|
"96 12240.0 \n",
|
|
"97 10454.0 \n",
|
|
"98 10040.0 \n",
|
|
"99 10755.0 \n",
|
|
"\n",
|
|
" Indeks Pembangunan Manusia Umur Harapan Hidup (Tahun) \\\n",
|
|
"0 66,41 65,28 \n",
|
|
"1 69,22 67,43 \n",
|
|
"2 67,44 64,4 \n",
|
|
"3 69,44 68,22 \n",
|
|
"4 67,83 68,74 \n",
|
|
"5 73,37 68,86 \n",
|
|
"6 71,67 67,99 \n",
|
|
"7 73,58 69,79 \n",
|
|
"8 70,7 66,95 \n",
|
|
"9 72,33 71,26 \n",
|
|
"10 69,46 68,81 \n",
|
|
"11 66,99 65,06 \n",
|
|
"12 67,56 65,53 \n",
|
|
"13 69,48 69,63 \n",
|
|
"14 69,31 69,24 \n",
|
|
"15 69,84 67,19 \n",
|
|
"16 73,27 69,26 \n",
|
|
"17 73,6 70,18 \n",
|
|
"18 85,71 71,52 \n",
|
|
"19 76,11 70,56 \n",
|
|
"20 77,44 69,43 \n",
|
|
"21 77,57 71,64 \n",
|
|
"22 65,27 64,07 \n",
|
|
"23 62,74 69,78 \n",
|
|
"24 67,19 62,65 \n",
|
|
"25 70,33 64,97 \n",
|
|
"26 69,61 67,24 \n",
|
|
"27 73,76 68,76 \n",
|
|
"28 75,39 70,29 \n",
|
|
"29 72,09 69,95 \n",
|
|
"30 70,49 68,37 \n",
|
|
"31 73,4 71,37 \n",
|
|
"32 71,84 69,19 \n",
|
|
"33 74,83 71,58 \n",
|
|
"34 75,53 71,77 \n",
|
|
"35 71,35 68,97 \n",
|
|
"36 62,35 68,86 \n",
|
|
"37 69,41 69,51 \n",
|
|
"38 67,94 65,96 \n",
|
|
"39 70,83 71,41 \n",
|
|
"40 70,56 68,82 \n",
|
|
"41 68,58 67,13 \n",
|
|
"42 70,11 67,22 \n",
|
|
"43 68,64 67,13 \n",
|
|
"44 71,69 68,81 \n",
|
|
"45 71,87 69,56 \n",
|
|
"46 62,82 69,55 \n",
|
|
"47 61,99 69,08 \n",
|
|
"48 73,94 69,25 \n",
|
|
"49 68,94 63,44 \n",
|
|
"50 79,17 73,77 \n",
|
|
"51 75,42 70,95 \n",
|
|
"52 81,21 73,23 \n",
|
|
"53 76,01 72,45 \n",
|
|
"54 75,48 69,5 \n",
|
|
"55 69,61 71,32 \n",
|
|
"56 61,35 64,73 \n",
|
|
"57 70,03 70,96 \n",
|
|
"58 69,24 68,79 \n",
|
|
"59 67,86 66,36 \n",
|
|
"60 72,46 70,12 \n",
|
|
"61 70,76 68,97 \n",
|
|
"62 72,57 72,53 \n",
|
|
"63 69,68 69,84 \n",
|
|
"64 66,77 67,59 \n",
|
|
"65 69,23 68,01 \n",
|
|
"66 71,76 71,53 \n",
|
|
"67 68,76 67,94 \n",
|
|
"68 82,9 73,69 \n",
|
|
"69 78,41 73,73 \n",
|
|
"70 72,88 70,1 \n",
|
|
"71 77,97 72,82 \n",
|
|
"72 80,7 74,5 \n",
|
|
"73 79,08 73,84 \n",
|
|
"74 77,07 70,38 \n",
|
|
"75 70,6 68,6 \n",
|
|
"76 70,01 70,26 \n",
|
|
"77 66,63 67,98 \n",
|
|
"78 72,08 71,24 \n",
|
|
"79 73,98 71,13 \n",
|
|
"80 73,02 70,83 \n",
|
|
"81 69,67 70,18 \n",
|
|
"82 73,58 71,24 \n",
|
|
"83 69,34 70,39 \n",
|
|
"84 65,7 67,78 \n",
|
|
"85 81,58 72,41 \n",
|
|
"86 74,75 70,98 \n",
|
|
"87 71,45 70 \n",
|
|
"88 69,53 71,29 \n",
|
|
"89 70,25 69,21 \n",
|
|
"90 70,11 70,64 \n",
|
|
"91 69,55 71,32 \n",
|
|
"92 64,91 66,34 \n",
|
|
"93 68,16 68,17 \n",
|
|
"94 69,35 70,02 \n",
|
|
"95 70,15 67,83 \n",
|
|
"96 79,12 72,71 \n",
|
|
"97 75,7 72,21 \n",
|
|
"98 69,6 68,24 \n",
|
|
"99 67,17 68,67 \n",
|
|
"\n",
|
|
" Persentase rumah tangga yang memiliki akses terhadap sanitasi layak \\\n",
|
|
"0 71,56 \n",
|
|
"1 69,56 \n",
|
|
"2 62,55 \n",
|
|
"3 62,71 \n",
|
|
"4 66,75 \n",
|
|
"5 90,58 \n",
|
|
"6 89,60 \n",
|
|
"7 87,40 \n",
|
|
"8 54,10 \n",
|
|
"9 81,89 \n",
|
|
"10 79,97 \n",
|
|
"11 65,71 \n",
|
|
"12 47,63 \n",
|
|
"13 87,45 \n",
|
|
"14 74,86 \n",
|
|
"15 81,60 \n",
|
|
"16 86,69 \n",
|
|
"17 74,30 \n",
|
|
"18 99,88 \n",
|
|
"19 92,25 \n",
|
|
"20 91,81 \n",
|
|
"21 93,10 \n",
|
|
"22 69,71 \n",
|
|
"23 19,93 \n",
|
|
"24 35,73 \n",
|
|
"25 46,41 \n",
|
|
"26 57,56 \n",
|
|
"27 83,79 \n",
|
|
"28 89,54 \n",
|
|
"29 81,50 \n",
|
|
"30 89,09 \n",
|
|
"31 91,75 \n",
|
|
"32 92,35 \n",
|
|
"33 84,33 \n",
|
|
"34 96,37 \n",
|
|
"35 80,76 \n",
|
|
"36 13,14 \n",
|
|
"37 91,65 \n",
|
|
"38 90,14 \n",
|
|
"39 91,09 \n",
|
|
"40 93,19 \n",
|
|
"41 88,04 \n",
|
|
"42 67,17 \n",
|
|
"43 59,62 \n",
|
|
"44 84,85 \n",
|
|
"45 79,75 \n",
|
|
"46 46,09 \n",
|
|
"47 38,02 \n",
|
|
"48 32,33 \n",
|
|
"49 89,07 \n",
|
|
"50 88,49 \n",
|
|
"51 95,88 \n",
|
|
"52 92,71 \n",
|
|
"53 95,21 \n",
|
|
"54 51,33 \n",
|
|
"55 45,13 \n",
|
|
"56 71,05 \n",
|
|
"57 71,40 \n",
|
|
"58 47,63 \n",
|
|
"59 72,63 \n",
|
|
"60 53,73 \n",
|
|
"61 55,86 \n",
|
|
"62 77,25 \n",
|
|
"63 55,69 \n",
|
|
"64 45,26 \n",
|
|
"65 58,57 \n",
|
|
"66 82,71 \n",
|
|
"67 70,28 \n",
|
|
"68 80,13 \n",
|
|
"69 91,40 \n",
|
|
"70 86,45 \n",
|
|
"71 73,04 \n",
|
|
"72 89,93 \n",
|
|
"73 89,49 \n",
|
|
"74 79,56 \n",
|
|
"75 87,98 \n",
|
|
"76 82,82 \n",
|
|
"77 49,64 \n",
|
|
"78 81,13 \n",
|
|
"79 95,41 \n",
|
|
"80 89,62 \n",
|
|
"81 88,85 \n",
|
|
"82 90,34 \n",
|
|
"83 73,94 \n",
|
|
"84 55,98 \n",
|
|
"85 96,28 \n",
|
|
"86 97,03 \n",
|
|
"87 66,32 \n",
|
|
"88 71,07 \n",
|
|
"89 76,03 \n",
|
|
"90 79,68 \n",
|
|
"91 89,17 \n",
|
|
"92 70,27 \n",
|
|
"93 81,89 \n",
|
|
"94 83,38 \n",
|
|
"95 77,58 \n",
|
|
"96 93,22 \n",
|
|
"97 74,04 \n",
|
|
"98 82,72 \n",
|
|
"99 68,09 \n",
|
|
"\n",
|
|
" Persentase rumah tangga yang memiliki akses terhadap air minum layak \\\n",
|
|
"0 87,45 \n",
|
|
"1 78,58 \n",
|
|
"2 79,65 \n",
|
|
"3 86,71 \n",
|
|
"4 83,16 \n",
|
|
"5 90,10 \n",
|
|
"6 94,22 \n",
|
|
"7 82,36 \n",
|
|
"8 89,24 \n",
|
|
"9 93,53 \n",
|
|
"10 91,09 \n",
|
|
"11 95,34 \n",
|
|
"12 84,68 \n",
|
|
"13 83,12 \n",
|
|
"14 90,13 \n",
|
|
"15 86,36 \n",
|
|
"16 89,71 \n",
|
|
"17 93,46 \n",
|
|
"18 99,37 \n",
|
|
"19 96,18 \n",
|
|
"20 97,14 \n",
|
|
"21 94,44 \n",
|
|
"22 60,87 \n",
|
|
"23 47,79 \n",
|
|
"24 73,78 \n",
|
|
"25 67,39 \n",
|
|
"26 68,81 \n",
|
|
"27 89,06 \n",
|
|
"28 95,04 \n",
|
|
"29 94,34 \n",
|
|
"30 95,78 \n",
|
|
"31 99,74 \n",
|
|
"32 91,90 \n",
|
|
"33 91,43 \n",
|
|
"34 98,18 \n",
|
|
"35 92,51 \n",
|
|
"36 66,21 \n",
|
|
"37 91,95 \n",
|
|
"38 70,69 \n",
|
|
"39 65,64 \n",
|
|
"40 98,14 \n",
|
|
"41 97,83 \n",
|
|
"42 77,58 \n",
|
|
"43 77,84 \n",
|
|
"44 84,66 \n",
|
|
"45 86,75 \n",
|
|
"46 58,17 \n",
|
|
"47 71,52 \n",
|
|
"48 92,40 \n",
|
|
"49 87,20 \n",
|
|
"50 99,78 \n",
|
|
"51 99,35 \n",
|
|
"52 98,80 \n",
|
|
"53 99,76 \n",
|
|
"54 54,13 \n",
|
|
"55 74,11 \n",
|
|
"56 55,46 \n",
|
|
"57 76,23 \n",
|
|
"58 78,09 \n",
|
|
"59 64,12 \n",
|
|
"60 86,50 \n",
|
|
"61 86,09 \n",
|
|
"62 87,46 \n",
|
|
"63 68,93 \n",
|
|
"64 82,57 \n",
|
|
"65 81,01 \n",
|
|
"66 70,27 \n",
|
|
"67 81,47 \n",
|
|
"68 95,52 \n",
|
|
"69 96,55 \n",
|
|
"70 88,27 \n",
|
|
"71 98,45 \n",
|
|
"72 97,05 \n",
|
|
"73 99,43 \n",
|
|
"74 97,69 \n",
|
|
"75 85,78 \n",
|
|
"76 64,84 \n",
|
|
"77 89,60 \n",
|
|
"78 83,61 \n",
|
|
"79 93,13 \n",
|
|
"80 90,41 \n",
|
|
"81 92,17 \n",
|
|
"82 93,26 \n",
|
|
"83 87,52 \n",
|
|
"84 88,69 \n",
|
|
"85 98,76 \n",
|
|
"86 92,86 \n",
|
|
"87 74,59 \n",
|
|
"88 68,91 \n",
|
|
"89 69,96 \n",
|
|
"90 73,08 \n",
|
|
"91 80,66 \n",
|
|
"92 85,91 \n",
|
|
"93 91,76 \n",
|
|
"94 69,97 \n",
|
|
"95 73,63 \n",
|
|
"96 95,83 \n",
|
|
"97 90,55 \n",
|
|
"98 81,78 \n",
|
|
"99 79,02 \n",
|
|
"\n",
|
|
" Tingkat Pengangguran Terbuka Tingkat Partisipasi Angkatan Kerja \\\n",
|
|
"0 5,71 71,15 \n",
|
|
"1 8,36 62,85 \n",
|
|
"2 6,46 60,85 \n",
|
|
"3 6,43 69,62 \n",
|
|
"4 7,13 59,48 \n",
|
|
"5 2,61 76,30 \n",
|
|
"6 7,09 60,05 \n",
|
|
"7 7,70 61,67 \n",
|
|
"8 7,28 60,29 \n",
|
|
"9 4,32 65,91 \n",
|
|
"10 8,31 58,47 \n",
|
|
"11 4,04 57,91 \n",
|
|
"12 1,84 78,99 \n",
|
|
"13 5,87 66,43 \n",
|
|
"14 4,99 64,99 \n",
|
|
"15 3,47 72,59 \n",
|
|
"16 1,24 77,53 \n",
|
|
"17 3,57 57,77 \n",
|
|
"18 8,94 63,00 \n",
|
|
"19 3,56 63,71 \n",
|
|
"20 7,21 67,04 \n",
|
|
"21 11,16 63,91 \n",
|
|
"22 6,26 63,78 \n",
|
|
"23 3,12 81,79 \n",
|
|
"24 6,12 69,79 \n",
|
|
"25 4,00 74,38 \n",
|
|
"26 7,24 75,05 \n",
|
|
"27 1,54 82,63 \n",
|
|
"28 0,83 80,38 \n",
|
|
"29 5,66 61,84 \n",
|
|
"30 6,39 63,02 \n",
|
|
"31 4,17 72,55 \n",
|
|
"32 1,49 85,73 \n",
|
|
"33 1,95 84,56 \n",
|
|
"34 9,13 66,78 \n",
|
|
"35 5,12 69,12 \n",
|
|
"36 3,91 72,25 \n",
|
|
"37 1,94 84,17 \n",
|
|
"38 1,36 87,70 \n",
|
|
"39 0,70 84,38 \n",
|
|
"40 3,93 66,75 \n",
|
|
"41 6,62 70,00 \n",
|
|
"42 3,19 76,82 \n",
|
|
"43 4,07 75,23 \n",
|
|
"44 4,71 66,38 \n",
|
|
"45 5,74 65,73 \n",
|
|
"46 3,00 74,27 \n",
|
|
"47 0,74 82,08 \n",
|
|
"48 8,72 71,19 \n",
|
|
"49 6,59 66,57 \n",
|
|
"50 11,00 68,80 \n",
|
|
"51 8,37 67,19 \n",
|
|
"52 10,81 62,16 \n",
|
|
"53 7,86 62,77 \n",
|
|
"54 7,18 68,69 \n",
|
|
"55 4,80 62,95 \n",
|
|
"56 2,79 82,57 \n",
|
|
"57 5,97 66,59 \n",
|
|
"58 4,67 71,21 \n",
|
|
"59 3,57 70,06 \n",
|
|
"60 4,63 66,88 \n",
|
|
"61 8,41 64,64 \n",
|
|
"62 5,06 66,49 \n",
|
|
"63 2,25 71,33 \n",
|
|
"64 4,92 69,35 \n",
|
|
"65 4,84 72,11 \n",
|
|
"66 5,00 73,04 \n",
|
|
"67 5,02 66,93 \n",
|
|
"68 13,37 63,78 \n",
|
|
"69 5,15 66,51 \n",
|
|
"70 6,38 68,05 \n",
|
|
"71 4,90 65,94 \n",
|
|
"72 6,09 67,42 \n",
|
|
"73 6,47 71,73 \n",
|
|
"74 6,09 62,70 \n",
|
|
"75 2,06 67,02 \n",
|
|
"76 3,32 68,45 \n",
|
|
"77 2,66 68,56 \n",
|
|
"78 2,34 69,18 \n",
|
|
"79 4,34 64,69 \n",
|
|
"80 4,27 63,06 \n",
|
|
"81 2,25 66,50 \n",
|
|
"82 6,63 66,76 \n",
|
|
"83 3,25 60,74 \n",
|
|
"84 4,43 65,60 \n",
|
|
"85 8,29 61,61 \n",
|
|
"86 6,29 64,91 \n",
|
|
"87 2,32 70,90 \n",
|
|
"88 4,83 69,85 \n",
|
|
"89 5,52 64,86 \n",
|
|
"90 4,26 68,81 \n",
|
|
"91 5,59 62,78 \n",
|
|
"92 1,56 71,22 \n",
|
|
"93 2,53 73,89 \n",
|
|
"94 2,83 70,65 \n",
|
|
"95 5,86 63,58 \n",
|
|
"96 10,66 63,12 \n",
|
|
"97 3,00 64,92 \n",
|
|
"98 4,57 69,96 \n",
|
|
"99 3,01 69,68 \n",
|
|
"\n",
|
|
" PDRB atas Dasar Harga Konstan menurut Pengeluaran (Rupiah) \\\n",
|
|
"0 1648096.0 \n",
|
|
"1 1780419.0 \n",
|
|
"2 4345784.0 \n",
|
|
"3 3487157.0 \n",
|
|
"4 8433526.0 \n",
|
|
"5 5953118.0 \n",
|
|
"6 7485861.0 \n",
|
|
"7 10261585.0 \n",
|
|
"8 7975099.0 \n",
|
|
"9 10374480.0 \n",
|
|
"10 16924103.0 \n",
|
|
"11 3069805.0 \n",
|
|
"12 1981879.0 \n",
|
|
"13 6062520.0 \n",
|
|
"14 7110421.0 \n",
|
|
"15 2033844.0 \n",
|
|
"16 3744095.0 \n",
|
|
"17 2635517.0 \n",
|
|
"18 15454371.0 \n",
|
|
"19 1152875.0 \n",
|
|
"20 3974614.0 \n",
|
|
"21 7252905.0 \n",
|
|
"22 1438997.0 \n",
|
|
"23 2666855.0 \n",
|
|
"24 9585899.0 \n",
|
|
"25 10036712.0 \n",
|
|
"26 7149285.0 \n",
|
|
"27 6058350.0 \n",
|
|
"28 5649103.0 \n",
|
|
"29 24147560.0 \n",
|
|
"30 27279586.0 \n",
|
|
"31 28648781.0 \n",
|
|
"32 6641674.0 \n",
|
|
"33 14582333.0 \n",
|
|
"34 72173623.0 \n",
|
|
"35 30247389.0 \n",
|
|
"36 4332626.0 \n",
|
|
"37 4219138.0 \n",
|
|
"38 872403.0 \n",
|
|
"39 3162101.0 \n",
|
|
"40 19863243.0 \n",
|
|
"41 24486056.0 \n",
|
|
"42 8593639.0 \n",
|
|
"43 8362135.0 \n",
|
|
"44 19620408.0 \n",
|
|
"45 17969257.0 \n",
|
|
"46 2417196.0 \n",
|
|
"47 1285664.0 \n",
|
|
"48 3595704.0 \n",
|
|
"49 5898808.0 \n",
|
|
"50 9547698.0 \n",
|
|
"51 4024777.0 \n",
|
|
"52 157689187.0 \n",
|
|
"53 8162776.0 \n",
|
|
"54 4346777.0 \n",
|
|
"55 3519128.0 \n",
|
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|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Provinsi</th>\n",
|
|
" <th>Kab/Kota</th>\n",
|
|
" <th>Persentase Penduduk Miskin (P0) Menurut Kabupaten/Kota (Persen)</th>\n",
|
|
" <th>Rata-rata Lama Sekolah Penduduk 15+ (Tahun)</th>\n",
|
|
" <th>Pengeluaran per Kapita Disesuaikan (Ribu Rupiah/Orang/Tahun)</th>\n",
|
|
" <th>Indeks Pembangunan Manusia</th>\n",
|
|
" <th>Umur Harapan Hidup (Tahun)</th>\n",
|
|
" <th>Persentase rumah tangga yang memiliki akses terhadap sanitasi layak</th>\n",
|
|
" <th>Persentase rumah tangga yang memiliki akses terhadap air minum layak</th>\n",
|
|
" <th>Tingkat Pengangguran Terbuka</th>\n",
|
|
" <th>Tingkat Partisipasi Angkatan Kerja</th>\n",
|
|
" <th>PDRB atas Dasar Harga Konstan menurut Pengeluaran (Rupiah)</th>\n",
|
|
" <th>Klasifikasi Kemiskinan</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Simeulue</td>\n",
|
|
" <td>18,98</td>\n",
|
|
" <td>9,48</td>\n",
|
|
" <td>7148.0</td>\n",
|
|
" <td>66,41</td>\n",
|
|
" <td>65,28</td>\n",
|
|
" <td>71,56</td>\n",
|
|
" <td>87,45</td>\n",
|
|
" <td>5,71</td>\n",
|
|
" <td>71,15</td>\n",
|
|
" <td>1648096.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Aceh Singkil</td>\n",
|
|
" <td>20,36</td>\n",
|
|
" <td>8,68</td>\n",
|
|
" <td>8776.0</td>\n",
|
|
" <td>69,22</td>\n",
|
|
" <td>67,43</td>\n",
|
|
" <td>69,56</td>\n",
|
|
" <td>78,58</td>\n",
|
|
" <td>8,36</td>\n",
|
|
" <td>62,85</td>\n",
|
|
" <td>1780419.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Aceh Selatan</td>\n",
|
|
" <td>13,18</td>\n",
|
|
" <td>8,88</td>\n",
|
|
" <td>8180.0</td>\n",
|
|
" <td>67,44</td>\n",
|
|
" <td>64,4</td>\n",
|
|
" <td>62,55</td>\n",
|
|
" <td>79,65</td>\n",
|
|
" <td>6,46</td>\n",
|
|
" <td>60,85</td>\n",
|
|
" <td>4345784.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Aceh Tenggara</td>\n",
|
|
" <td>13,41</td>\n",
|
|
" <td>9,67</td>\n",
|
|
" <td>8030.0</td>\n",
|
|
" <td>69,44</td>\n",
|
|
" <td>68,22</td>\n",
|
|
" <td>62,71</td>\n",
|
|
" <td>86,71</td>\n",
|
|
" <td>6,43</td>\n",
|
|
" <td>69,62</td>\n",
|
|
" <td>3487157.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Aceh Timur</td>\n",
|
|
" <td>14,45</td>\n",
|
|
" <td>8,21</td>\n",
|
|
" <td>8577.0</td>\n",
|
|
" <td>67,83</td>\n",
|
|
" <td>68,74</td>\n",
|
|
" <td>66,75</td>\n",
|
|
" <td>83,16</td>\n",
|
|
" <td>7,13</td>\n",
|
|
" <td>59,48</td>\n",
|
|
" <td>8433526.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Aceh Tengah</td>\n",
|
|
" <td>15,26</td>\n",
|
|
" <td>9,86</td>\n",
|
|
" <td>10780.0</td>\n",
|
|
" <td>73,37</td>\n",
|
|
" <td>68,86</td>\n",
|
|
" <td>90,58</td>\n",
|
|
" <td>90,10</td>\n",
|
|
" <td>2,61</td>\n",
|
|
" <td>76,30</td>\n",
|
|
" <td>5953118.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Aceh Barat</td>\n",
|
|
" <td>18,81</td>\n",
|
|
" <td>9,55</td>\n",
|
|
" <td>9593.0</td>\n",
|
|
" <td>71,67</td>\n",
|
|
" <td>67,99</td>\n",
|
|
" <td>89,60</td>\n",
|
|
" <td>94,22</td>\n",
|
|
" <td>7,09</td>\n",
|
|
" <td>60,05</td>\n",
|
|
" <td>7485861.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Aceh Besar</td>\n",
|
|
" <td>14,05</td>\n",
|
|
" <td>10,33</td>\n",
|
|
" <td>9644.0</td>\n",
|
|
" <td>73,58</td>\n",
|
|
" <td>69,79</td>\n",
|
|
" <td>87,40</td>\n",
|
|
" <td>82,36</td>\n",
|
|
" <td>7,70</td>\n",
|
|
" <td>61,67</td>\n",
|
|
" <td>10261585.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Pidie</td>\n",
|
|
" <td>19,59</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>9860.0</td>\n",
|
|
" <td>70,7</td>\n",
|
|
" <td>66,95</td>\n",
|
|
" <td>54,10</td>\n",
|
|
" <td>89,24</td>\n",
|
|
" <td>7,28</td>\n",
|
|
" <td>60,29</td>\n",
|
|
" <td>7975099.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Bireuen</td>\n",
|
|
" <td>13,25</td>\n",
|
|
" <td>9,29</td>\n",
|
|
" <td>8867.0</td>\n",
|
|
" <td>72,33</td>\n",
|
|
" <td>71,26</td>\n",
|
|
" <td>81,89</td>\n",
|
|
" <td>93,53</td>\n",
|
|
" <td>4,32</td>\n",
|
|
" <td>65,91</td>\n",
|
|
" <td>10374480.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Aceh Utara</td>\n",
|
|
" <td>17,43</td>\n",
|
|
" <td>8,64</td>\n",
|
|
" <td>8201.0</td>\n",
|
|
" <td>69,46</td>\n",
|
|
" <td>68,81</td>\n",
|
|
" <td>79,97</td>\n",
|
|
" <td>91,09</td>\n",
|
|
" <td>8,31</td>\n",
|
|
" <td>58,47</td>\n",
|
|
" <td>16924103.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>11</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Aceh Barat Daya</td>\n",
|
|
" <td>16,34</td>\n",
|
|
" <td>8,67</td>\n",
|
|
" <td>8428.0</td>\n",
|
|
" <td>66,99</td>\n",
|
|
" <td>65,06</td>\n",
|
|
" <td>65,71</td>\n",
|
|
" <td>95,34</td>\n",
|
|
" <td>4,04</td>\n",
|
|
" <td>57,91</td>\n",
|
|
" <td>3069805.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>12</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Gayo Lues</td>\n",
|
|
" <td>19,64</td>\n",
|
|
" <td>8,4</td>\n",
|
|
" <td>8856.0</td>\n",
|
|
" <td>67,56</td>\n",
|
|
" <td>65,53</td>\n",
|
|
" <td>47,63</td>\n",
|
|
" <td>84,68</td>\n",
|
|
" <td>1,84</td>\n",
|
|
" <td>78,99</td>\n",
|
|
" <td>1981879.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>13</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Aceh Tamiang</td>\n",
|
|
" <td>13,34</td>\n",
|
|
" <td>8,91</td>\n",
|
|
" <td>8367.0</td>\n",
|
|
" <td>69,48</td>\n",
|
|
" <td>69,63</td>\n",
|
|
" <td>87,45</td>\n",
|
|
" <td>83,12</td>\n",
|
|
" <td>5,87</td>\n",
|
|
" <td>66,43</td>\n",
|
|
" <td>6062520.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>14</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Nagan Raya</td>\n",
|
|
" <td>18,23</td>\n",
|
|
" <td>8,69</td>\n",
|
|
" <td>8292.0</td>\n",
|
|
" <td>69,31</td>\n",
|
|
" <td>69,24</td>\n",
|
|
" <td>74,86</td>\n",
|
|
" <td>90,13</td>\n",
|
|
" <td>4,99</td>\n",
|
|
" <td>64,99</td>\n",
|
|
" <td>7110421.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>15</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Aceh Jaya</td>\n",
|
|
" <td>13,23</td>\n",
|
|
" <td>8,71</td>\n",
|
|
" <td>9666.0</td>\n",
|
|
" <td>69,84</td>\n",
|
|
" <td>67,19</td>\n",
|
|
" <td>81,60</td>\n",
|
|
" <td>86,36</td>\n",
|
|
" <td>3,47</td>\n",
|
|
" <td>72,59</td>\n",
|
|
" <td>2033844.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>16</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Bener Meriah</td>\n",
|
|
" <td>19,16</td>\n",
|
|
" <td>10</td>\n",
|
|
" <td>11118.0</td>\n",
|
|
" <td>73,27</td>\n",
|
|
" <td>69,26</td>\n",
|
|
" <td>86,69</td>\n",
|
|
" <td>89,71</td>\n",
|
|
" <td>1,24</td>\n",
|
|
" <td>77,53</td>\n",
|
|
" <td>3744095.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>17</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Pidie Jaya</td>\n",
|
|
" <td>19,55</td>\n",
|
|
" <td>9,34</td>\n",
|
|
" <td>10290.0</td>\n",
|
|
" <td>73,6</td>\n",
|
|
" <td>70,18</td>\n",
|
|
" <td>74,30</td>\n",
|
|
" <td>93,46</td>\n",
|
|
" <td>3,57</td>\n",
|
|
" <td>57,77</td>\n",
|
|
" <td>2635517.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>18</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Kota Banda Aceh</td>\n",
|
|
" <td>7,61</td>\n",
|
|
" <td>12,83</td>\n",
|
|
" <td>16891.0</td>\n",
|
|
" <td>85,71</td>\n",
|
|
" <td>71,52</td>\n",
|
|
" <td>99,88</td>\n",
|
|
" <td>99,37</td>\n",
|
|
" <td>8,94</td>\n",
|
|
" <td>63,00</td>\n",
|
|
" <td>15454371.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>19</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Kota Sabang</td>\n",
|
|
" <td>15,32</td>\n",
|
|
" <td>11,18</td>\n",
|
|
" <td>11378.0</td>\n",
|
|
" <td>76,11</td>\n",
|
|
" <td>70,56</td>\n",
|
|
" <td>92,25</td>\n",
|
|
" <td>96,18</td>\n",
|
|
" <td>3,56</td>\n",
|
|
" <td>63,71</td>\n",
|
|
" <td>1152875.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>20</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Kota Langsa</td>\n",
|
|
" <td>10,96</td>\n",
|
|
" <td>11,12</td>\n",
|
|
" <td>12067.0</td>\n",
|
|
" <td>77,44</td>\n",
|
|
" <td>69,43</td>\n",
|
|
" <td>91,81</td>\n",
|
|
" <td>97,14</td>\n",
|
|
" <td>7,21</td>\n",
|
|
" <td>67,04</td>\n",
|
|
" <td>3974614.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>21</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Kota Lhokseumawe</td>\n",
|
|
" <td>11,16</td>\n",
|
|
" <td>11,11</td>\n",
|
|
" <td>11390.0</td>\n",
|
|
" <td>77,57</td>\n",
|
|
" <td>71,64</td>\n",
|
|
" <td>93,10</td>\n",
|
|
" <td>94,44</td>\n",
|
|
" <td>11,16</td>\n",
|
|
" <td>63,91</td>\n",
|
|
" <td>7252905.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>22</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>Kota Subulussalam</td>\n",
|
|
" <td>17,65</td>\n",
|
|
" <td>8,03</td>\n",
|
|
" <td>7385.0</td>\n",
|
|
" <td>65,27</td>\n",
|
|
" <td>64,07</td>\n",
|
|
" <td>69,71</td>\n",
|
|
" <td>60,87</td>\n",
|
|
" <td>6,26</td>\n",
|
|
" <td>63,78</td>\n",
|
|
" <td>1438997.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>23</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Nias</td>\n",
|
|
" <td>16,82</td>\n",
|
|
" <td>5,64</td>\n",
|
|
" <td>6995.0</td>\n",
|
|
" <td>62,74</td>\n",
|
|
" <td>69,78</td>\n",
|
|
" <td>19,93</td>\n",
|
|
" <td>47,79</td>\n",
|
|
" <td>3,12</td>\n",
|
|
" <td>81,79</td>\n",
|
|
" <td>2666855.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>24</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Mandailing Natal</td>\n",
|
|
" <td>9,49</td>\n",
|
|
" <td>8,63</td>\n",
|
|
" <td>9771.0</td>\n",
|
|
" <td>67,19</td>\n",
|
|
" <td>62,65</td>\n",
|
|
" <td>35,73</td>\n",
|
|
" <td>73,78</td>\n",
|
|
" <td>6,12</td>\n",
|
|
" <td>69,79</td>\n",
|
|
" <td>9585899.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Tapanuli Selatan</td>\n",
|
|
" <td>8,8</td>\n",
|
|
" <td>9,29</td>\n",
|
|
" <td>11304.0</td>\n",
|
|
" <td>70,33</td>\n",
|
|
" <td>64,97</td>\n",
|
|
" <td>46,41</td>\n",
|
|
" <td>67,39</td>\n",
|
|
" <td>4,00</td>\n",
|
|
" <td>74,38</td>\n",
|
|
" <td>10036712.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>26</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Tapanuli Tengah</td>\n",
|
|
" <td>12,67</td>\n",
|
|
" <td>8,84</td>\n",
|
|
" <td>10138.0</td>\n",
|
|
" <td>69,61</td>\n",
|
|
" <td>67,24</td>\n",
|
|
" <td>57,56</td>\n",
|
|
" <td>68,81</td>\n",
|
|
" <td>7,24</td>\n",
|
|
" <td>75,05</td>\n",
|
|
" <td>7149285.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>27</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Tapanuli Utara</td>\n",
|
|
" <td>9,72</td>\n",
|
|
" <td>9,99</td>\n",
|
|
" <td>11710.0</td>\n",
|
|
" <td>73,76</td>\n",
|
|
" <td>68,76</td>\n",
|
|
" <td>83,79</td>\n",
|
|
" <td>89,06</td>\n",
|
|
" <td>1,54</td>\n",
|
|
" <td>82,63</td>\n",
|
|
" <td>6058350.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>28</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Toba Samosir</td>\n",
|
|
" <td>8,99</td>\n",
|
|
" <td>10,57</td>\n",
|
|
" <td>12224.0</td>\n",
|
|
" <td>75,39</td>\n",
|
|
" <td>70,29</td>\n",
|
|
" <td>89,54</td>\n",
|
|
" <td>95,04</td>\n",
|
|
" <td>0,83</td>\n",
|
|
" <td>80,38</td>\n",
|
|
" <td>5649103.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>29</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Labuhan Batu</td>\n",
|
|
" <td>8,74</td>\n",
|
|
" <td>9,25</td>\n",
|
|
" <td>11212.0</td>\n",
|
|
" <td>72,09</td>\n",
|
|
" <td>69,95</td>\n",
|
|
" <td>81,50</td>\n",
|
|
" <td>94,34</td>\n",
|
|
" <td>5,66</td>\n",
|
|
" <td>61,84</td>\n",
|
|
" <td>24147560.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>30</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Asahan</td>\n",
|
|
" <td>9,35</td>\n",
|
|
" <td>8,8</td>\n",
|
|
" <td>11030.0</td>\n",
|
|
" <td>70,49</td>\n",
|
|
" <td>68,37</td>\n",
|
|
" <td>89,09</td>\n",
|
|
" <td>95,78</td>\n",
|
|
" <td>6,39</td>\n",
|
|
" <td>63,02</td>\n",
|
|
" <td>27279586.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>31</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Simalungun</td>\n",
|
|
" <td>8,81</td>\n",
|
|
" <td>9,61</td>\n",
|
|
" <td>11376.0</td>\n",
|
|
" <td>73,4</td>\n",
|
|
" <td>71,37</td>\n",
|
|
" <td>91,75</td>\n",
|
|
" <td>99,74</td>\n",
|
|
" <td>4,17</td>\n",
|
|
" <td>72,55</td>\n",
|
|
" <td>28648781.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>32</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Dairi</td>\n",
|
|
" <td>8,31</td>\n",
|
|
" <td>9,59</td>\n",
|
|
" <td>10504.0</td>\n",
|
|
" <td>71,84</td>\n",
|
|
" <td>69,19</td>\n",
|
|
" <td>92,35</td>\n",
|
|
" <td>91,90</td>\n",
|
|
" <td>1,49</td>\n",
|
|
" <td>85,73</td>\n",
|
|
" <td>6641674.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>33</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Karo</td>\n",
|
|
" <td>8,79</td>\n",
|
|
" <td>10</td>\n",
|
|
" <td>12412.0</td>\n",
|
|
" <td>74,83</td>\n",
|
|
" <td>71,58</td>\n",
|
|
" <td>84,33</td>\n",
|
|
" <td>91,43</td>\n",
|
|
" <td>1,95</td>\n",
|
|
" <td>84,56</td>\n",
|
|
" <td>14582333.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>34</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Deli Serdang</td>\n",
|
|
" <td>4,01</td>\n",
|
|
" <td>10,1</td>\n",
|
|
" <td>12291.0</td>\n",
|
|
" <td>75,53</td>\n",
|
|
" <td>71,77</td>\n",
|
|
" <td>96,37</td>\n",
|
|
" <td>98,18</td>\n",
|
|
" <td>9,13</td>\n",
|
|
" <td>66,78</td>\n",
|
|
" <td>72173623.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>35</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Langkat</td>\n",
|
|
" <td>10,12</td>\n",
|
|
" <td>8,66</td>\n",
|
|
" <td>11142.0</td>\n",
|
|
" <td>71,35</td>\n",
|
|
" <td>68,97</td>\n",
|
|
" <td>80,76</td>\n",
|
|
" <td>92,51</td>\n",
|
|
" <td>5,12</td>\n",
|
|
" <td>69,12</td>\n",
|
|
" <td>30247389.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>36</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Nias Selatan</td>\n",
|
|
" <td>16,92</td>\n",
|
|
" <td>6,06</td>\n",
|
|
" <td>7041.0</td>\n",
|
|
" <td>62,35</td>\n",
|
|
" <td>68,86</td>\n",
|
|
" <td>13,14</td>\n",
|
|
" <td>66,21</td>\n",
|
|
" <td>3,91</td>\n",
|
|
" <td>72,25</td>\n",
|
|
" <td>4332626.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>37</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Humbang Hasundutan</td>\n",
|
|
" <td>9,65</td>\n",
|
|
" <td>9,71</td>\n",
|
|
" <td>8016.0</td>\n",
|
|
" <td>69,41</td>\n",
|
|
" <td>69,51</td>\n",
|
|
" <td>91,65</td>\n",
|
|
" <td>91,95</td>\n",
|
|
" <td>1,94</td>\n",
|
|
" <td>84,17</td>\n",
|
|
" <td>4219138.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>38</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Pakpak Bharat</td>\n",
|
|
" <td>9,35</td>\n",
|
|
" <td>9,14</td>\n",
|
|
" <td>8254.0</td>\n",
|
|
" <td>67,94</td>\n",
|
|
" <td>65,96</td>\n",
|
|
" <td>90,14</td>\n",
|
|
" <td>70,69</td>\n",
|
|
" <td>1,36</td>\n",
|
|
" <td>87,70</td>\n",
|
|
" <td>872403.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>39</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Samosir</td>\n",
|
|
" <td>12,68</td>\n",
|
|
" <td>9,44</td>\n",
|
|
" <td>8504.0</td>\n",
|
|
" <td>70,83</td>\n",
|
|
" <td>71,41</td>\n",
|
|
" <td>91,09</td>\n",
|
|
" <td>65,64</td>\n",
|
|
" <td>0,70</td>\n",
|
|
" <td>84,38</td>\n",
|
|
" <td>3162101.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>40</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Serdang Bedagai</td>\n",
|
|
" <td>8,3</td>\n",
|
|
" <td>8,69</td>\n",
|
|
" <td>11017.0</td>\n",
|
|
" <td>70,56</td>\n",
|
|
" <td>68,82</td>\n",
|
|
" <td>93,19</td>\n",
|
|
" <td>98,14</td>\n",
|
|
" <td>3,93</td>\n",
|
|
" <td>66,75</td>\n",
|
|
" <td>19863243.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>41</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Batu Bara</td>\n",
|
|
" <td>12,38</td>\n",
|
|
" <td>8,07</td>\n",
|
|
" <td>10539.0</td>\n",
|
|
" <td>68,58</td>\n",
|
|
" <td>67,13</td>\n",
|
|
" <td>88,04</td>\n",
|
|
" <td>97,83</td>\n",
|
|
" <td>6,62</td>\n",
|
|
" <td>70,00</td>\n",
|
|
" <td>24486056.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>42</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Padang Lawas Utara</td>\n",
|
|
" <td>9,92</td>\n",
|
|
" <td>9,38</td>\n",
|
|
" <td>10055.0</td>\n",
|
|
" <td>70,11</td>\n",
|
|
" <td>67,22</td>\n",
|
|
" <td>67,17</td>\n",
|
|
" <td>77,58</td>\n",
|
|
" <td>3,19</td>\n",
|
|
" <td>76,82</td>\n",
|
|
" <td>8593639.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>43</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Padang Lawas</td>\n",
|
|
" <td>8,69</td>\n",
|
|
" <td>9,02</td>\n",
|
|
" <td>8921.0</td>\n",
|
|
" <td>68,64</td>\n",
|
|
" <td>67,13</td>\n",
|
|
" <td>59,62</td>\n",
|
|
" <td>77,84</td>\n",
|
|
" <td>4,07</td>\n",
|
|
" <td>75,23</td>\n",
|
|
" <td>8362135.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>44</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Labuhan Batu Selatan</td>\n",
|
|
" <td>8,53</td>\n",
|
|
" <td>8,9</td>\n",
|
|
" <td>11562.0</td>\n",
|
|
" <td>71,69</td>\n",
|
|
" <td>68,81</td>\n",
|
|
" <td>84,85</td>\n",
|
|
" <td>84,66</td>\n",
|
|
" <td>4,71</td>\n",
|
|
" <td>66,38</td>\n",
|
|
" <td>19620408.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>45</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Labuhan Batu Utara</td>\n",
|
|
" <td>10,02</td>\n",
|
|
" <td>8,41</td>\n",
|
|
" <td>11840.0</td>\n",
|
|
" <td>71,87</td>\n",
|
|
" <td>69,56</td>\n",
|
|
" <td>79,75</td>\n",
|
|
" <td>86,75</td>\n",
|
|
" <td>5,74</td>\n",
|
|
" <td>65,73</td>\n",
|
|
" <td>17969257.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>46</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Nias Utara</td>\n",
|
|
" <td>25,66</td>\n",
|
|
" <td>6,77</td>\n",
|
|
" <td>6155.0</td>\n",
|
|
" <td>62,82</td>\n",
|
|
" <td>69,55</td>\n",
|
|
" <td>46,09</td>\n",
|
|
" <td>58,17</td>\n",
|
|
" <td>3,00</td>\n",
|
|
" <td>74,27</td>\n",
|
|
" <td>2417196.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>47</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Nias Barat</td>\n",
|
|
" <td>26,42</td>\n",
|
|
" <td>6,69</td>\n",
|
|
" <td>5924.0</td>\n",
|
|
" <td>61,99</td>\n",
|
|
" <td>69,08</td>\n",
|
|
" <td>38,02</td>\n",
|
|
" <td>71,52</td>\n",
|
|
" <td>0,74</td>\n",
|
|
" <td>82,08</td>\n",
|
|
" <td>1285664.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>48</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Kota Sibolga</td>\n",
|
|
" <td>12,33</td>\n",
|
|
" <td>10,41</td>\n",
|
|
" <td>11540.0</td>\n",
|
|
" <td>73,94</td>\n",
|
|
" <td>69,25</td>\n",
|
|
" <td>32,33</td>\n",
|
|
" <td>92,40</td>\n",
|
|
" <td>8,72</td>\n",
|
|
" <td>71,19</td>\n",
|
|
" <td>3595704.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>49</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Kota Tanjung Balai</td>\n",
|
|
" <td>13,4</td>\n",
|
|
" <td>9,45</td>\n",
|
|
" <td>11225.0</td>\n",
|
|
" <td>68,94</td>\n",
|
|
" <td>63,44</td>\n",
|
|
" <td>89,07</td>\n",
|
|
" <td>87,20</td>\n",
|
|
" <td>6,59</td>\n",
|
|
" <td>66,57</td>\n",
|
|
" <td>5898808.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Kota Pematang Siantar</td>\n",
|
|
" <td>8,52</td>\n",
|
|
" <td>11,29</td>\n",
|
|
" <td>12436.0</td>\n",
|
|
" <td>79,17</td>\n",
|
|
" <td>73,77</td>\n",
|
|
" <td>88,49</td>\n",
|
|
" <td>99,78</td>\n",
|
|
" <td>11,00</td>\n",
|
|
" <td>68,80</td>\n",
|
|
" <td>9547698.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>51</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Kota Tebing Tinggi</td>\n",
|
|
" <td>10,3</td>\n",
|
|
" <td>10,44</td>\n",
|
|
" <td>12939.0</td>\n",
|
|
" <td>75,42</td>\n",
|
|
" <td>70,95</td>\n",
|
|
" <td>95,88</td>\n",
|
|
" <td>99,35</td>\n",
|
|
" <td>8,37</td>\n",
|
|
" <td>67,19</td>\n",
|
|
" <td>4024777.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>52</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Kota Medan</td>\n",
|
|
" <td>8,34</td>\n",
|
|
" <td>11,48</td>\n",
|
|
" <td>14999.0</td>\n",
|
|
" <td>81,21</td>\n",
|
|
" <td>73,23</td>\n",
|
|
" <td>92,71</td>\n",
|
|
" <td>98,80</td>\n",
|
|
" <td>10,81</td>\n",
|
|
" <td>62,16</td>\n",
|
|
" <td>157689187.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>53</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Kota Binjai</td>\n",
|
|
" <td>5,81</td>\n",
|
|
" <td>10,94</td>\n",
|
|
" <td>11063.0</td>\n",
|
|
" <td>76,01</td>\n",
|
|
" <td>72,45</td>\n",
|
|
" <td>95,21</td>\n",
|
|
" <td>99,76</td>\n",
|
|
" <td>7,86</td>\n",
|
|
" <td>62,77</td>\n",
|
|
" <td>8162776.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>54</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Kota Padangsidimpuan</td>\n",
|
|
" <td>7,53</td>\n",
|
|
" <td>11,09</td>\n",
|
|
" <td>10965.0</td>\n",
|
|
" <td>75,48</td>\n",
|
|
" <td>69,5</td>\n",
|
|
" <td>51,33</td>\n",
|
|
" <td>54,13</td>\n",
|
|
" <td>7,18</td>\n",
|
|
" <td>68,69</td>\n",
|
|
" <td>4346777.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>55</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>Kota Gunungsitoli</td>\n",
|
|
" <td>16,45</td>\n",
|
|
" <td>8,62</td>\n",
|
|
" <td>8134.0</td>\n",
|
|
" <td>69,61</td>\n",
|
|
" <td>71,32</td>\n",
|
|
" <td>45,13</td>\n",
|
|
" <td>74,11</td>\n",
|
|
" <td>4,80</td>\n",
|
|
" <td>62,95</td>\n",
|
|
" <td>3519128.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>56</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>Kepulauan Mentawai</td>\n",
|
|
" <td>14,84</td>\n",
|
|
" <td>7,2</td>\n",
|
|
" <td>6321.0</td>\n",
|
|
" <td>61,35</td>\n",
|
|
" <td>64,73</td>\n",
|
|
" <td>71,05</td>\n",
|
|
" <td>55,46</td>\n",
|
|
" <td>2,79</td>\n",
|
|
" <td>82,57</td>\n",
|
|
" <td>3041549.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>57</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>Pesisir Selatan</td>\n",
|
|
" <td>7,92</td>\n",
|
|
" <td>8,27</td>\n",
|
|
" <td>9270.0</td>\n",
|
|
" <td>70,03</td>\n",
|
|
" <td>70,96</td>\n",
|
|
" <td>71,40</td>\n",
|
|
" <td>76,23</td>\n",
|
|
" <td>5,97</td>\n",
|
|
" <td>66,59</td>\n",
|
|
" <td>9790360.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>58</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>Solok</td>\n",
|
|
" <td>8,01</td>\n",
|
|
" <td>7,87</td>\n",
|
|
" <td>10215.0</td>\n",
|
|
" <td>69,24</td>\n",
|
|
" <td>68,79</td>\n",
|
|
" <td>47,63</td>\n",
|
|
" <td>78,09</td>\n",
|
|
" <td>4,67</td>\n",
|
|
" <td>71,21</td>\n",
|
|
" <td>10119822.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>59</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>Sijunjung</td>\n",
|
|
" <td>6,8</td>\n",
|
|
" <td>8,12</td>\n",
|
|
" <td>10389.0</td>\n",
|
|
" <td>67,86</td>\n",
|
|
" <td>66,36</td>\n",
|
|
" <td>72,63</td>\n",
|
|
" <td>64,12</td>\n",
|
|
" <td>3,57</td>\n",
|
|
" <td>70,06</td>\n",
|
|
" <td>6893214.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>60</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>Tanah Datar</td>\n",
|
|
" <td>4,54</td>\n",
|
|
" <td>8,62</td>\n",
|
|
" <td>10616.0</td>\n",
|
|
" <td>72,46</td>\n",
|
|
" <td>70,12</td>\n",
|
|
" <td>53,73</td>\n",
|
|
" <td>86,50</td>\n",
|
|
" <td>4,63</td>\n",
|
|
" <td>66,88</td>\n",
|
|
" <td>9891020.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>61</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>Padang Pariaman</td>\n",
|
|
" <td>7,22</td>\n",
|
|
" <td>7,88</td>\n",
|
|
" <td>11050.0</td>\n",
|
|
" <td>70,76</td>\n",
|
|
" <td>68,97</td>\n",
|
|
" <td>55,86</td>\n",
|
|
" <td>86,09</td>\n",
|
|
" <td>8,41</td>\n",
|
|
" <td>64,64</td>\n",
|
|
" <td>12199848.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>62</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>Agam</td>\n",
|
|
" <td>6,85</td>\n",
|
|
" <td>8,97</td>\n",
|
|
" <td>9662.0</td>\n",
|
|
" <td>72,57</td>\n",
|
|
" <td>72,53</td>\n",
|
|
" <td>77,25</td>\n",
|
|
" <td>87,46</td>\n",
|
|
" <td>5,06</td>\n",
|
|
" <td>66,49</td>\n",
|
|
" <td>14939509.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>63</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>Lima Puluh Kota</td>\n",
|
|
" <td>7,29</td>\n",
|
|
" <td>8,07</td>\n",
|
|
" <td>9668.0</td>\n",
|
|
" <td>69,68</td>\n",
|
|
" <td>69,84</td>\n",
|
|
" <td>55,69</td>\n",
|
|
" <td>68,93</td>\n",
|
|
" <td>2,25</td>\n",
|
|
" <td>71,33</td>\n",
|
|
" <td>11430548.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>64</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>Pasaman</td>\n",
|
|
" <td>7,48</td>\n",
|
|
" <td>8,1</td>\n",
|
|
" <td>8440.0</td>\n",
|
|
" <td>66,77</td>\n",
|
|
" <td>67,59</td>\n",
|
|
" <td>45,26</td>\n",
|
|
" <td>82,57</td>\n",
|
|
" <td>4,92</td>\n",
|
|
" <td>69,35</td>\n",
|
|
" <td>6330067.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>65</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>Solok Selatan</td>\n",
|
|
" <td>7,52</td>\n",
|
|
" <td>8,32</td>\n",
|
|
" <td>10367.0</td>\n",
|
|
" <td>69,23</td>\n",
|
|
" <td>68,01</td>\n",
|
|
" <td>58,57</td>\n",
|
|
" <td>81,01</td>\n",
|
|
" <td>4,84</td>\n",
|
|
" <td>72,11</td>\n",
|
|
" <td>4059515.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>66</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>Dharmasraya</td>\n",
|
|
" <td>6,67</td>\n",
|
|
" <td>8,55</td>\n",
|
|
" <td>11324.0</td>\n",
|
|
" <td>71,76</td>\n",
|
|
" <td>71,53</td>\n",
|
|
" <td>82,71</td>\n",
|
|
" <td>70,27</td>\n",
|
|
" <td>5,00</td>\n",
|
|
" <td>73,04</td>\n",
|
|
" <td>7709700.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>67</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>Pasaman Barat</td>\n",
|
|
" <td>7,51</td>\n",
|
|
" <td>8,27</td>\n",
|
|
" <td>9089.0</td>\n",
|
|
" <td>68,76</td>\n",
|
|
" <td>67,94</td>\n",
|
|
" <td>70,28</td>\n",
|
|
" <td>81,47</td>\n",
|
|
" <td>5,02</td>\n",
|
|
" <td>66,93</td>\n",
|
|
" <td>11682234.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>68</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>Kota Padang</td>\n",
|
|
" <td>4,94</td>\n",
|
|
" <td>11,59</td>\n",
|
|
" <td>14540.0</td>\n",
|
|
" <td>82,9</td>\n",
|
|
" <td>73,69</td>\n",
|
|
" <td>80,13</td>\n",
|
|
" <td>95,52</td>\n",
|
|
" <td>13,37</td>\n",
|
|
" <td>63,78</td>\n",
|
|
" <td>45227957.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>69</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>Kota Solok</td>\n",
|
|
" <td>3,12</td>\n",
|
|
" <td>11,04</td>\n",
|
|
" <td>12168.0</td>\n",
|
|
" <td>78,41</td>\n",
|
|
" <td>73,73</td>\n",
|
|
" <td>91,40</td>\n",
|
|
" <td>96,55</td>\n",
|
|
" <td>5,15</td>\n",
|
|
" <td>66,51</td>\n",
|
|
" <td>2936828.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>70</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>Kota Sawah Lunto</td>\n",
|
|
" <td>2,38</td>\n",
|
|
" <td>10,32</td>\n",
|
|
" <td>10195.0</td>\n",
|
|
" <td>72,88</td>\n",
|
|
" <td>70,1</td>\n",
|
|
" <td>86,45</td>\n",
|
|
" <td>88,27</td>\n",
|
|
" <td>6,38</td>\n",
|
|
" <td>68,05</td>\n",
|
|
" <td>2829590.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>71</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>Kota Padang Panjang</td>\n",
|
|
" <td>5,92</td>\n",
|
|
" <td>11,63</td>\n",
|
|
" <td>10754.0</td>\n",
|
|
" <td>77,97</td>\n",
|
|
" <td>72,82</td>\n",
|
|
" <td>73,04</td>\n",
|
|
" <td>98,45</td>\n",
|
|
" <td>4,90</td>\n",
|
|
" <td>65,94</td>\n",
|
|
" <td>2631518.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>72</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>Kota Bukittinggi</td>\n",
|
|
" <td>5,14</td>\n",
|
|
" <td>11.34</td>\n",
|
|
" <td>13331.0</td>\n",
|
|
" <td>80,7</td>\n",
|
|
" <td>74,5</td>\n",
|
|
" <td>89,93</td>\n",
|
|
" <td>97,05</td>\n",
|
|
" <td>6,09</td>\n",
|
|
" <td>67,42</td>\n",
|
|
" <td>6263130.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>73</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>Kota Payakumbuh</td>\n",
|
|
" <td>6,16</td>\n",
|
|
" <td>10.81</td>\n",
|
|
" <td>13317.0</td>\n",
|
|
" <td>79,08</td>\n",
|
|
" <td>73,84</td>\n",
|
|
" <td>89,49</td>\n",
|
|
" <td>99,43</td>\n",
|
|
" <td>6,47</td>\n",
|
|
" <td>71,73</td>\n",
|
|
" <td>4571927.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>74</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>Kota Pariaman</td>\n",
|
|
" <td>4,38</td>\n",
|
|
" <td>10.67</td>\n",
|
|
" <td>12818.0</td>\n",
|
|
" <td>77,07</td>\n",
|
|
" <td>70,38</td>\n",
|
|
" <td>79,56</td>\n",
|
|
" <td>97,69</td>\n",
|
|
" <td>6,09</td>\n",
|
|
" <td>62,70</td>\n",
|
|
" <td>3669629.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>75</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>Kuantan Singingi</td>\n",
|
|
" <td>8.97</td>\n",
|
|
" <td>8.75</td>\n",
|
|
" <td>10309.0</td>\n",
|
|
" <td>70,6</td>\n",
|
|
" <td>68,6</td>\n",
|
|
" <td>87,98</td>\n",
|
|
" <td>85,78</td>\n",
|
|
" <td>2,06</td>\n",
|
|
" <td>67,02</td>\n",
|
|
" <td>24689509.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>76</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>Indragiri Hulu</td>\n",
|
|
" <td>6.18</td>\n",
|
|
" <td>8.39</td>\n",
|
|
" <td>10260.0</td>\n",
|
|
" <td>70,01</td>\n",
|
|
" <td>70,26</td>\n",
|
|
" <td>82,82</td>\n",
|
|
" <td>64,84</td>\n",
|
|
" <td>3,32</td>\n",
|
|
" <td>68,45</td>\n",
|
|
" <td>31176538.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>77</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>Indragiri Hilir</td>\n",
|
|
" <td>6,18</td>\n",
|
|
" <td>7,24</td>\n",
|
|
" <td>9945.0</td>\n",
|
|
" <td>66,63</td>\n",
|
|
" <td>67,98</td>\n",
|
|
" <td>49,64</td>\n",
|
|
" <td>89,60</td>\n",
|
|
" <td>2,66</td>\n",
|
|
" <td>68,56</td>\n",
|
|
" <td>46921264.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>78</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>Pelalawan</td>\n",
|
|
" <td>9,63</td>\n",
|
|
" <td>8,7</td>\n",
|
|
" <td>11672.0</td>\n",
|
|
" <td>72,08</td>\n",
|
|
" <td>71,24</td>\n",
|
|
" <td>81,13</td>\n",
|
|
" <td>83,61</td>\n",
|
|
" <td>2,34</td>\n",
|
|
" <td>69,18</td>\n",
|
|
" <td>36538811.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>79</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>Siak</td>\n",
|
|
" <td>5,18</td>\n",
|
|
" <td>9,86</td>\n",
|
|
" <td>11807.0</td>\n",
|
|
" <td>73,98</td>\n",
|
|
" <td>71,13</td>\n",
|
|
" <td>95,41</td>\n",
|
|
" <td>93,13</td>\n",
|
|
" <td>4,34</td>\n",
|
|
" <td>64,69</td>\n",
|
|
" <td>54543286.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>80</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>Kampar</td>\n",
|
|
" <td>7,82</td>\n",
|
|
" <td>9,27</td>\n",
|
|
" <td>10858.0</td>\n",
|
|
" <td>73,02</td>\n",
|
|
" <td>70,83</td>\n",
|
|
" <td>89,62</td>\n",
|
|
" <td>90,41</td>\n",
|
|
" <td>4,27</td>\n",
|
|
" <td>63,06</td>\n",
|
|
" <td>53196375.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>81</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>Rokan Hulu</td>\n",
|
|
" <td>10,4</td>\n",
|
|
" <td>8,54</td>\n",
|
|
" <td>9406.0</td>\n",
|
|
" <td>69,67</td>\n",
|
|
" <td>70,18</td>\n",
|
|
" <td>88,85</td>\n",
|
|
" <td>92,17</td>\n",
|
|
" <td>2,25</td>\n",
|
|
" <td>66,50</td>\n",
|
|
" <td>26752468.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>82</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>Bengkalis</td>\n",
|
|
" <td>6,64</td>\n",
|
|
" <td>9,7</td>\n",
|
|
" <td>11415.0</td>\n",
|
|
" <td>73,58</td>\n",
|
|
" <td>71,24</td>\n",
|
|
" <td>90,34</td>\n",
|
|
" <td>93,26</td>\n",
|
|
" <td>6,63</td>\n",
|
|
" <td>66,76</td>\n",
|
|
" <td>74229737.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>83</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>Rokan Hilir</td>\n",
|
|
" <td>7,18</td>\n",
|
|
" <td>8,26</td>\n",
|
|
" <td>9417.0</td>\n",
|
|
" <td>69,34</td>\n",
|
|
" <td>70,39</td>\n",
|
|
" <td>73,94</td>\n",
|
|
" <td>87,52</td>\n",
|
|
" <td>3,25</td>\n",
|
|
" <td>60,74</td>\n",
|
|
" <td>46761280.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>84</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>Kepulauan Meranti</td>\n",
|
|
" <td>25,68</td>\n",
|
|
" <td>7,84</td>\n",
|
|
" <td>7780.0</td>\n",
|
|
" <td>65,7</td>\n",
|
|
" <td>67,78</td>\n",
|
|
" <td>55,98</td>\n",
|
|
" <td>88,69</td>\n",
|
|
" <td>4,43</td>\n",
|
|
" <td>65,60</td>\n",
|
|
" <td>13008808.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>85</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>Kota Pekanbaru</td>\n",
|
|
" <td>2,83</td>\n",
|
|
" <td>11,92</td>\n",
|
|
" <td>14360.0</td>\n",
|
|
" <td>81,58</td>\n",
|
|
" <td>72,41</td>\n",
|
|
" <td>96,28</td>\n",
|
|
" <td>98,76</td>\n",
|
|
" <td>8,29</td>\n",
|
|
" <td>61,61</td>\n",
|
|
" <td>72619083.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>86</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>Kota Dumai</td>\n",
|
|
" <td>3,42</td>\n",
|
|
" <td>10,14</td>\n",
|
|
" <td>11818.0</td>\n",
|
|
" <td>74,75</td>\n",
|
|
" <td>70,98</td>\n",
|
|
" <td>97,03</td>\n",
|
|
" <td>92,86</td>\n",
|
|
" <td>6,29</td>\n",
|
|
" <td>64,91</td>\n",
|
|
" <td>26068578.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>87</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>Kerinci</td>\n",
|
|
" <td>7,71</td>\n",
|
|
" <td>8,56</td>\n",
|
|
" <td>10184.0</td>\n",
|
|
" <td>71,45</td>\n",
|
|
" <td>70</td>\n",
|
|
" <td>66,32</td>\n",
|
|
" <td>74,59</td>\n",
|
|
" <td>2,32</td>\n",
|
|
" <td>70,90</td>\n",
|
|
" <td>6844238.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>88</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>Merangin</td>\n",
|
|
" <td>9,11</td>\n",
|
|
" <td>7,9</td>\n",
|
|
" <td>10380.0</td>\n",
|
|
" <td>69,53</td>\n",
|
|
" <td>71,29</td>\n",
|
|
" <td>71,07</td>\n",
|
|
" <td>68,91</td>\n",
|
|
" <td>4,83</td>\n",
|
|
" <td>69,85</td>\n",
|
|
" <td>10371678.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>89</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>Sarolangun</td>\n",
|
|
" <td>8,87</td>\n",
|
|
" <td>8,04</td>\n",
|
|
" <td>11792.0</td>\n",
|
|
" <td>70,25</td>\n",
|
|
" <td>69,21</td>\n",
|
|
" <td>76,03</td>\n",
|
|
" <td>69,96</td>\n",
|
|
" <td>5,52</td>\n",
|
|
" <td>64,86</td>\n",
|
|
" <td>11397733.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>90</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>Batang Hari</td>\n",
|
|
" <td>10,05</td>\n",
|
|
" <td>8,12</td>\n",
|
|
" <td>10032.0</td>\n",
|
|
" <td>70,11</td>\n",
|
|
" <td>70,64</td>\n",
|
|
" <td>79,68</td>\n",
|
|
" <td>73,08</td>\n",
|
|
" <td>4,26</td>\n",
|
|
" <td>68,81</td>\n",
|
|
" <td>12221193.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>91</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>Muaro Jambi</td>\n",
|
|
" <td>4,53</td>\n",
|
|
" <td>8,58</td>\n",
|
|
" <td>8825.0</td>\n",
|
|
" <td>69,55</td>\n",
|
|
" <td>71,32</td>\n",
|
|
" <td>89,17</td>\n",
|
|
" <td>80,66</td>\n",
|
|
" <td>5,59</td>\n",
|
|
" <td>62,78</td>\n",
|
|
" <td>16847013.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>92</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>Tanjung Jabung Timur</td>\n",
|
|
" <td>11,39</td>\n",
|
|
" <td>6,92</td>\n",
|
|
" <td>9163.0</td>\n",
|
|
" <td>64,91</td>\n",
|
|
" <td>66,34</td>\n",
|
|
" <td>70,27</td>\n",
|
|
" <td>85,91</td>\n",
|
|
" <td>1,56</td>\n",
|
|
" <td>71,22</td>\n",
|
|
" <td>17284926.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>93</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>Tanjung Jabung Barat</td>\n",
|
|
" <td>10,75</td>\n",
|
|
" <td>8</td>\n",
|
|
" <td>9699.0</td>\n",
|
|
" <td>68,16</td>\n",
|
|
" <td>68,17</td>\n",
|
|
" <td>81,89</td>\n",
|
|
" <td>91,76</td>\n",
|
|
" <td>2,53</td>\n",
|
|
" <td>73,89</td>\n",
|
|
" <td>30976199.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>94</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>Tebo</td>\n",
|
|
" <td>6,68</td>\n",
|
|
" <td>7,59</td>\n",
|
|
" <td>10546.0</td>\n",
|
|
" <td>69,35</td>\n",
|
|
" <td>70,02</td>\n",
|
|
" <td>83,38</td>\n",
|
|
" <td>69,97</td>\n",
|
|
" <td>2,83</td>\n",
|
|
" <td>70,65</td>\n",
|
|
" <td>10597493.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>95</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>Bungo</td>\n",
|
|
" <td>6,23</td>\n",
|
|
" <td>8,28</td>\n",
|
|
" <td>11670.0</td>\n",
|
|
" <td>70,15</td>\n",
|
|
" <td>67,83</td>\n",
|
|
" <td>77,58</td>\n",
|
|
" <td>73,63</td>\n",
|
|
" <td>5,86</td>\n",
|
|
" <td>63,58</td>\n",
|
|
" <td>13133523.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>96</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>Kota Jambi</td>\n",
|
|
" <td>9,02</td>\n",
|
|
" <td>11,2</td>\n",
|
|
" <td>12240.0</td>\n",
|
|
" <td>79,12</td>\n",
|
|
" <td>72,71</td>\n",
|
|
" <td>93,22</td>\n",
|
|
" <td>95,83</td>\n",
|
|
" <td>10,66</td>\n",
|
|
" <td>63,12</td>\n",
|
|
" <td>19515486.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>97</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>Kota Sungai Penuh</td>\n",
|
|
" <td>3,41</td>\n",
|
|
" <td>10,33</td>\n",
|
|
" <td>10454.0</td>\n",
|
|
" <td>75,7</td>\n",
|
|
" <td>72,21</td>\n",
|
|
" <td>74,04</td>\n",
|
|
" <td>90,55</td>\n",
|
|
" <td>3,00</td>\n",
|
|
" <td>64,92</td>\n",
|
|
" <td>4768840.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>98</th>\n",
|
|
" <td>SUMATERA SELATAN</td>\n",
|
|
" <td>Ogan Komering Ulu</td>\n",
|
|
" <td>12,62</td>\n",
|
|
" <td>8,71</td>\n",
|
|
" <td>10040.0</td>\n",
|
|
" <td>69,6</td>\n",
|
|
" <td>68,24</td>\n",
|
|
" <td>82,72</td>\n",
|
|
" <td>81,78</td>\n",
|
|
" <td>4,57</td>\n",
|
|
" <td>69,96</td>\n",
|
|
" <td>10114558.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>99</th>\n",
|
|
" <td>SUMATERA SELATAN</td>\n",
|
|
" <td>Ogan Komering Ilir</td>\n",
|
|
" <td>14,68</td>\n",
|
|
" <td>7,05</td>\n",
|
|
" <td>10755.0</td>\n",
|
|
" <td>67,17</td>\n",
|
|
" <td>68,67</td>\n",
|
|
" <td>68,09</td>\n",
|
|
" <td>79,02</td>\n",
|
|
" <td>3,01</td>\n",
|
|
" <td>69,68</td>\n",
|
|
" <td>20909479.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>\n",
|
|
" <div class=\"colab-df-buttons\">\n",
|
|
"\n",
|
|
" <div class=\"colab-df-container\">\n",
|
|
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec77df7d-60e2-4bb3-9aec-7f383de26377')\"\n",
|
|
" title=\"Convert this dataframe to an interactive table.\"\n",
|
|
" style=\"display:none;\">\n",
|
|
"\n",
|
|
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
|
|
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
|
|
" </svg>\n",
|
|
" </button>\n",
|
|
"\n",
|
|
" <style>\n",
|
|
" .colab-df-container {\n",
|
|
" display:flex;\n",
|
|
" gap: 12px;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .colab-df-convert {\n",
|
|
" background-color: #E8F0FE;\n",
|
|
" border: none;\n",
|
|
" border-radius: 50%;\n",
|
|
" cursor: pointer;\n",
|
|
" display: none;\n",
|
|
" fill: #1967D2;\n",
|
|
" height: 32px;\n",
|
|
" padding: 0 0 0 0;\n",
|
|
" width: 32px;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .colab-df-convert:hover {\n",
|
|
" background-color: #E2EBFA;\n",
|
|
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
|
" fill: #174EA6;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .colab-df-buttons div {\n",
|
|
" margin-bottom: 4px;\n",
|
|
" }\n",
|
|
"\n",
|
|
" [theme=dark] .colab-df-convert {\n",
|
|
" background-color: #3B4455;\n",
|
|
" fill: #D2E3FC;\n",
|
|
" }\n",
|
|
"\n",
|
|
" [theme=dark] .colab-df-convert:hover {\n",
|
|
" background-color: #434B5C;\n",
|
|
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
|
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
|
" fill: #FFFFFF;\n",
|
|
" }\n",
|
|
" </style>\n",
|
|
"\n",
|
|
" <script>\n",
|
|
" const buttonEl =\n",
|
|
" document.querySelector('#df-ec77df7d-60e2-4bb3-9aec-7f383de26377 button.colab-df-convert');\n",
|
|
" buttonEl.style.display =\n",
|
|
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
|
"\n",
|
|
" async function convertToInteractive(key) {\n",
|
|
" const element = document.querySelector('#df-ec77df7d-60e2-4bb3-9aec-7f383de26377');\n",
|
|
" const dataTable =\n",
|
|
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
|
" [key], {});\n",
|
|
" if (!dataTable) return;\n",
|
|
"\n",
|
|
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
|
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
|
" + ' to learn more about interactive tables.';\n",
|
|
" element.innerHTML = '';\n",
|
|
" dataTable['output_type'] = 'display_data';\n",
|
|
" await google.colab.output.renderOutput(dataTable, element);\n",
|
|
" const docLink = document.createElement('div');\n",
|
|
" docLink.innerHTML = docLinkHtml;\n",
|
|
" element.appendChild(docLink);\n",
|
|
" }\n",
|
|
" </script>\n",
|
|
" </div>\n",
|
|
"\n",
|
|
"\n",
|
|
" <div id=\"df-6681bfdb-3373-46ad-82a6-731bccb88148\">\n",
|
|
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6681bfdb-3373-46ad-82a6-731bccb88148')\"\n",
|
|
" title=\"Suggest charts\"\n",
|
|
" style=\"display:none;\">\n",
|
|
"\n",
|
|
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
|
|
" width=\"24px\">\n",
|
|
" <g>\n",
|
|
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
|
|
" </g>\n",
|
|
"</svg>\n",
|
|
" </button>\n",
|
|
"\n",
|
|
"<style>\n",
|
|
" .colab-df-quickchart {\n",
|
|
" --bg-color: #E8F0FE;\n",
|
|
" --fill-color: #1967D2;\n",
|
|
" --hover-bg-color: #E2EBFA;\n",
|
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" --hover-fill-color: #174EA6;\n",
|
|
" --disabled-fill-color: #AAA;\n",
|
|
" --disabled-bg-color: #DDD;\n",
|
|
" }\n",
|
|
"\n",
|
|
" [theme=dark] .colab-df-quickchart {\n",
|
|
" --bg-color: #3B4455;\n",
|
|
" --fill-color: #D2E3FC;\n",
|
|
" --hover-bg-color: #434B5C;\n",
|
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" --hover-fill-color: #FFFFFF;\n",
|
|
" --disabled-bg-color: #3B4455;\n",
|
|
" --disabled-fill-color: #666;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .colab-df-quickchart {\n",
|
|
" background-color: var(--bg-color);\n",
|
|
" border: none;\n",
|
|
" border-radius: 50%;\n",
|
|
" cursor: pointer;\n",
|
|
" display: none;\n",
|
|
" fill: var(--fill-color);\n",
|
|
" height: 32px;\n",
|
|
" padding: 0;\n",
|
|
" width: 32px;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .colab-df-quickchart:hover {\n",
|
|
" background-color: var(--hover-bg-color);\n",
|
|
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
|
" fill: var(--button-hover-fill-color);\n",
|
|
" }\n",
|
|
"\n",
|
|
" .colab-df-quickchart-complete:disabled,\n",
|
|
" .colab-df-quickchart-complete:disabled:hover {\n",
|
|
" background-color: var(--disabled-bg-color);\n",
|
|
" fill: var(--disabled-fill-color);\n",
|
|
" box-shadow: none;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .colab-df-spinner {\n",
|
|
" border: 2px solid var(--fill-color);\n",
|
|
" border-color: transparent;\n",
|
|
" border-bottom-color: var(--fill-color);\n",
|
|
" animation:\n",
|
|
" spin 1s steps(1) infinite;\n",
|
|
" }\n",
|
|
"\n",
|
|
" @keyframes spin {\n",
|
|
" 0% {\n",
|
|
" border-color: transparent;\n",
|
|
" border-bottom-color: var(--fill-color);\n",
|
|
" border-left-color: var(--fill-color);\n",
|
|
" }\n",
|
|
" 20% {\n",
|
|
" border-color: transparent;\n",
|
|
" border-left-color: var(--fill-color);\n",
|
|
" border-top-color: var(--fill-color);\n",
|
|
" }\n",
|
|
" 30% {\n",
|
|
" border-color: transparent;\n",
|
|
" border-left-color: var(--fill-color);\n",
|
|
" border-top-color: var(--fill-color);\n",
|
|
" border-right-color: var(--fill-color);\n",
|
|
" }\n",
|
|
" 40% {\n",
|
|
" border-color: transparent;\n",
|
|
" border-right-color: var(--fill-color);\n",
|
|
" border-top-color: var(--fill-color);\n",
|
|
" }\n",
|
|
" 60% {\n",
|
|
" border-color: transparent;\n",
|
|
" border-right-color: var(--fill-color);\n",
|
|
" }\n",
|
|
" 80% {\n",
|
|
" border-color: transparent;\n",
|
|
" border-right-color: var(--fill-color);\n",
|
|
" border-bottom-color: var(--fill-color);\n",
|
|
" }\n",
|
|
" 90% {\n",
|
|
" border-color: transparent;\n",
|
|
" border-bottom-color: var(--fill-color);\n",
|
|
" }\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"\n",
|
|
" <script>\n",
|
|
" async function quickchart(key) {\n",
|
|
" const quickchartButtonEl =\n",
|
|
" document.querySelector('#' + key + ' button');\n",
|
|
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
|
|
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
|
|
" try {\n",
|
|
" const charts = await google.colab.kernel.invokeFunction(\n",
|
|
" 'suggestCharts', [key], {});\n",
|
|
" } catch (error) {\n",
|
|
" console.error('Error during call to suggestCharts:', error);\n",
|
|
" }\n",
|
|
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
|
|
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
|
|
" }\n",
|
|
" (() => {\n",
|
|
" let quickchartButtonEl =\n",
|
|
" document.querySelector('#df-6681bfdb-3373-46ad-82a6-731bccb88148 button');\n",
|
|
" quickchartButtonEl.style.display =\n",
|
|
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
|
" })();\n",
|
|
" </script>\n",
|
|
" </div>\n",
|
|
"\n",
|
|
" </div>\n",
|
|
" </div>\n"
|
|
],
|
|
"application/vnd.google.colaboratory.intrinsic+json": {
|
|
"type": "dataframe",
|
|
"variable_name": "df",
|
|
"summary": "{\n \"name\": \"df\",\n \"rows\": 999,\n \"fields\": [\n {\n \"column\": \"Provinsi\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 34,\n \"samples\": [\n \"BANTEN\",\n \"KALIMANTAN BARAT\",\n \"SULAWESI TENGGARA\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Kab/Kota\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 514,\n \"samples\": [\n \"Manggarai\",\n \"Yahukimo\",\n \"Gorontalo\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Persentase Penduduk Miskin (P0) Menurut Kabupaten/Kota (Persen)\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 452,\n \"samples\": [\n \"5,91\",\n \"8,3\",\n \"12,85\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Rata-rata Lama Sekolah Penduduk 15+ (Tahun)\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 377,\n \"samples\": [\n \"10,91\",\n \"7,12\",\n \"6,02\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Pengeluaran per Kapita Disesuaikan (Ribu Rupiah/Orang/Tahun)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2717.144185738409,\n \"min\": 3976.0,\n \"max\": 23888.0,\n \"num_unique_values\": 498,\n \"samples\": [\n 5708.0,\n 13317.0,\n 9410.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Indeks Pembangunan Manusia\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 452,\n \"samples\": [\n \"65,87\",\n \"70,83\",\n \"80,59\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Umur Harapan Hidup (Tahun)\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 411,\n \"samples\": [\n \"73,8\",\n \"71,58\",\n \"70,38\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Persentase rumah tangga yang memiliki akses terhadap sanitasi layak\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 487,\n \"samples\": [\n \"89,49\",\n \"91,56\",\n \"90,56\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Persentase rumah tangga yang memiliki akses terhadap air minum layak\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 465,\n \"samples\": [\n \"74,11\",\n \"85,78\",\n \"91,43\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Tingkat Pengangguran Terbuka\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 373,\n \"samples\": [\n \"4,56\",\n \"1,95\",\n \"3,47\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Tingkat Partisipasi Angkatan Kerja\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 448,\n \"samples\": [\n \" 77,57 \",\n \" 70,16 \",\n \" 70,13 \"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PDRB atas Dasar Harga Konstan menurut Pengeluaran (Rupiah)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 47904920.44381927,\n \"min\": 147485.0,\n \"max\": 460081046.0,\n \"num_unique_values\": 514,\n \"samples\": [\n 3025880.0,\n 1556231.0,\n 9082312.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Klasifikasi Kemiskinan\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3260054197106571,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 1.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
|
|
}
|
|
},
|
|
"metadata": {},
|
|
"execution_count": 219
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"df_subset = df[[\"Provinsi\", \"Klasifikasi Kemiskinan\"]]\n",
|
|
"df_subset.head(100)\n"
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 1000
|
|
},
|
|
"id": "oixH4QIIKJDG",
|
|
"outputId": "db2f76be-51d5-43b0-b5a8-0f508cda6ba7"
|
|
},
|
|
"execution_count": 244,
|
|
"outputs": [
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
" Provinsi Klasifikasi Kemiskinan\n",
|
|
"0 ACEH 0.0\n",
|
|
"1 ACEH 1.0\n",
|
|
"2 ACEH 0.0\n",
|
|
"3 ACEH 0.0\n",
|
|
"4 ACEH 0.0\n",
|
|
"5 ACEH 0.0\n",
|
|
"6 ACEH 0.0\n",
|
|
"7 ACEH 0.0\n",
|
|
"8 ACEH 0.0\n",
|
|
"9 ACEH 0.0\n",
|
|
"10 ACEH 0.0\n",
|
|
"11 ACEH 0.0\n",
|
|
"12 ACEH 0.0\n",
|
|
"13 ACEH 0.0\n",
|
|
"14 ACEH 0.0\n",
|
|
"15 ACEH 0.0\n",
|
|
"16 ACEH 0.0\n",
|
|
"17 ACEH 0.0\n",
|
|
"18 ACEH 0.0\n",
|
|
"19 ACEH 0.0\n",
|
|
"20 ACEH 0.0\n",
|
|
"21 ACEH 0.0\n",
|
|
"22 ACEH 0.0\n",
|
|
"23 SUMATERA UTARA 0.0\n",
|
|
"24 SUMATERA UTARA 0.0\n",
|
|
"25 SUMATERA UTARA 0.0\n",
|
|
"26 SUMATERA UTARA 0.0\n",
|
|
"27 SUMATERA UTARA 0.0\n",
|
|
"28 SUMATERA UTARA 0.0\n",
|
|
"29 SUMATERA UTARA 0.0\n",
|
|
"30 SUMATERA UTARA 0.0\n",
|
|
"31 SUMATERA UTARA 0.0\n",
|
|
"32 SUMATERA UTARA 0.0\n",
|
|
"33 SUMATERA UTARA 0.0\n",
|
|
"34 SUMATERA UTARA 0.0\n",
|
|
"35 SUMATERA UTARA 0.0\n",
|
|
"36 SUMATERA UTARA 0.0\n",
|
|
"37 SUMATERA UTARA 0.0\n",
|
|
"38 SUMATERA UTARA 0.0\n",
|
|
"39 SUMATERA UTARA 0.0\n",
|
|
"40 SUMATERA UTARA 0.0\n",
|
|
"41 SUMATERA UTARA 0.0\n",
|
|
"42 SUMATERA UTARA 0.0\n",
|
|
"43 SUMATERA UTARA 0.0\n",
|
|
"44 SUMATERA UTARA 0.0\n",
|
|
"45 SUMATERA UTARA 0.0\n",
|
|
"46 SUMATERA UTARA 1.0\n",
|
|
"47 SUMATERA UTARA 1.0\n",
|
|
"48 SUMATERA UTARA 0.0\n",
|
|
"49 SUMATERA UTARA 0.0\n",
|
|
"50 SUMATERA UTARA 0.0\n",
|
|
"51 SUMATERA UTARA 0.0\n",
|
|
"52 SUMATERA UTARA 0.0\n",
|
|
"53 SUMATERA UTARA 0.0\n",
|
|
"54 SUMATERA UTARA 0.0\n",
|
|
"55 SUMATERA UTARA 0.0\n",
|
|
"56 SUMATERA BARAT 0.0\n",
|
|
"57 SUMATERA BARAT 0.0\n",
|
|
"58 SUMATERA BARAT 0.0\n",
|
|
"59 SUMATERA BARAT 0.0\n",
|
|
"60 SUMATERA BARAT 0.0\n",
|
|
"61 SUMATERA BARAT 0.0\n",
|
|
"62 SUMATERA BARAT 0.0\n",
|
|
"63 SUMATERA BARAT 0.0\n",
|
|
"64 SUMATERA BARAT 0.0\n",
|
|
"65 SUMATERA BARAT 0.0\n",
|
|
"66 SUMATERA BARAT 0.0\n",
|
|
"67 SUMATERA BARAT 0.0\n",
|
|
"68 SUMATERA BARAT 0.0\n",
|
|
"69 SUMATERA BARAT 0.0\n",
|
|
"70 SUMATERA BARAT 0.0\n",
|
|
"71 SUMATERA BARAT 0.0\n",
|
|
"72 SUMATERA BARAT 0.0\n",
|
|
"73 SUMATERA BARAT 0.0\n",
|
|
"74 SUMATERA BARAT 0.0\n",
|
|
"75 RIAU 0.0\n",
|
|
"76 RIAU 0.0\n",
|
|
"77 RIAU 0.0\n",
|
|
"78 RIAU 0.0\n",
|
|
"79 RIAU 0.0\n",
|
|
"80 RIAU 0.0\n",
|
|
"81 RIAU 0.0\n",
|
|
"82 RIAU 0.0\n",
|
|
"83 RIAU 0.0\n",
|
|
"84 RIAU 1.0\n",
|
|
"85 RIAU 0.0\n",
|
|
"86 RIAU 0.0\n",
|
|
"87 JAMBI 0.0\n",
|
|
"88 JAMBI 0.0\n",
|
|
"89 JAMBI 0.0\n",
|
|
"90 JAMBI 0.0\n",
|
|
"91 JAMBI 0.0\n",
|
|
"92 JAMBI 0.0\n",
|
|
"93 JAMBI 0.0\n",
|
|
"94 JAMBI 0.0\n",
|
|
"95 JAMBI 0.0\n",
|
|
"96 JAMBI 0.0\n",
|
|
"97 JAMBI 0.0\n",
|
|
"98 SUMATERA SELATAN 0.0\n",
|
|
"99 SUMATERA SELATAN 0.0"
|
|
],
|
|
"text/html": [
|
|
"\n",
|
|
" <div id=\"df-01588f50-e194-4be4-aaab-239a8cd62a81\" class=\"colab-df-container\">\n",
|
|
" <div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
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"\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>Provinsi</th>\n",
|
|
" <th>Klasifikasi Kemiskinan</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>11</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>12</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>13</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>14</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>15</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>16</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>17</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>18</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>19</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>20</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>21</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>22</th>\n",
|
|
" <td>ACEH</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>23</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>24</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>26</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>27</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>28</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>29</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>30</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>31</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>32</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>33</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>34</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>35</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>36</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>37</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>38</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>39</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>40</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>41</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>42</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>43</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>44</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>45</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>46</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>47</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>48</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>49</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>51</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>52</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>53</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>54</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>55</th>\n",
|
|
" <td>SUMATERA UTARA</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>56</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>57</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>58</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>59</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>60</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>61</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>62</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>63</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>64</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>65</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>66</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>67</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>68</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>69</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>70</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>71</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>72</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>73</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>74</th>\n",
|
|
" <td>SUMATERA BARAT</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>75</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>76</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>77</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>78</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>79</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>80</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>81</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>82</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>83</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>84</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>85</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>86</th>\n",
|
|
" <td>RIAU</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>87</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>88</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>89</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>90</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>91</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>92</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>93</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>94</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>95</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>96</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>97</th>\n",
|
|
" <td>JAMBI</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>98</th>\n",
|
|
" <td>SUMATERA SELATAN</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>99</th>\n",
|
|
" <td>SUMATERA SELATAN</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>\n",
|
|
" <div class=\"colab-df-buttons\">\n",
|
|
"\n",
|
|
" <div class=\"colab-df-container\">\n",
|
|
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-01588f50-e194-4be4-aaab-239a8cd62a81')\"\n",
|
|
" title=\"Convert this dataframe to an interactive table.\"\n",
|
|
" style=\"display:none;\">\n",
|
|
"\n",
|
|
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
|
|
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
|
|
" </svg>\n",
|
|
" </button>\n",
|
|
"\n",
|
|
" <style>\n",
|
|
" .colab-df-container {\n",
|
|
" display:flex;\n",
|
|
" gap: 12px;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .colab-df-convert {\n",
|
|
" background-color: #E8F0FE;\n",
|
|
" border: none;\n",
|
|
" border-radius: 50%;\n",
|
|
" cursor: pointer;\n",
|
|
" display: none;\n",
|
|
" fill: #1967D2;\n",
|
|
" height: 32px;\n",
|
|
" padding: 0 0 0 0;\n",
|
|
" width: 32px;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .colab-df-convert:hover {\n",
|
|
" background-color: #E2EBFA;\n",
|
|
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
|
" fill: #174EA6;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .colab-df-buttons div {\n",
|
|
" margin-bottom: 4px;\n",
|
|
" }\n",
|
|
"\n",
|
|
" [theme=dark] .colab-df-convert {\n",
|
|
" background-color: #3B4455;\n",
|
|
" fill: #D2E3FC;\n",
|
|
" }\n",
|
|
"\n",
|
|
" [theme=dark] .colab-df-convert:hover {\n",
|
|
" background-color: #434B5C;\n",
|
|
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
|
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
|
" fill: #FFFFFF;\n",
|
|
" }\n",
|
|
" </style>\n",
|
|
"\n",
|
|
" <script>\n",
|
|
" const buttonEl =\n",
|
|
" document.querySelector('#df-01588f50-e194-4be4-aaab-239a8cd62a81 button.colab-df-convert');\n",
|
|
" buttonEl.style.display =\n",
|
|
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
|
"\n",
|
|
" async function convertToInteractive(key) {\n",
|
|
" const element = document.querySelector('#df-01588f50-e194-4be4-aaab-239a8cd62a81');\n",
|
|
" const dataTable =\n",
|
|
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
|
" [key], {});\n",
|
|
" if (!dataTable) return;\n",
|
|
"\n",
|
|
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
|
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
|
" + ' to learn more about interactive tables.';\n",
|
|
" element.innerHTML = '';\n",
|
|
" dataTable['output_type'] = 'display_data';\n",
|
|
" await google.colab.output.renderOutput(dataTable, element);\n",
|
|
" const docLink = document.createElement('div');\n",
|
|
" docLink.innerHTML = docLinkHtml;\n",
|
|
" element.appendChild(docLink);\n",
|
|
" }\n",
|
|
" </script>\n",
|
|
" </div>\n",
|
|
"\n",
|
|
"\n",
|
|
" <div id=\"df-34eee68a-edf2-4aa6-84c9-db3a991e6dbe\">\n",
|
|
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-34eee68a-edf2-4aa6-84c9-db3a991e6dbe')\"\n",
|
|
" title=\"Suggest charts\"\n",
|
|
" style=\"display:none;\">\n",
|
|
"\n",
|
|
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
|
|
" width=\"24px\">\n",
|
|
" <g>\n",
|
|
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
|
|
" </g>\n",
|
|
"</svg>\n",
|
|
" </button>\n",
|
|
"\n",
|
|
"<style>\n",
|
|
" .colab-df-quickchart {\n",
|
|
" --bg-color: #E8F0FE;\n",
|
|
" --fill-color: #1967D2;\n",
|
|
" --hover-bg-color: #E2EBFA;\n",
|
|
" --hover-fill-color: #174EA6;\n",
|
|
" --disabled-fill-color: #AAA;\n",
|
|
" --disabled-bg-color: #DDD;\n",
|
|
" }\n",
|
|
"\n",
|
|
" [theme=dark] .colab-df-quickchart {\n",
|
|
" --bg-color: #3B4455;\n",
|
|
" --fill-color: #D2E3FC;\n",
|
|
" --hover-bg-color: #434B5C;\n",
|
|
" --hover-fill-color: #FFFFFF;\n",
|
|
" --disabled-bg-color: #3B4455;\n",
|
|
" --disabled-fill-color: #666;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .colab-df-quickchart {\n",
|
|
" background-color: var(--bg-color);\n",
|
|
" border: none;\n",
|
|
" border-radius: 50%;\n",
|
|
" cursor: pointer;\n",
|
|
" display: none;\n",
|
|
" fill: var(--fill-color);\n",
|
|
" height: 32px;\n",
|
|
" padding: 0;\n",
|
|
" width: 32px;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .colab-df-quickchart:hover {\n",
|
|
" background-color: var(--hover-bg-color);\n",
|
|
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
|
" fill: var(--button-hover-fill-color);\n",
|
|
" }\n",
|
|
"\n",
|
|
" .colab-df-quickchart-complete:disabled,\n",
|
|
" .colab-df-quickchart-complete:disabled:hover {\n",
|
|
" background-color: var(--disabled-bg-color);\n",
|
|
" fill: var(--disabled-fill-color);\n",
|
|
" box-shadow: none;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .colab-df-spinner {\n",
|
|
" border: 2px solid var(--fill-color);\n",
|
|
" border-color: transparent;\n",
|
|
" border-bottom-color: var(--fill-color);\n",
|
|
" animation:\n",
|
|
" spin 1s steps(1) infinite;\n",
|
|
" }\n",
|
|
"\n",
|
|
" @keyframes spin {\n",
|
|
" 0% {\n",
|
|
" border-color: transparent;\n",
|
|
" border-bottom-color: var(--fill-color);\n",
|
|
" border-left-color: var(--fill-color);\n",
|
|
" }\n",
|
|
" 20% {\n",
|
|
" border-color: transparent;\n",
|
|
" border-left-color: var(--fill-color);\n",
|
|
" border-top-color: var(--fill-color);\n",
|
|
" }\n",
|
|
" 30% {\n",
|
|
" border-color: transparent;\n",
|
|
" border-left-color: var(--fill-color);\n",
|
|
" border-top-color: var(--fill-color);\n",
|
|
" border-right-color: var(--fill-color);\n",
|
|
" }\n",
|
|
" 40% {\n",
|
|
" border-color: transparent;\n",
|
|
" border-right-color: var(--fill-color);\n",
|
|
" border-top-color: var(--fill-color);\n",
|
|
" }\n",
|
|
" 60% {\n",
|
|
" border-color: transparent;\n",
|
|
" border-right-color: var(--fill-color);\n",
|
|
" }\n",
|
|
" 80% {\n",
|
|
" border-color: transparent;\n",
|
|
" border-right-color: var(--fill-color);\n",
|
|
" border-bottom-color: var(--fill-color);\n",
|
|
" }\n",
|
|
" 90% {\n",
|
|
" border-color: transparent;\n",
|
|
" border-bottom-color: var(--fill-color);\n",
|
|
" }\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"\n",
|
|
" <script>\n",
|
|
" async function quickchart(key) {\n",
|
|
" const quickchartButtonEl =\n",
|
|
" document.querySelector('#' + key + ' button');\n",
|
|
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
|
|
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
|
|
" try {\n",
|
|
" const charts = await google.colab.kernel.invokeFunction(\n",
|
|
" 'suggestCharts', [key], {});\n",
|
|
" } catch (error) {\n",
|
|
" console.error('Error during call to suggestCharts:', error);\n",
|
|
" }\n",
|
|
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
|
|
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
|
|
" }\n",
|
|
" (() => {\n",
|
|
" let quickchartButtonEl =\n",
|
|
" document.querySelector('#df-34eee68a-edf2-4aa6-84c9-db3a991e6dbe button');\n",
|
|
" quickchartButtonEl.style.display =\n",
|
|
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
|
" })();\n",
|
|
" </script>\n",
|
|
" </div>\n",
|
|
"\n",
|
|
" </div>\n",
|
|
" </div>\n"
|
|
],
|
|
"application/vnd.google.colaboratory.intrinsic+json": {
|
|
"type": "dataframe",
|
|
"variable_name": "df_subset",
|
|
"summary": "{\n \"name\": \"df_subset\",\n \"rows\": 999,\n \"fields\": [\n {\n \"column\": \"Provinsi\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 34,\n \"samples\": [\n \"BANTEN\",\n \"KALIMANTAN BARAT\",\n \"SULAWESI TENGGARA\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Klasifikasi Kemiskinan\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3260054197106571,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 1.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
|
|
}
|
|
},
|
|
"metadata": {},
|
|
"execution_count": 244
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"df_subset = df[[\"Provinsi\", \"Klasifikasi Kemiskinan\"]]\n",
|
|
"df_subset = df_subset.dropna()\n",
|
|
"\n",
|
|
"plt.figure(figsize=(12, 4))\n",
|
|
"sns.lineplot(data=df_subset, x=\"Provinsi\", y=\"Klasifikasi Kemiskinan\", marker=\"o\")\n",
|
|
"\n",
|
|
"plt.xticks(rotation=90)\n",
|
|
"plt.title(\"Line Plot Klasifikasi Kemiskinan di setiap Provinsi\")\n",
|
|
"plt.xlabel(\"Provinsi\")\n",
|
|
"plt.ylabel(\"Klasifikasi Kemiskinan\")\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()\n"
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 398
|
|
},
|
|
"id": "9cbPJC3aKczY",
|
|
"outputId": "b0693709-f4d7-4746-9d65-fb74acd90521"
|
|
},
|
|
"execution_count": 240,
|
|
"outputs": [
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 1200x400 with 1 Axes>"
|
|
],
|
|
"image/png": 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\n"
|
|
},
|
|
"metadata": {}
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"import pandas as pd\n",
|
|
"import numpy as np\n",
|
|
"import seaborn as sns\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"from sklearn.model_selection import train_test_split, cross_val_score\n",
|
|
"from sklearn.preprocessing import LabelEncoder\n",
|
|
"from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
|
|
"from sklearn.ensemble import RandomForestClassifier\n",
|
|
"from sklearn.linear_model import LogisticRegression\n",
|
|
"from sklearn.impute import SimpleImputer\n",
|
|
"\n"
|
|
],
|
|
"metadata": {
|
|
"id": "LIvFHTtMw5mI"
|
|
},
|
|
"execution_count": 179,
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"y = pd.to_numeric(df[\"Klasifikasi Kemiskinan\"], errors='coerce')\n",
|
|
"y = y.fillna(0).astype(int)\n",
|
|
"\n",
|
|
"X = df.drop(\"Klasifikasi Kemiskinan\", axis=1)\n"
|
|
],
|
|
"metadata": {
|
|
"id": "LPcA1dxyxERN"
|
|
},
|
|
"execution_count": 180,
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"X = X.apply(lambda s: s.astype(str).str.replace(\",\", \".\").str.replace(\" \", \"\", regex=True))\n",
|
|
"\n",
|
|
"for col in X.columns:\n",
|
|
" X[col] = pd.to_numeric(X[col], errors='coerce')\n",
|
|
"\n",
|
|
"label_enc = LabelEncoder()\n",
|
|
"X[\"Provinsi\"] = label_enc.fit_transform(X[\"Provinsi\"].astype(str))\n",
|
|
"X[\"Kab/Kota\"] = label_enc.fit_transform(X[\"Kab/Kota\"].astype(str))\n",
|
|
"\n",
|
|
"imputer = SimpleImputer(strategy='mean')\n",
|
|
"X[X.columns] = imputer.fit_transform(X)\n"
|
|
],
|
|
"metadata": {
|
|
"id": "g_oElrw_xSFf"
|
|
},
|
|
"execution_count": 181,
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"X_train, X_test, y_train, y_test = train_test_split(\n",
|
|
" X, y, test_size=0.2, random_state=42\n",
|
|
")\n"
|
|
],
|
|
"metadata": {
|
|
"id": "QubEOuCOxVE7"
|
|
},
|
|
"execution_count": 182,
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"rf = RandomForestClassifier(n_estimators=200, random_state=42)\n",
|
|
"rf.fit(X_train, y_train)\n",
|
|
"\n",
|
|
"pred_rf = rf.predict(X_test)\n",
|
|
"\n",
|
|
"print(\"=== Random Forest Accuracy ===\")\n",
|
|
"print(accuracy_score(y_test, pred_rf))\n",
|
|
"print(\"\\n=== Classification Report ===\")\n",
|
|
"print(classification_report(y_test, pred_rf))\n",
|
|
"print(\"\\n=== Confusion Matrix ===\")\n",
|
|
"print(confusion_matrix(y_test, pred_rf))\n",
|
|
"\n",
|
|
"cv_rf = cross_val_score(rf, X, y, cv=5)\n",
|
|
"print(\"\\n=== Cross Validation (Random Forest) ===\")\n",
|
|
"print(cv_rf)\n",
|
|
"print(\"Mean CV Score:\", cv_rf.mean())\n",
|
|
"\n"
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "aEaCyi8XxXql",
|
|
"outputId": "c080eb18-5fdb-4cb1-e7e3-b1a92d56f8dc"
|
|
},
|
|
"execution_count": 183,
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
|
|
"=== Random Forest Accuracy ===\n",
|
|
"0.99\n",
|
|
"\n",
|
|
"=== Classification Report ===\n",
|
|
" precision recall f1-score support\n",
|
|
"\n",
|
|
" 0 0.99 1.00 0.99 190\n",
|
|
" 1 1.00 0.80 0.89 10\n",
|
|
"\n",
|
|
" accuracy 0.99 200\n",
|
|
" macro avg 0.99 0.90 0.94 200\n",
|
|
"weighted avg 0.99 0.99 0.99 200\n",
|
|
"\n",
|
|
"\n",
|
|
"=== Confusion Matrix ===\n",
|
|
"[[190 0]\n",
|
|
" [ 2 8]]\n",
|
|
"\n",
|
|
"=== Cross Validation (Random Forest) ===\n",
|
|
"[0.99 1. 0.97 0.99 1. ]\n",
|
|
"Mean CV Score: 0.99\n"
|
|
]
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import numpy as np\n"
|
|
],
|
|
"metadata": {
|
|
"id": "l_Bms2snyjks"
|
|
},
|
|
"execution_count": 184,
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"accuracy = 0.99\n",
|
|
"\n",
|
|
"conf_matrix = np.array([\n",
|
|
" [190, 0],\n",
|
|
" [2, 8]\n",
|
|
"])"
|
|
],
|
|
"metadata": {
|
|
"id": "isQJoC9Tyq5G"
|
|
},
|
|
"execution_count": 185,
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"classes = [\"0\", \"1\"]\n",
|
|
"precision = [0.99, 1.00]\n",
|
|
"recall = [1.00, 0.80]\n",
|
|
"f1 = [0.99, 0.89]\n",
|
|
"\n",
|
|
"cv_scores = np.array([0.99, 1.00, 0.97, 0.99, 1.00])"
|
|
],
|
|
"metadata": {
|
|
"id": "ZmNBm0wyzKA8"
|
|
},
|
|
"execution_count": 186,
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"plt.figure()\n",
|
|
"plt.bar([\"Accuracy\"], [accuracy])\n",
|
|
"plt.title(\"Random Forest Accuracy\")\n",
|
|
"plt.ylabel(\"Score\")\n",
|
|
"plt.ylim(0, 1)\n",
|
|
"plt.show()"
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 452
|
|
},
|
|
"id": "kTf7LP5my2ja",
|
|
"outputId": "694bd198-655a-40b3-d73c-bb9819699268"
|
|
},
|
|
"execution_count": 187,
|
|
"outputs": [
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
],
|
|
"image/png": 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\n"
|
|
},
|
|
"metadata": {}
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"conf_matrix = np.array([[190, 0],\n",
|
|
" [2, 8]])\n",
|
|
"\n",
|
|
"plt.figure(figsize=(6,4))\n",
|
|
"plt.imshow(conf_matrix)\n",
|
|
"plt.title(\"Confusion Matrix\")\n",
|
|
"plt.colorbar()\n",
|
|
"plt.xticks([0,1], [\"Pred 0\", \"Pred 1\"])\n",
|
|
"plt.yticks([0,1], [\"Actual 0\", \"Actual 1\"])\n",
|
|
"plt.show()\n"
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 391
|
|
},
|
|
"id": "mV2_uYR1y5zj",
|
|
"outputId": "dec16ae1-483f-4392-ff5b-06df5788abee"
|
|
},
|
|
"execution_count": 188,
|
|
"outputs": [
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 600x400 with 2 Axes>"
|
|
],
|
|
"image/png": 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\n"
|
|
},
|
|
"metadata": {}
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"x = np.arange(len(classes))\n",
|
|
"width = 0.25\n",
|
|
"\n",
|
|
"plt.figure()\n",
|
|
"plt.bar(x - width, precision, width, label=\"Precision\")\n",
|
|
"plt.bar(x, recall, width, label=\"Recall\")\n",
|
|
"plt.bar(x + width, f1, width, label=\"F1-Score\")\n",
|
|
"\n",
|
|
"plt.xticks(x, classes)\n",
|
|
"plt.ylabel(\"Score\")\n",
|
|
"plt.title(\"Classification Metrics per Class\")\n",
|
|
"plt.legend()\n",
|
|
"plt.ylim(0, 1)\n",
|
|
"plt.show()"
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 452
|
|
},
|
|
"id": "OgLQduE6y85d",
|
|
"outputId": "dbbe51e6-c672-466c-a62e-8f5d37ed03be"
|
|
},
|
|
"execution_count": 189,
|
|
"outputs": [
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
],
|
|
"image/png": 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55Ze1ceNGtWvXTrNmzdKrr75asp2EaTByA1N6/vnn5enpqf79+ystLa3A44cOHbI7PffvnJ2dC4woLFmyRL/++qtd299PYXV1dVXjxo1lGIZycnKUl5dXYKjc399f1atXV1ZWVnF3q4CqVauqQ4cOmjdvno4ePWr32F/rL2x/pk+frry8PLs2T09PSSoQegpz3333KTU1VYsXL7a15ebmavr06fLy8lLHjh2LuztFYrFYNG3aNMXFxV3xqs0PPPCAnJ2dNW7cuAL7bhiG3Wvn6el5zYc0mjdvrpCQEE2dOrXA83dp+xcuXFBmZqbdY3Xr1lXFihWL/H6YMWOG3XpnzJghFxcX3XXXXZL+HPXIy8vTK6+8UmDZ3NzcIr22lzNu3Dj98ccf6t+/v3Jzcws8/tVXX2nFihWFLntp5O7vr8XfrwxdlM9MRkZGge2HhobKycmpVD5XuPExcgNTqlu3rhISEhQZGalGjRrZXaF448aNtlOWL6dbt24aP368oqOjdccdd2jnzp1auHBhgfkOXbp0UWBgoNq1a6eAgAD9/PPPmjFjhrp27aqKFSvqzJkzqlmzph588EGFhYXJy8tL69at048//mg3inItpk2bpvbt26t58+YaOHCgQkJClJKSopUrV2r79u22/fn444/l4+Ojxo0ba9OmTVq3bp2qVKlit66mTZvK2dlZr7/+utLT02W1WvWPf/xD/v7+BbY7cOBAvffee+rXr5+2bNmi4OBgLV26VN9//72mTp16TRNPr6ZHjx7q0aPHFfvUrVtXr776qkaNGqWUlBT17NlTFStWVHJysj777DMNHDhQzz33nCSpRYsWWrx4sWJjY9WqVSt5eXmpe/fuxarJyclJ7777rrp3766mTZsqOjpa1apV0969e7V7926tWbNG+/fv11133aXevXurcePGqlChgj777DOlpaXp4Ycfvuo23NzctHr1akVFRalNmzb68ssvtXLlSr344ouqWrWqJKljx44aNGiQ4uPjtX37dnXp0kUuLi46cOCAlixZorffflsPPvhgsfbtksjISO3cuVMTJkzQtm3b9Mgjj9iuULx69WolJibaXePpr7y9vW1zjHJyclSjRg199dVXSk5Otut39uzZq35mvv76a8XExOihhx5S/fr1lZubq48//ljOzs7q1atXifYNJuOQc7SAMrJ//35jwIABRnBwsOHq6mpUrFjRaNeunTF9+nQjMzPT1q+wU8GfffZZo1q1aoa7u7vRrl07Y9OmTQVOh37vvfeMDh06GFWqVDGsVqtRt25dY8SIEUZ6erphGIaRlZVljBgxwggLCzMqVqxoeHp6GmFhYcY777xjV+e1nApuGIaxa9cu45///Kfh6+truLm5GQ0aNDBGjx5te/z06dNGdHS04efnZ3h5eRkRERHG3r17C+y3YRjGnDlzjDp16hjOzs52pzr/fd8NwzDS0tJs63V1dTVCQ0ML1Hap5sJOf5ZkxMXFFWj/q7+eCn4lfz8V/JL//Oc/Rvv27Q1PT0/D09PTaNiwoTF48GBj3759tj7nzp0z+vTpY/j6+hqSbK/Flbb991PBL9mwYYNx9913217v2267zZg+fbphGIZx8uRJY/DgwUbDhg0NT09Pw8fHx2jTpo3xySefXHHfDOPP94inp6dx6NAho0uXLoaHh4cREBBgxMXFFbhsgWEYxuzZs40WLVoY7u7uRsWKFY3Q0FDj+eefN3777Tdbn8LeY0WRmJho9OjRw/D39zcqVKhgVK1a1ejevbvx+eef2/oU9l795ZdfbO9THx8f46GHHjJ+++03u/dBUT4zhw8fNh5//HGjbt26hpubm1G5cmWjc+fOxrp164q9LzAni2EUczYfAKDM9evXT0uXLtW5c+ccXQpQ7jHnBgAAmArhBgAAmArhBgAAmIpDw8369evVvXt3Va9eXRaLRcuWLbvqMklJSWrevLmsVqtuueWWAr+ODABmNH/+fObbAEXk0HBz/vx5hYWFaebMmUXqn5ycrK5du6pz587avn27hg8frv79+2vNmjXXuVIAAHCjKDdnS1ksFn322Wfq2bPnZfu88MILWrlypd3VWB9++GGdOXPGdglvAABwc7uhLuK3adOmApc1j4iI0PDhwy+7TFZWlt0VK/Pz83Xq1ClVqVKlVC65DgAArj/DMHT27FlVr169wA/2/t0NFW5SU1MVEBBg1xYQEKCMjAxdvHix0N9RiY+P17hx48qqRAAAcB0dO3ZMNWvWvGKfGyrclMSoUaMUGxtru5+enq5atWrp2LFj8vb2dmBlJhV/5TfcTWEUv0p8M7s1jjmAu8ZFOLoEmFBGRoaCgoKK9NMuN1S4CQwMLPAjiGlpafL29r7sr99arVZZrdYC7d7e3oSb68HKoT7xvrqpOVk9HF2Cw/HdiuupKFNKbqhw07ZtW61atcqube3atWrbtq2DKiooeORKR5fgUClujq7A8UI/DHV0CQ61M2qno0sAcJNz6Kng586d0/bt222/XJycnKzt27fr6NGjkv48pNS3b19b/yeffFKHDx/W888/r7179+qdd97RJ598omeeecYR5QMAgHLIoeHmf//7n5o1a6ZmzZpJkmJjY9WsWTONGTNGkvT777/bgo4khYSEaOXKlVq7dq3CwsI0efJkzZ07VxERHN8FAAB/cuhhqU6dOulKl9kp7OrDnTp10rZt265jVQCAG51hGMrNzVVeXp6jS0ExuLi4yNnZ+ZrXc0PNuQEA4Gqys7P1+++/68KFC44uBcVksVhUs2ZNeXl5XdN6CDcAANPIz89XcnKynJ2dVb16dbm6unLB1huEYRg6ceKEfvnlF9WrV++aRnAINwAA08jOzlZ+fr6CgoLk4cFp+TeaqlWrKiUlRTk5OdcUbhw6oRgAgOvhapfnR/lUWqNsvPoAAMBUCDcAAMBUmHMDALgplPUV5FNe61qm2ysJi8Wizz77TD179izVvo7GyA0AAOVAv379ZLFYZLFY5OrqqltuuUXjx49Xbm7uddvm77//rnvvvbfU+zoaIzcAAJQT99xzjz744ANlZWVp1apVGjx4sFxcXDRq1Ci7ftnZ2XJ1db3m7QUGBl6Xvo7GyA0AAOWE1WpVYGCgateuraeeekrh4eFavny5+vXrp549e2rChAmqXr26GjRoIEk6duyYevfuLV9fX1WuXFk9evRQSkqK3TrnzZunJk2ayGq1qlq1aoqJibE9ZrFYtGzZMkl/BqaYmBhVq1ZNbm5uql27tuLj4wvtK0k7d+7UP/7xD7m7u6tKlSoaOHCgzp07Z3v8Us1vvvmmqlWrpipVqmjw4MHKyckp/Sfubwg3AACUU+7u7srOzpYkJSYmat++fVq7dq1WrFihnJwcRUREqGLFivruu+/0/fffy8vLS/fcc49tmXfffVeDBw/WwIEDtXPnTi1fvly33HJLoduaNm2ali9frk8++UT79u3TwoULFRwcXGjf8+fPKyIiQpUqVdKPP/6oJUuWaN26dXbBSZK++eYbHTp0SN98840+/PBDzZ8/v9CfViptHJYCAKCcMQxDiYmJWrNmjYYMGaITJ07I09NTc+fOtR2OWrBggfLz8zV37lzb9WE++OAD+fr6KikpSV26dNGrr76qZ599VsOGDbOtu1WrVoVu8+jRo6pXr57at28vi8Wi2rVrX7a+hIQEZWZm6qOPPpKnp6ckacaMGerevbtef/11BQQESJIqVaqkGTNmyNnZWQ0bNlTXrl2VmJioAQMGlMrzdDmM3AAAUE6sWLFCXl5ecnNz07333qvIyEiNHTtWkhQaGmo3z+ann37SwYMHVbFiRXl5ecnLy0uVK1dWZmamDh06pOPHj+u3337TXXfdVaRt9+vXT9u3b1eDBg00dOhQffXVV5ft+/PPPyssLMwWbCSpXbt2ys/P1759+2xtTZo0sbvScLVq1XT8+PGiPh0lxsgNAADlROfOnfXuu+/K1dVV1atXV4UK//9n+q9BQpLOnTunFi1aaOHChQXWU7Vq1WJfpbl58+ZKTk7Wl19+qXXr1ql3794KDw/X0qVLS7Yz+vNXvv/KYrEoPz+/xOsrKsINAADlhKen52XnxPxd8+bNtXjxYvn7+8vb27vQPsHBwUpMTFTnzp2LtE5vb29FRkYqMjJSDz74oO655x6dOnVKlStXtuvXqFEjzZ8/X+fPn7eFru+//15OTk62yc6OxGEpAABuQI8++qj8/PzUo0cPfffdd0pOTlZSUpKGDh2qX375RZI0duxYTZ48WdOmTdOBAwe0detWTZ8+vdD1TZkyRf/+97+1d+9e7d+/X0uWLFFgYKB8fX0L3babm5uioqK0a9cuffPNNxoyZIgee+wx23wbR2LkBgBwU7gRrhhcHB4eHlq/fr1eeOEFPfDAAzp79qxq1Kihu+66yzaSExUVpczMTL311lt67rnn5OfnpwcffLDQ9VWsWFGTJk3SgQMH5OzsrFatWmnVqlWFHt7y8PDQmjVrNGzYMLVq1UoeHh7q1auXpkyZcl33uagshmEYji6iLGVkZMjHx0fp6emXHca7FmV9ee/yJsWtj6NLcLjQkFqOLsGhdkbtdHQJDnWzfwdIjg0RmZmZSk5OVkhIiNzc3BxWB0rmSq9fcf5+c1gKAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCj+/AAC4OYz1KePtpZft9kqBxWLRZ599pp49eyolJUUhISHatm2bmjZt6ujSioWRGwAAyoF+/frJYrHIYrHIxcVFISEhev7555WZmeno0m44jNwAAFBO3HPPPfrggw+Uk5OjLVu2KCoqShaLRa+//rqjS7uhMHIDAEA5YbVaFRgYqKCgIPXs2VPh4eFau3atJCk/P1/x8fEKCQmRu7u7wsLCtHTpUrvld+/erW7dusnb21sVK1bUnXfeqUOHDkmSfvzxR919993y8/OTj4+POnbsqK1bt5b5PpYFwg0AAOXQrl27tHHjRrm6ukqS4uPj9dFHH2nWrFnavXu3nnnmGf3rX//St99+K0n69ddf1aFDB1mtVn399dfasmWLHn/8ceXm5kqSzp49q6ioKG3YsEH//e9/Va9ePd133306e/asw/bxeuGwFAAA5cSKFSvk5eWl3NxcZWVlycnJSTNmzFBWVpYmTpyodevWqW3btpKkOnXqaMOGDXrvvffUsWNHzZw5Uz4+Plq0aJFcXFwkSfXr17et+x//+IfdtmbPni1fX199++236tatW9ntZBkg3AAAcB3sPrm7WP3PZJ5Rq/atNGbSGF24cEEfz/pYzhWc1bBjQ63+cbUuXLigu8LvslsmJydHjUIbaffJ3dqweYNCW4Vqf/r+Qtd/8vhJTY+frh+//1GnTp5SXl6eMi9m6oc9Pyjk9hBbv6MZR7X75G79eupXSdKh04fkctKlWPvSxK9JsfqXNsINAADlhIeHh2rVqSVJemXaK+rVqZf+s+A/qteoniTpnYR3FFAtwG4ZF+ufwcPqZr3iul+KeUlnTp/RyAkjVT2oulxdXfXofY8qJzvnOuyJYxFuAAAoh5ycnDRg+ABNGj1JK39YKVerq37/9Xe1ateq0P71m9TX8sXLlZOTYzss9VfbNm/Ty5NeVoe7O0iSfv/1d53+4/R13QdHYUIxAADlVJf7u8jZ2VmffPiJ+j3dT5NGT9Lniz7X0eSj2vPTHi2cs1CfL/pcktTniT46d/acRgwYoV3bd+nIoSNa/slyJR9MliTVrlNbXyz5Qof2H9KOLTs08smRcnN3c+TuXTeM3AAAbg5ldMXg4s61uZIKFSrokSce0QczPtDq/61WpSqVNPftuTp25Ji8fbzVKLSRBgwfIEnyreyr9z99X5PHTlZ0j2g5OTmp4a0N1ax1M0nS+KnjNfbZsep9V28FVg/UsJeG6c2xb5ZareWJxTAMw9FFlKWMjAz5+PgoPT1d3t7epb7+4JErS32dN5IUtz6OLsHhQkNqOboEh9oZtdPRJTjUzf4dIEkpr3V12LYzMzOVnJyskJAQubk5ZlSiNMPNjaqkE4qv9PoV5+83h6UAAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICp8PMLAICbQuiHoWW6vUVdF5Xp9vD/CDcAAJQDL8W8pM8Xf16gfdUPq3Q89bg+mPmB9vy0RyfSTujtD9/WXffdddV17t21VzNem6EdW3bo3Nlz8vP3U2jzUL0Y/6KqVK1yPXajXCDcAABQTrT/R3u9Ou1Vu7ZKfpV05PARNWjSQP/s808N7ze8SOs6dfKU+vfqr453d9R7i99TRZ+K+u3Yb/pm9Te6eOHidaj+Tzk5Oddt3UXFnBsAAMoJV6ur/AL87G7Ozs66M/xODX1xqMK7hhd5Xds2b9O5jHMaN3WcGt3WSDVr11Tr9q31wqsvqGbtmrZ+B/ce1NN9nlabkDZqHdxafbv11dHko5Kk/Px8vfvmu7rrtrvUrEYz9erUSxsSN9iW/fXor7q16q368rMv1e/+fmpes7lWLv3zx2Pnzp2rRo0ayc3NTQ0bNtQ777xTSs/S1TFyAwCACfn5+yk3N1eJKxPV5f4uslgsBfqk/Z6mqPuj1OqOVnr/s/flVdFL237Ypry8PEnSgtkL9OE7H2rM5DFqFNpInyV8ppjHYvT5d5+rdt3atvVMfXWqnhv3nF4NfVVWq1ULFy7UmDFjNGPGDDVr1kzbtm3TgAED5OnpqaioqOu+74QbAADKiW+/+latarey3b/zrjs1Zd6UEq0rrGWYBgwfoBeefEHjR4xXaLNQtb6zte7vfb/8/P0kSf9+/9+q6F1Rb8x5Qy4uLpKk4LrBtnXMnzlfjw95XPf98z5JUuyYWG3esFkfv/exXp70sq3fvwb+S3d3u9t2v3/P/po8ebIeeOABSVJISIj27Nmj9957j3ADAMDNpFX7VhozaYztvruHe5GWm/3WbM2ZOsd2f/n3y1WtZjUNe2mYop6K0g/f/aAdW3fokw8/0dypczV/+XzVb1xf+3btU/M2zW3B5q/OnT2n46nH1ax1M7v2Zq2bad/ufXZtTZo2sf37wvkLOnTokJ544gkNGDDA1p6bmysfH58i7c+1ItwAAFBOeHh4qFadWsVeLrJfpO7pcY/tftXAqrZ/+1b2VUSPCEX0iNDwl4brwX88qPkz52vizImyullLre5LLpy/IEmaM2eO2rRpY9fP2dm5VLZ3NYQbAABucD6VfORT6eqjIi6uLgoKDrKdLVW/SX0tX7xcOTk5BUZvvCp6yT/QX9s2b1Ordv9/qGzb5m0KbXb5awb5+fupevXqOnz4sB599NES7tG1IdwAAFDOXTh3wXYGk/TnWUp7d+6VTyUfVatZrdBlkr5K0peffal7/3mvgusGyzAMJa1J0nfrvtMr016RJPV5oo8S5iZoxIAR6j+8vypWrKiftvyk0OahCrklRNGDozVz0kwFBQepYWhDLUtYpr279ur1d1+/Yr3jxo3T0KFD5ePjo3vuuUdZWVn63//+p9OnTys2Nrb0npjLINwAAG4KO6N2lsl2dp/cXerr3PXTLj3e83Hb/UmjJ0mSekT20IQZEwpdpm79unJ3d9ebY95U6m+pcnV1Va06tTTurXG6v/f9kv48ZPX+p+9r8tjJiu4RLScnJzW8taFtns2jAx/V2bNn9Wbcm/rj5B+qW7+uZnw8w+5MqcL0799fHh4eeuONNzRixAh5enoqNDRUw4cPL4Vn4+oshmEYZbKlciIjI0M+Pj5KT0+Xt7d3qa8/eOTKUl/njSTFrY+jS3C40JDiHy83k7L6A1Je3ezfAZKU8lpXh207MzNTycnJCgkJkZubm0NquB7h5kbTxK/J1TsV4kqvX3H+fnMRPwAAYCqEGwAAYCqEGwAAYCoODzczZ85UcHCw3Nzc1KZNG23evPmK/adOnaoGDRrI3d1dQUFBeuaZZ5SZmVlG1QIAgPLOoeFm8eLFio2NVVxcnLZu3aqwsDBFRETo+PHjhfZPSEjQyJEjFRcXp59//lnvv/++Fi9erBdffLGMKwcAlGc32bkyplFar5tDw82UKVM0YMAARUdHq3Hjxpo1a5Y8PDw0b968Qvtv3LhR7dq1U58+fRQcHKwuXbrokUceuepoDwDg5nDpQnQXLlxwcCUoiezsbEnXfiVjh13nJjs7W1u2bNGoUaNsbU5OTgoPD9emTZsKXeaOO+7QggULtHnzZrVu3VqHDx/WqlWr9Nhjj112O1lZWcrKyrLdz8jIKL2dAACUK87OzvL19bUdAfDw8Cj017Cvp/yc/DLdXnlUkuki+fn5OnHihDw8PFShwrXFE4eFm5MnTyovL08BAQF27QEBAdq7d2+hy/Tp00cnT55U+/btZRiGcnNz9eSTT17xsFR8fLzGjRtXqrUDAK5gbNn8OOLlBMoi1euj47XvlZxdy3z7x6/xD7MZVDhTsufAyclJtWrVuuZAekO9AklJSZo4caLeeecdtWnTRgcPHtSwYcP0yiuvaPTo0YUuM2rUKLtLPWdkZCgoKKisSgYAlDGLDFU7sFD+hz9VjlsVqYxHbobVqF6m2yuPlv9zeYmWc3V1lZPTtc+YcVi48fPzk7Ozs9LS0uza09LSFBgYWOgyo0eP1mOPPab+/ftLkkJDQ3X+/HkNHDhQL730UqFPiNVqldVaOr96CgC4cTjnXZTz+V/KfLu/Z5dtmCqPHHV16EscNqHY1dVVLVq0UGJioq0tPz9fiYmJatu2baHLXLhwoUCAuTTpiJnxAABAcvBhqdjYWEVFRally5Zq3bq1pk6dqvPnzys6OlqS1LdvX9WoUUPx8fGSpO7du2vKlClq1qyZ7bDU6NGj1b1792ueWQ0AAMzBoeEmMjJSJ06c0JgxY5SamqqmTZtq9erVtknGR48etRupefnll2WxWPTyyy/r119/VdWqVdW9e3dNmFD4L6ICAICbj8MnFMfExCgmJqbQx5KSkuzuV6hQQXFxcYqLiyuDygAAwI3I4T+/AAAAUJoINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQcHm5mzpyp4OBgubm5qU2bNtq8efMV+585c0aDBw9WtWrVZLVaVb9+fa1ataqMqgUAAOVdBUdufPHixYqNjdWsWbPUpk0bTZ06VREREdq3b5/8/f0L9M/Oztbdd98tf39/LV26VDVq1NCRI0fk6+tb9sUDAIByyaHhZsqUKRowYICio6MlSbNmzdLKlSs1b948jRw5skD/efPm6dSpU9q4caNcXFwkScHBwWVZMgAAKOccdlgqOztbW7ZsUXh4+P8X4+Sk8PBwbdq0qdBlli9frrZt22rw4MEKCAjQrbfeqokTJyovL++y28nKylJGRobdDQAAmJfDws3JkyeVl5engIAAu/aAgAClpqYWuszhw4e1dOlS5eXladWqVRo9erQmT56sV1999bLbiY+Pl4+Pj+0WFBRUqvsBAADKF4dPKC6O/Px8+fv7a/bs2WrRooUiIyP10ksvadasWZddZtSoUUpPT7fdjh07VoYVAwCAsuawOTd+fn5ydnZWWlqaXXtaWpoCAwMLXaZatWpycXGRs7Ozra1Ro0ZKTU1Vdna2XF1dCyxjtVpltVpLt3gAAFBuOWzkxtXVVS1atFBiYqKtLT8/X4mJiWrbtm2hy7Rr104HDx5Ufn6+rW3//v2qVq1aocEGAADcfBx6WCo2NlZz5szRhx9+qJ9//llPPfWUzp8/bzt7qm/fvho1apSt/1NPPaVTp05p2LBh2r9/v1auXKmJEydq8ODBjtoFAABQzjj0VPDIyEidOHFCY8aMUWpqqpo2barVq1fbJhkfPXpUTk7/n7+CgoK0Zs0aPfPMM7rttttUo0YNDRs2TC+88IKjdgEAAJQzDg03khQTE6OYmJhCH0tKSirQ1rZtW/33v/+9zlUBAIAb1Q11thQAAMDVEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpXFO4yc7O1r59+5Sbm1ta9QAAAFyTEoWbCxcu6IknnpCHh4eaNGmio0ePSpKGDBmi1157rVQLBAAAKI4ShZtRo0bpp59+UlJSktzc3Gzt4eHhWrx4cakVBwAAUFwVSrLQsmXLtHjxYt1+++2yWCy29iZNmujQoUOlVhwAAEBxlWjk5sSJE/L39y/Qfv78ebuwAwAAUNZKFG5atmyplStX2u5fCjRz585V27ZtS6cyAACAEijRYamJEyfq3nvv1Z49e5Sbm6u3335be/bs0caNG/Xtt9+Wdo0AAABFVqKRm/bt2+unn35Sbm6uQkND9dVXX8nf31+bNm1SixYtSrtGAACAIiv2yE1OTo4GDRqk0aNHa86cOdejJgAAgBIr9siNi4uL/vOf/1yPWgAAAK5ZiQ5L9ezZU8uWLSvlUgAAAK5diSYU16tXT+PHj9f333+vFi1ayNPT0+7xoUOHlkpxAAAAxVWicPP+++/L19dXW7Zs0ZYtW+wes1gshBsAAOAwJQo3ycnJpV0HAABAqbimXwWXJMMwZBhGadQCAABwzUocbj766COFhobK3d1d7u7uuu222/Txxx+XZm0AAADFVqLDUlOmTNHo0aMVExOjdu3aSZI2bNigJ598UidPntQzzzxTqkUCAAAUVYnCzfTp0/Xuu++qb9++trb7779fTZo00dixYwk3AADAYUp0WOr333/XHXfcUaD9jjvu0O+//37NRQEAAJRUicLNLbfcok8++aRA++LFi1WvXr1rLgoAAKCkSnRYaty4cYqMjNT69ettc26+//57JSYmFhp6AAAAykqJRm569eqlH374QX5+flq2bJmWLVsmPz8/bd68Wf/85z9Lu0YAAIAiK9HIjSS1aNFCCxYsKM1aAAAArlmJRm5WrVqlNWvWFGhfs2aNvvzyy2suCgAAoKRKFG5GjhypvLy8Au2GYWjkyJHXXBQAAEBJlSjcHDhwQI0bNy7Q3rBhQx08ePCaiwIAACipEoUbHx8fHT58uED7wYMH5enpec1FAQAAlFSJwk2PHj00fPhwHTp0yNZ28OBBPfvss7r//vtLrTgAAIDiKlG4mTRpkjw9PdWwYUOFhIQoJCREDRs2VJUqVfTmm2+Wdo0AAABFVqJTwX18fLRx40atXbtWP/30k9zd3RUWFqY777yztOsDAAAolmKN3GzatEkrVqyQJFksFnXp0kX+/v5688031atXLw0cOFBZWVnXpVAAAICiKFa4GT9+vHbv3m27v3PnTg0YMEB33323Ro4cqS+++ELx8fGlXiQAAEBRFSvcbN++XXfddZft/qJFi9S6dWvNmTNHsbGxmjZtGr8tBQAAHKpY4eb06dMKCAiw3f/2229177332u63atVKx44dK73qAAAAiqlY4SYgIEDJycmSpOzsbG3dulW333677fGzZ8/KxcWldCsEAAAohmKFm/vuu08jR47Ud999p1GjRsnDw8PuDKkdO3aobt26pV4kAABAURXrVPBXXnlFDzzwgDp27CgvLy99+OGHcnV1tT0+b948denSpdSLBAAAKKpihRs/Pz+tX79e6enp8vLykrOzs93jS5YskZeXV6kWCAAAUBwlvohfYSpXrnxNxQAAAFyrEv38AgAAQHlFuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZSLsLNzJkzFRwcLDc3N7Vp00abN28u0nKLFi2SxWJRz549r2+BAADghuHwcLN48WLFxsYqLi5OW7duVVhYmCIiInT8+PErLpeSkqLnnntOd955ZxlVCgAAbgQODzdTpkzRgAEDFB0drcaNG2vWrFny8PDQvHnzLrtMXl6eHn30UY0bN0516tS54vqzsrKUkZFhdwMAAObl0HCTnZ2tLVu2KDw83Nbm5OSk8PBwbdq06bLLjR8/Xv7+/nriiSeuuo34+Hj5+PjYbkFBQaVSOwAAKJ8cGm5OnjypvLw8BQQE2LUHBAQoNTW10GU2bNig999/X3PmzCnSNkaNGqX09HTb7dixY9dcNwAAKL8qOLqA4jh79qwee+wxzZkzR35+fkVaxmq1ymq1XufKAABAeeHQcOPn5ydnZ2elpaXZtaelpSkwMLBA/0OHDiklJUXdu3e3teXn50uSKlSooH379qlu3brXt2gAAFCuOfSwlKurq1q0aKHExERbW35+vhITE9W2bdsC/Rs2bKidO3dq+/btttv999+vzp07a/v27cynAQAAjj8sFRsbq6ioKLVs2VKtW7fW1KlTdf78eUVHR0uS+vbtqxo1aig+Pl5ubm669dZb7Zb39fWVpALtAADg5uTwcBMZGakTJ05ozJgxSk1NVdOmTbV69WrbJOOjR4/KycnhZ6wDAIAbhMPDjSTFxMQoJiam0MeSkpKuuOz8+fNLvyAAAHDDYkgEAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYSrkINzNnzlRwcLDc3NzUpk0bbd68+bJ958yZozvvvFOVKlVSpUqVFB4efsX+AADg5uLwcLN48WLFxsYqLi5OW7duVVhYmCIiInT8+PFC+yclJemRRx7RN998o02bNikoKEhdunTRr7/+WsaVAwCA8sjh4WbKlCkaMGCAoqOj1bhxY82aNUseHh6aN29eof0XLlyop59+Wk2bNlXDhg01d+5c5efnKzExsdD+WVlZysjIsLsBAADzcmi4yc7O1pYtWxQeHm5rc3JyUnh4uDZt2lSkdVy4cEE5OTmqXLlyoY/Hx8fLx8fHdgsKCiqV2gEAQPnk0HBz8uRJ5eXlKSAgwK49ICBAqampRVrHCy+8oOrVq9sFpL8aNWqU0tPTbbdjx45dc90AAKD8quDoAq7Fa6+9pkWLFikpKUlubm6F9rFarbJarWVcGQAAcBSHhhs/Pz85OzsrLS3Nrj0tLU2BgYFXXPbNN9/Ua6+9pnXr1um22267nmUCAIAbiEMPS7m6uqpFixZ2k4EvTQ5u27btZZebNGmSXnnlFa1evVotW7Ysi1IBAMANwuGHpWJjYxUVFaWWLVuqdevWmjp1qs6fP6/o6GhJUt++fVWjRg3Fx8dLkl5//XWNGTNGCQkJCg4Ots3N8fLykpeXl8P2AwAAlA8ODzeRkZE6ceKExowZo9TUVDVt2lSrV6+2TTI+evSonJz+f4Dp3XffVXZ2th588EG79cTFxWns2LFlWToAACiHHB5uJCkmJkYxMTGFPpaUlGR3PyUl5foXBAAAblgOv4gfAABAaSLcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUykX4WbmzJkKDg6Wm5ub2rRpo82bN1+x/5IlS9SwYUO5ubkpNDRUq1atKqNKAQBAeefwcLN48WLFxsYqLi5OW7duVVhYmCIiInT8+PFC+2/cuFGPPPKInnjiCW3btk09e/ZUz549tWvXrjKuHAAAlEcODzdTpkzRgAEDFB0drcaNG2vWrFny8PDQvHnzCu3/9ttv65577tGIESPUqFEjvfLKK2revLlmzJhRxpUDAIDyqIIjN56dna0tW7Zo1KhRtjYnJyeFh4dr06ZNhS6zadMmxcbG2rVFRERo2bJlhfbPyspSVlaW7X56erokKSMj4xqrL1x+1oXrst4bRYbFcHQJDpd3Mc/RJTjU9fps3Shu9u8Aie+Bm/07QLo+3wOX1mkYV39/OTTcnDx5Unl5eQoICLBrDwgI0N69ewtdJjU1tdD+qamphfaPj4/XuHHjCrQHBQWVsGpciY+jCygXfnZ0AQ7l8xTvgpsd74Cb+ztAur7fA2fPnpWPz5XX79BwUxZGjRplN9KTn5+vU6dOqUqVKrJYLA6sDKUtIyNDQUFBOnbsmLy9vR1dDgAH4HvAvAzD0NmzZ1W9evWr9nVouPHz85Ozs7PS0tLs2tPS0hQYGFjoMoGBgcXqb7VaZbVa7dp8fX1LXjTKPW9vb77UgJsc3wPmdLURm0scOqHY1dVVLVq0UGJioq0tPz9fiYmJatu2baHLtG3b1q6/JK1du/ay/QEAwM3F4YelYmNjFRUVpZYtW6p169aaOnWqzp8/r+joaElS3759VaNGDcXHx0uShg0bpo4dO2ry5Mnq2rWrFi1apP/973+aPXu2I3cDAACUEw4PN5GRkTpx4oTGjBmj1NRUNW3aVKtXr7ZNGj569KicnP5/gOmOO+5QQkKCXn75Zb344ouqV6+eli1bpltvvdVRu4Bywmq1Ki4ursBhSAA3D74HIEkWoyjnVAEAANwgHH4RPwAAgNJEuAEAAKZCuAEAAKZCuAEAAKZCuAEAAKZCuIFpzJw5U8HBwXJzc1ObNm20efNmR5cEoIysX79e3bt3V/Xq1WWxWC77Y8q4ORBuYAqLFy9WbGys4uLitHXrVoWFhSkiIkLHjx93dGkAysD58+cVFhammTNnOroUlANc5wam0KZNG7Vq1UozZsyQ9OfPeAQFBWnIkCEaOXKkg6sDUJYsFos+++wz9ezZ09GlwEEYucENLzs7W1u2bFF4eLitzcnJSeHh4dq0aZMDKwMAOALhBje8kydPKi8vz/aTHZcEBAQoNTXVQVUBAByFcAMAAEyFcIMbnp+fn5ydnZWWlmbXnpaWpsDAQAdVBQBwFMINbniurq5q0aKFEhMTbW35+flKTExU27ZtHVgZAMARKji6AKA0xMbGKioqSi1btlTr1q01depUnT9/XtHR0Y4uDUAZOHfunA4ePGi7n5ycrO3bt6ty5cqqVauWAyuDI3AqOExjxowZeuONN5SamqqmTZtq2rRpatOmjaPLAlAGkpKS1Llz5wLtUVFRmj9/ftkXBIci3AAAAFNhzg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADAVwg0AADCV/wOvrRj61RqQlAAAAABJRU5ErkJggg==\n"
|
|
},
|
|
"metadata": {}
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"cv_scores = [0.99, 1.00, 0.97, 0.99, 1.00]\n",
|
|
"\n",
|
|
"plt.figure(figsize=(7,4))\n",
|
|
"plt.plot(cv_scores, marker='o')\n",
|
|
"plt.title(\"Cross Validation Scores (Random Forest)\")\n",
|
|
"plt.xlabel(\"Fold\")\n",
|
|
"plt.ylabel(\"Score\")\n",
|
|
"plt.ylim(0,1)\n",
|
|
"plt.grid(True)\n",
|
|
"plt.show()\n"
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 410
|
|
},
|
|
"id": "EhayQ2eQyyIL",
|
|
"outputId": "1d8826c8-5f48-49c0-e31b-79ef8a4f6fa9"
|
|
},
|
|
"execution_count": 190,
|
|
"outputs": [
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 700x400 with 1 Axes>"
|
|
],
|
|
"image/png": 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\n"
|
|
},
|
|
"metadata": {}
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"logreg = LogisticRegression(max_iter=2000)\n",
|
|
"logreg.fit(X_train, y_train)\n",
|
|
"\n",
|
|
"pred_lr = logreg.predict(X_test)\n",
|
|
"\n",
|
|
"print(\"\\n=== Logistic Regression Accuracy ===\")\n",
|
|
"print(accuracy_score(y_test, pred_lr))\n",
|
|
"print(\"\\n=== Classification Report ===\")\n",
|
|
"print(classification_report(y_test, pred_lr))\n",
|
|
"print(\"\\n=== Confusion Matrix ===\")\n",
|
|
"print(confusion_matrix(y_test, pred_lr))\n",
|
|
"\n",
|
|
"cv_lr = cross_val_score(logreg, X, y, cv=5)\n",
|
|
"print(\"\\n=== Cross Validation (Logistic Regression) ===\")\n",
|
|
"print(cv_lr)\n",
|
|
"print(\"Mean CV Score:\", cv_lr.mean())\n",
|
|
"\n",
|
|
"\n"
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "8pScuIEBxcuQ",
|
|
"outputId": "d655cf36-3e97-4e91-8aac-f14d21924860"
|
|
},
|
|
"execution_count": 191,
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
|
|
"\n",
|
|
"=== Logistic Regression Accuracy ===\n",
|
|
"0.985\n",
|
|
"\n",
|
|
"=== Classification Report ===\n",
|
|
" precision recall f1-score support\n",
|
|
"\n",
|
|
" 0 0.98 1.00 0.99 190\n",
|
|
" 1 1.00 0.70 0.82 10\n",
|
|
"\n",
|
|
" accuracy 0.98 200\n",
|
|
" macro avg 0.99 0.85 0.91 200\n",
|
|
"weighted avg 0.99 0.98 0.98 200\n",
|
|
"\n",
|
|
"\n",
|
|
"=== Confusion Matrix ===\n",
|
|
"[[190 0]\n",
|
|
" [ 3 7]]\n",
|
|
"\n",
|
|
"=== Cross Validation (Logistic Regression) ===\n",
|
|
"[0.975 0.98 0.965 0.99 1. ]\n",
|
|
"Mean CV Score: 0.982\n"
|
|
]
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import numpy as np"
|
|
],
|
|
"metadata": {
|
|
"id": "PdQqzT5Xxgce"
|
|
},
|
|
"execution_count": 192,
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"accuracy = 0.985"
|
|
],
|
|
"metadata": {
|
|
"id": "RfPb1-Ao2WNr"
|
|
},
|
|
"execution_count": 193,
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"classes = [\"0\", \"1\"]\n",
|
|
"precision = [0.98, 1.00]\n",
|
|
"recall = [1.00, 0.70]\n",
|
|
"f1 = [0.99, 0.82]"
|
|
],
|
|
"metadata": {
|
|
"id": "0FViVUXj2Z3V"
|
|
},
|
|
"execution_count": 194,
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"conf_matrix = np.array([[190, 0],\n",
|
|
" [3, 7]])"
|
|
],
|
|
"metadata": {
|
|
"id": "jPpaJQ_Q2fNt"
|
|
},
|
|
"execution_count": 195,
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"cv_scores = np.array([0.975, 0.98, 0.965, 0.99, 1.0])"
|
|
],
|
|
"metadata": {
|
|
"id": "r4kFvyrF2i1J"
|
|
},
|
|
"execution_count": 196,
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"plt.figure()\n",
|
|
"plt.bar([\"Accuracy\"], [accuracy])\n",
|
|
"plt.title(\"Logistic Regression Accuracy\")\n",
|
|
"plt.ylabel(\"Score\")\n",
|
|
"plt.ylim(0, 1)\n",
|
|
"plt.show()"
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 452
|
|
},
|
|
"id": "dLeeMyia2oWm",
|
|
"outputId": "1c1108d5-2805-4517-c535-c7ef527fae03"
|
|
},
|
|
"execution_count": 197,
|
|
"outputs": [
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
],
|
|
"image/png": 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\n"
|
|
},
|
|
"metadata": {}
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"plt.figure()\n",
|
|
"plt.imshow(conf_matrix)\n",
|
|
"plt.title(\"Confusion Matrix\")\n",
|
|
"plt.colorbar()\n",
|
|
"plt.xticks([0,1], [\"Pred 0\", \"Pred 1\"])\n",
|
|
"plt.yticks([0,1], [\"Actual 0\", \"Actual 1\"])\n",
|
|
"plt.show()"
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 452
|
|
},
|
|
"id": "uqY9L5R82sCw",
|
|
"outputId": "a1c41775-17a9-48eb-bb69-69ff9bbd6650"
|
|
},
|
|
"execution_count": 198,
|
|
"outputs": [
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 2 Axes>"
|
|
],
|
|
"image/png": 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\n"
|
|
},
|
|
"metadata": {}
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"x = np.arange(len(classes))\n",
|
|
"width = 0.25\n",
|
|
"\n",
|
|
"plt.figure()\n",
|
|
"plt.bar(x - width, precision, width, label=\"Precision\")\n",
|
|
"plt.bar(x, recall, width, label=\"Recall\")\n",
|
|
"plt.bar(x + width, f1, width, label=\"F1-Score\")\n",
|
|
"plt.xticks(x, classes)\n",
|
|
"plt.ylabel(\"Score\")\n",
|
|
"plt.title(\"Classification Metrics per Class (Logistic Regression)\")\n",
|
|
"plt.legend()\n",
|
|
"plt.ylim(0, 1)\n",
|
|
"plt.show()"
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 452
|
|
},
|
|
"id": "5XsjRubu21tt",
|
|
"outputId": "c3ae9d33-851c-4c2d-86d0-88615473ecad"
|
|
},
|
|
"execution_count": 199,
|
|
"outputs": [
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
],
|
|
"image/png": 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\n"
|
|
},
|
|
"metadata": {}
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"plt.figure()\n",
|
|
"plt.plot(cv_scores, marker='o')\n",
|
|
"plt.title(\"Cross Validation Scores (Logistic Regression)\")\n",
|
|
"plt.xlabel(\"Fold\")\n",
|
|
"plt.ylabel(\"Score\")\n",
|
|
"plt.ylim(0, 1)\n",
|
|
"plt.show()"
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 472
|
|
},
|
|
"id": "sF-dgugW28Dt",
|
|
"outputId": "0da542a8-9334-4b4c-e44c-dccc95017a12"
|
|
},
|
|
"execution_count": 200,
|
|
"outputs": [
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
],
|
|
"image/png": 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\n"
|
|
},
|
|
"metadata": {}
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import numpy as np\n",
|
|
"\n",
|
|
"# DATA DARI HASIL MODEL\n",
|
|
"\n",
|
|
"# --- Accuracy ---\n",
|
|
"acc_rf = 0.99\n",
|
|
"acc_lr = 0.985\n",
|
|
"\n",
|
|
"# --- Cross Validation ---\n",
|
|
"cv_rf = [0.99, 1.0, 0.97, 0.99, 1.0]\n",
|
|
"cv_lr = [0.975, 0.98, 0.965, 0.99, 1.0]\n",
|
|
"\n",
|
|
"# --- Classification Metrics ---\n",
|
|
"precision_rf = [0.99, 1.00]\n",
|
|
"recall_rf = [1.00, 0.80]\n",
|
|
"f1_rf = [0.99, 0.89]\n",
|
|
"\n",
|
|
"precision_lr = [0.98, 1.00]\n",
|
|
"recall_lr = [1.00, 0.70]\n",
|
|
"f1_lr = [0.99, 0.82]\n",
|
|
"\n",
|
|
"classes = [\"0\", \"1\"]\n",
|
|
"\n",
|
|
"# 1. PERBANDINGAN AKURASI\n",
|
|
"plt.figure()\n",
|
|
"plt.bar([\"Random Forest\", \"Logistic Regression\"], [acc_rf, acc_lr])\n",
|
|
"plt.title(\"Perbandingan Akurasi\")\n",
|
|
"plt.ylabel(\"Accuracy\")\n",
|
|
"plt.ylim(0, 1)\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"\n",
|
|
"# 2. PERBANDINGAN CROSS VALIDATION\n",
|
|
"plt.figure()\n",
|
|
"plt.plot(cv_rf, marker=\"o\", label=\"Random Forest\")\n",
|
|
"plt.plot(cv_lr, marker=\"o\", label=\"Logistic Regression\")\n",
|
|
"plt.title(\"Cross Validation Comparison\")\n",
|
|
"plt.xlabel(\"Fold\")\n",
|
|
"plt.ylabel(\"Score\")\n",
|
|
"plt.ylim(0, 1)\n",
|
|
"plt.legend()\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"# 3. PERBANDINGAN METRIK KELAS\n",
|
|
"\n",
|
|
"x = np.arange(len(classes))\n",
|
|
"width = 0.35\n",
|
|
"\n",
|
|
"plt.figure()\n",
|
|
"plt.bar(x - width/2, precision_rf, width, label=\"RF Precision\")\n",
|
|
"plt.bar(x + width/2, precision_lr, width, label=\"LR Precision\")\n",
|
|
"plt.xticks(x, classes)\n",
|
|
"plt.title(\"Precision per Class\")\n",
|
|
"plt.ylabel(\"Score\")\n",
|
|
"plt.ylim(0, 1)\n",
|
|
"plt.legend()\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"plt.figure()\n",
|
|
"plt.bar(x - width/2, recall_rf, width, label=\"RF Recall\")\n",
|
|
"plt.bar(x + width/2, recall_lr, width, label=\"LR Recall\")\n",
|
|
"plt.xticks(x, classes)\n",
|
|
"plt.title(\"Recall per Class\")\n",
|
|
"plt.ylabel(\"Score\")\n",
|
|
"plt.ylim(0, 1)\n",
|
|
"plt.legend()\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"plt.figure()\n",
|
|
"plt.bar(x - width/2, f1_rf, width, label=\"RF F1\")\n",
|
|
"plt.bar(x + width/2, f1_lr, width, label=\"LR F1\")\n",
|
|
"plt.xticks(x, classes)\n",
|
|
"plt.title(\"F1-Score per Class\")\n",
|
|
"plt.ylabel(\"Score\")\n",
|
|
"plt.ylim(0, 1)\n",
|
|
"plt.legend()\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"# 4. CONFUSION MATRIX RF vs LR\n",
|
|
"conf_rf = np.array([[190, 0],\n",
|
|
" [2, 8]])\n",
|
|
"\n",
|
|
"conf_lr = np.array([[190, 0],\n",
|
|
" [3, 7]])\n",
|
|
"\n",
|
|
"plt.figure()\n",
|
|
"plt.imshow(conf_rf)\n",
|
|
"plt.title(\"Confusion Matrix - Random Forest\")\n",
|
|
"plt.colorbar()\n",
|
|
"plt.xticks([0, 1], [\"Pred 0\", \"Pred 1\"])\n",
|
|
"plt.yticks([0, 1], [\"Actual 0\", \"Actual 1\"])\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"plt.figure()\n",
|
|
"plt.imshow(conf_lr)\n",
|
|
"plt.title(\"Confusion Matrix - Logistic Regression\")\n",
|
|
"plt.colorbar()\n",
|
|
"plt.xticks([0, 1], [\"Pred 0\", \"Pred 1\"])\n",
|
|
"plt.yticks([0, 1], [\"Actual 0\", \"Actual 1\"])\n",
|
|
"plt.show()\n"
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 1000
|
|
},
|
|
"id": "Ou8UKwHH3HqG",
|
|
"outputId": "9ee46e90-2d68-4282-a434-6ef62685c915"
|
|
},
|
|
"execution_count": 201,
|
|
"outputs": [
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
],
|
|
"image/png": 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\n"
|
|
},
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
|
"text/plain": [
|
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"<Figure size 640x480 with 1 Axes>"
|
|
],
|
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"image/png": 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\n"
|
|
},
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
|
"text/plain": [
|
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"<Figure size 640x480 with 1 Axes>"
|
|
],
|
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"image/png": 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\n"
|
|
},
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
|
"text/plain": [
|
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"<Figure size 640x480 with 1 Axes>"
|
|
],
|
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"image/png": 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\n"
|
|
},
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
|
"text/plain": [
|
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"<Figure size 640x480 with 1 Axes>"
|
|
],
|
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"image/png": 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\n"
|
|
},
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 2 Axes>"
|
|
],
|
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"image/png": 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\n"
|
|
},
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
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"text/plain": [
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"<Figure size 640x480 with 2 Axes>"
|
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],
|
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"image/png": 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\n"
|
|
},
|
|
"metadata": {}
|
|
}
|
|
]
|
|
}
|
|
]
|
|
} |