Implementasi Lima Algoritma Clustering untuk Menentukan Negara Prioritas Penerima Bantuan
Eka Aliya (24031554012) & Hulwah Fatin Fathinah (24031554007)
2026-04-14
Pendahuluan
Penelitian ini bertujuan untuk mengelompokkan negara berdasarkan indikator sosial-ekonomi guna mengidentifikasi negara yang menjadi prioritas penerima bantuan dari HELP International. Analisis dilakukan menggunakan beberapa metode clustering untuk membandingkan hasil dan menentukan metode terbaik berdasarkan evaluasi metrik.
Import Library
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.1 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.2 ✔ tibble 3.3.1
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ✔ purrr 1.2.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(cluster)
library(dbscan)##
## Attaching package: 'dbscan'
##
## The following object is masked from 'package:stats':
##
## as.dendrogramlibrary(e1071)##
## Attaching package: 'e1071'
##
## The following object is masked from 'package:ggplot2':
##
## elementlibrary(factoextra)## Welcome to factoextra!
## Want to learn more? See two factoextra-related books at https://www.datanovia.com/en/product/practical-guide-to-principal-component-methods-in-r/library(meanShiftR)
library(ggplot2)Load Dataset
Dataset yang digunakan adalah Country Data yang berisi informasi sosial-ekonomi dari berbagai negara.
data_country <- read_csv("Country-data.csv")## Rows: 167 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): country
## dbl (9): child_mort, exports, health, imports, income, inflation, life_expec...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Preprocessing Data
Data kemudian dikonversi menjadi format data frame dan dilakukan pemisahan antara variabel identitas (nama negara) dengan variabel numerik. Nama negara dijadikan sebagai row names untuk mempermudah identifikasi hasil clustering.
Selanjutnya, dilakukan normalisasi data menggunakan metode Z-score (scale()) agar seluruh variabel memiliki skala yang sebanding dan tidak mendominasi hasil analisis.
data_country <- as.data.frame(data_country)
print(data_country)## country child_mort exports health imports income
## 1 Afghanistan 90.2 10.000 7.58 44.9000 1610
## 2 Albania 16.6 28.000 6.55 48.6000 9930
## 3 Algeria 27.3 38.400 4.17 31.4000 12900
## 4 Angola 119.0 62.300 2.85 42.9000 5900
## 5 Antigua and Barbuda 10.3 45.500 6.03 58.9000 19100
## 6 Argentina 14.5 18.900 8.10 16.0000 18700
## 7 Armenia 18.1 20.800 4.40 45.3000 6700
## 8 Australia 4.8 19.800 8.73 20.9000 41400
## 9 Austria 4.3 51.300 11.00 47.8000 43200
## 10 Azerbaijan 39.2 54.300 5.88 20.7000 16000
## 11 Bahamas 13.8 35.000 7.89 43.7000 22900
## 12 Bahrain 8.6 69.500 4.97 50.9000 41100
## 13 Bangladesh 49.4 16.000 3.52 21.8000 2440
## 14 Barbados 14.2 39.500 7.97 48.7000 15300
## 15 Belarus 5.5 51.400 5.61 64.5000 16200
## 16 Belgium 4.5 76.400 10.70 74.7000 41100
## 17 Belize 18.8 58.200 5.20 57.5000 7880
## 18 Benin 111.0 23.800 4.10 37.2000 1820
## 19 Bhutan 42.7 42.500 5.20 70.7000 6420
## 20 Bolivia 46.6 41.200 4.84 34.3000 5410
## 21 Bosnia and Herzegovina 6.9 29.700 11.10 51.3000 9720
## 22 Botswana 52.5 43.600 8.30 51.3000 13300
## 23 Brazil 19.8 10.700 9.01 11.8000 14500
## 24 Brunei 10.5 67.400 2.84 28.0000 80600
## 25 Bulgaria 10.8 50.200 6.87 53.0000 15300
## 26 Burkina Faso 116.0 19.200 6.74 29.6000 1430
## 27 Burundi 93.6 8.920 11.60 39.2000 764
## 28 Cambodia 44.4 54.100 5.68 59.5000 2520
## 29 Cameroon 108.0 22.200 5.13 27.0000 2660
## 30 Canada 5.6 29.100 11.30 31.0000 40700
## 31 Cape Verde 26.5 32.700 4.09 61.8000 5830
## 32 Central African Republic 149.0 11.800 3.98 26.5000 888
## 33 Chad 150.0 36.800 4.53 43.5000 1930
## 34 Chile 8.7 37.700 7.96 31.3000 19400
## 35 China 15.7 26.300 5.07 22.6000 9530
## 36 Colombia 18.6 15.900 7.59 17.8000 10900
## 37 Comoros 88.2 16.500 4.51 51.7000 1410
## 38 Congo, Dem. Rep. 116.0 41.100 7.91 49.6000 609
## 39 Congo, Rep. 63.9 85.100 2.46 54.7000 5190
## 40 Costa Rica 10.2 33.200 10.90 35.0000 13000
## 41 Cote d'Ivoire 111.0 50.600 5.30 43.3000 2690
## 42 Croatia 5.5 37.600 7.76 38.1000 20100
## 43 Cyprus 3.6 50.200 5.97 57.5000 33900
## 44 Czech Republic 3.4 66.000 7.88 62.9000 28300
## 45 Denmark 4.1 50.500 11.40 43.6000 44000
## 46 Dominican Republic 34.4 22.700 6.22 33.3000 11100
## 47 Ecuador 25.1 27.900 8.06 32.4000 9350
## 48 Egypt 29.1 21.300 4.66 26.6000 9860
## 49 El Salvador 19.2 26.900 6.91 46.6000 7300
## 50 Equatorial Guinea 111.0 85.800 4.48 58.9000 33700
## 51 Eritrea 55.2 4.790 2.66 23.3000 1420
## 52 Estonia 4.5 75.100 6.03 68.7000 22700
## 53 Fiji 24.1 57.800 4.86 63.9000 7350
## 54 Finland 3.0 38.700 8.95 37.4000 39800
## 55 France 4.2 26.800 11.90 28.1000 36900
## 56 Gabon 63.7 57.700 3.50 18.9000 15400
## 57 Gambia 80.3 23.800 5.69 42.7000 1660
## 58 Georgia 16.5 35.000 10.10 52.8000 6730
## 59 Germany 4.2 42.300 11.60 37.1000 40400
## 60 Ghana 74.7 29.500 5.22 45.9000 3060
## 61 Greece 3.9 22.100 10.30 30.7000 28700
## 62 Grenada 14.6 23.800 5.86 49.2000 11200
## 63 Guatemala 35.4 25.800 6.85 36.3000 6710
## 64 Guinea 109.0 30.300 4.93 43.2000 1190
## 65 Guinea-Bissau 114.0 14.900 8.50 35.2000 1390
## 66 Guyana 37.6 51.400 5.38 79.1000 5840
## 67 Haiti 208.0 15.300 6.91 64.7000 1500
## 68 Hungary 6.0 81.800 7.33 76.5000 22300
## 69 Iceland 2.6 53.400 9.40 43.3000 38800
## 70 India 58.8 22.600 4.05 27.1000 4410
## 71 Indonesia 33.3 24.300 2.61 22.4000 8430
## 72 Iran 19.3 24.400 5.60 19.4000 17400
## 73 Iraq 36.9 39.400 8.41 34.1000 12700
## 74 Ireland 4.2 103.000 9.19 86.5000 45700
## 75 Israel 4.6 35.000 7.63 32.9000 29600
## 76 Italy 4.0 25.200 9.53 27.2000 36200
## 77 Jamaica 18.1 31.300 4.81 49.6000 8000
## 78 Japan 3.2 15.000 9.49 13.6000 35800
## 79 Jordan 21.1 48.300 8.04 69.0000 9470
## 80 Kazakhstan 21.5 44.200 4.29 29.9000 20100
## 81 Kenya 62.2 20.700 4.75 33.6000 2480
## 82 Kiribati 62.7 13.300 11.30 79.9000 1730
## 83 Kuwait 10.8 66.700 2.63 30.4000 75200
## 84 Kyrgyz Republic 29.6 51.600 6.18 81.7000 2790
## 85 Lao 78.9 35.400 4.47 49.3000 3980
## 86 Latvia 7.8 53.700 6.68 55.1000 18300
## 87 Lebanon 10.3 35.800 7.03 60.2000 16300
## 88 Lesotho 99.7 39.400 11.10 101.0000 2380
## 89 Liberia 89.3 19.100 11.80 92.6000 700
## 90 Libya 16.6 65.600 3.88 42.1000 29600
## 91 Lithuania 6.1 65.300 7.04 67.2000 21100
## 92 Luxembourg 2.8 175.000 7.77 142.0000 91700
## 93 Macedonia, FYR 10.4 39.800 7.09 58.1000 11400
## 94 Madagascar 62.2 25.000 3.77 43.0000 1390
## 95 Malawi 90.5 22.800 6.59 34.9000 1030
## 96 Malaysia 7.9 86.900 4.39 71.0000 21100
## 97 Maldives 13.2 77.600 6.33 65.4000 10500
## 98 Mali 137.0 22.800 4.98 35.1000 1870
## 99 Malta 6.8 153.000 8.65 154.0000 28300
## 100 Mauritania 97.4 50.700 4.41 61.2000 3320
## 101 Mauritius 15.0 51.200 6.00 62.2000 15900
## 102 Micronesia, Fed. Sts. 40.0 23.500 14.20 81.0000 3340
## 103 Moldova 17.2 39.200 11.70 78.5000 3910
## 104 Mongolia 26.1 46.700 5.44 56.7000 7710
## 105 Montenegro 6.8 37.000 9.11 62.7000 14000
## 106 Morocco 33.5 32.200 5.20 43.0000 6440
## 107 Mozambique 101.0 31.500 5.21 46.2000 918
## 108 Myanmar 64.4 0.109 1.97 0.0659 3720
## 109 Namibia 56.0 47.800 6.78 60.7000 8460
## 110 Nepal 47.0 9.580 5.25 36.4000 1990
## 111 Netherlands 4.5 72.000 11.90 63.6000 45500
## 112 New Zealand 6.2 30.300 10.10 28.0000 32300
## 113 Niger 123.0 22.200 5.16 49.1000 814
## 114 Nigeria 130.0 25.300 5.07 17.4000 5150
## 115 Norway 3.2 39.700 9.48 28.5000 62300
## 116 Oman 11.7 65.700 2.77 41.2000 45300
## 117 Pakistan 92.1 13.500 2.20 19.4000 4280
## 118 Panama 19.7 70.000 8.10 78.2000 15400
## 119 Paraguay 24.1 55.100 5.87 51.5000 7290
## 120 Peru 20.3 27.800 5.08 23.8000 9960
## 121 Philippines 31.9 34.800 3.61 36.6000 5600
## 122 Poland 6.0 40.100 7.46 42.1000 21800
## 123 Portugal 3.9 29.900 11.00 37.4000 27200
## 124 Qatar 9.0 62.300 1.81 23.8000 125000
## 125 Romania 11.5 32.600 5.58 38.8000 17800
## 126 Russia 10.0 29.200 5.08 21.1000 23100
## 127 Rwanda 63.6 12.000 10.50 30.0000 1350
## 128 Samoa 18.9 29.200 6.47 53.1000 5400
## 129 Saudi Arabia 15.7 49.600 4.29 33.0000 45400
## 130 Senegal 66.8 24.900 5.66 40.3000 2180
## 131 Serbia 7.6 32.900 10.40 47.9000 12700
## 132 Seychelles 14.4 93.800 3.40 108.0000 20400
## 133 Sierra Leone 160.0 16.800 13.10 34.5000 1220
## 134 Singapore 2.8 200.000 3.96 174.0000 72100
## 135 Slovak Republic 7.0 76.300 8.79 77.8000 25200
## 136 Slovenia 3.2 64.300 9.41 62.9000 28700
## 137 Solomon Islands 28.1 49.300 8.55 81.2000 1780
## 138 South Africa 53.7 28.600 8.94 27.4000 12000
## 139 South Korea 4.1 49.400 6.93 46.2000 30400
## 140 Spain 3.8 25.500 9.54 26.8000 32500
## 141 Sri Lanka 11.2 19.600 2.94 26.8000 8560
## 142 St. Vincent and the Grenadines 20.7 26.900 4.47 57.1000 9920
## 143 Sudan 76.7 19.700 6.32 17.2000 3370
## 144 Suriname 24.1 52.500 7.01 38.4000 14200
## 145 Sweden 3.0 46.200 9.63 40.7000 42900
## 146 Switzerland 4.5 64.000 11.50 53.3000 55500
## 147 Tajikistan 52.4 14.900 5.98 58.6000 2110
## 148 Tanzania 71.9 18.700 6.01 29.1000 2090
## 149 Thailand 14.9 66.500 3.88 60.8000 13500
## 150 Timor-Leste 62.6 2.200 9.12 27.8000 1850
## 151 Togo 90.3 40.200 7.65 57.3000 1210
## 152 Tonga 17.4 12.400 5.07 60.3000 4980
## 153 Tunisia 17.4 50.500 6.21 55.3000 10400
## 154 Turkey 19.1 20.400 6.74 25.5000 18000
## 155 Turkmenistan 62.0 76.300 2.50 44.5000 9940
## 156 Uganda 81.0 17.100 9.01 28.6000 1540
## 157 Ukraine 11.7 47.100 7.72 51.1000 7820
## 158 United Arab Emirates 8.6 77.700 3.66 63.6000 57600
## 159 United Kingdom 5.2 28.200 9.64 30.8000 36200
## 160 United States 7.3 12.400 17.90 15.8000 49400
## 161 Uruguay 10.6 26.300 8.35 25.4000 17100
## 162 Uzbekistan 36.3 31.700 5.81 28.5000 4240
## 163 Vanuatu 29.2 46.600 5.25 52.7000 2950
## 164 Venezuela 17.1 28.500 4.91 17.6000 16500
## 165 Vietnam 23.3 72.000 6.84 80.2000 4490
## 166 Yemen 56.3 30.000 5.18 34.4000 4480
## 167 Zambia 83.1 37.000 5.89 30.9000 3280
## inflation life_expec total_fer gdpp
## 1 9.440 56.2 5.82 553
## 2 4.490 76.3 1.65 4090
## 3 16.100 76.5 2.89 4460
## 4 22.400 60.1 6.16 3530
## 5 1.440 76.8 2.13 12200
## 6 20.900 75.8 2.37 10300
## 7 7.770 73.3 1.69 3220
## 8 1.160 82.0 1.93 51900
## 9 0.873 80.5 1.44 46900
## 10 13.800 69.1 1.92 5840
## 11 -0.393 73.8 1.86 28000
## 12 7.440 76.0 2.16 20700
## 13 7.140 70.4 2.33 758
## 14 0.321 76.7 1.78 16000
## 15 15.100 70.4 1.49 6030
## 16 1.880 80.0 1.86 44400
## 17 1.140 71.4 2.71 4340
## 18 0.885 61.8 5.36 758
## 19 5.990 72.1 2.38 2180
## 20 8.780 71.6 3.20 1980
## 21 1.400 76.8 1.31 4610
## 22 8.920 57.1 2.88 6350
## 23 8.410 74.2 1.80 11200
## 24 16.700 77.1 1.84 35300
## 25 1.110 73.9 1.57 6840
## 26 6.810 57.9 5.87 575
## 27 12.300 57.7 6.26 231
## 28 3.120 66.1 2.88 786
## 29 1.910 57.3 5.11 1310
## 30 2.870 81.3 1.63 47400
## 31 0.505 72.5 2.67 3310
## 32 2.010 47.5 5.21 446
## 33 6.390 56.5 6.59 897
## 34 8.960 79.1 1.88 12900
## 35 6.940 74.6 1.59 4560
## 36 3.860 76.4 2.01 6250
## 37 3.870 65.9 4.75 769
## 38 20.800 57.5 6.54 334
## 39 20.700 60.4 4.95 2740
## 40 6.570 80.4 1.92 8200
## 41 5.390 56.3 5.27 1220
## 42 0.821 76.3 1.55 13500
## 43 2.010 79.9 1.42 30800
## 44 -1.430 77.5 1.51 19800
## 45 3.220 79.5 1.87 58000
## 46 5.440 74.6 2.60 5450
## 47 7.470 76.7 2.66 4660
## 48 10.100 70.5 3.19 2600
## 49 2.650 74.1 2.27 2990
## 50 24.900 60.9 5.21 17100
## 51 11.600 61.7 4.61 482
## 52 1.740 76.0 1.72 14600
## 53 4.230 65.3 2.67 3650
## 54 0.351 80.0 1.87 46200
## 55 1.050 81.4 2.03 40600
## 56 16.600 62.9 4.08 8750
## 57 4.300 65.5 5.71 562
## 58 8.550 72.8 1.92 2960
## 59 0.758 80.1 1.39 41800
## 60 16.600 62.2 4.27 1310
## 61 0.673 80.4 1.48 26900
## 62 0.480 71.3 2.24 7370
## 63 5.140 71.3 3.38 2830
## 64 16.100 58.0 5.34 648
## 65 2.970 55.6 5.05 547
## 66 5.730 65.5 2.65 3040
## 67 5.450 32.1 3.33 662
## 68 2.330 74.5 1.25 13100
## 69 5.470 82.0 2.20 41900
## 70 8.980 66.2 2.60 1350
## 71 15.300 69.9 2.48 3110
## 72 15.900 74.5 1.76 6530
## 73 16.600 67.2 4.56 4500
## 74 -3.220 80.4 2.05 48700
## 75 1.770 81.4 3.03 30600
## 76 0.319 81.7 1.46 35800
## 77 9.810 74.7 2.17 4680
## 78 -1.900 82.8 1.39 44500
## 79 8.430 75.8 3.66 3680
## 80 19.500 68.4 2.60 9070
## 81 2.090 62.8 4.37 967
## 82 1.520 60.7 3.84 1490
## 83 11.200 78.2 2.21 38500
## 84 10.000 68.5 3.10 880
## 85 9.200 63.8 3.15 1140
## 86 -0.812 73.1 1.36 11300
## 87 0.238 79.8 1.61 8860
## 88 4.150 46.5 3.30 1170
## 89 5.470 60.8 5.02 327
## 90 14.200 76.1 2.41 12100
## 91 2.380 73.2 1.50 12000
## 92 3.620 81.3 1.63 105000
## 93 2.040 74.0 1.47 4540
## 94 8.790 60.8 4.60 413
## 95 12.100 53.1 5.31 459
## 96 7.270 74.5 2.15 9070
## 97 2.880 77.9 2.23 7100
## 98 4.370 59.5 6.55 708
## 99 3.830 80.3 1.36 21100
## 100 18.900 68.2 4.98 1200
## 101 1.130 73.4 1.57 8000
## 102 3.800 65.4 3.46 2860
## 103 11.100 69.7 1.27 1630
## 104 39.200 66.2 2.64 2650
## 105 1.600 76.4 1.77 6680
## 106 0.976 73.5 2.58 2830
## 107 7.640 54.5 5.56 419
## 108 7.040 66.8 2.41 988
## 109 3.560 58.6 3.60 5190
## 110 15.100 68.3 2.61 592
## 111 0.848 80.7 1.79 50300
## 112 3.730 80.9 2.17 33700
## 113 2.550 58.8 7.49 348
## 114 104.000 60.5 5.84 2330
## 115 5.950 81.0 1.95 87800
## 116 15.600 76.1 2.90 19300
## 117 10.900 65.3 3.85 1040
## 118 2.590 77.8 2.62 8080
## 119 6.100 74.1 2.73 3230
## 120 5.710 77.9 2.54 5020
## 121 4.220 69.0 3.16 2130
## 122 1.660 76.3 1.41 12600
## 123 0.643 79.8 1.39 22500
## 124 6.980 79.5 2.07 70300
## 125 3.530 73.7 1.59 8230
## 126 14.200 69.2 1.57 10700
## 127 2.610 64.6 4.51 563
## 128 1.720 71.5 4.34 3450
## 129 17.200 75.1 2.96 19300
## 130 1.850 64.0 5.06 1000
## 131 5.880 74.7 1.40 5410
## 132 -4.210 73.4 2.17 10800
## 133 17.200 55.0 5.20 399
## 134 -0.046 82.7 1.15 46600
## 135 0.485 75.5 1.43 16600
## 136 -0.987 79.5 1.57 23400
## 137 6.810 61.7 4.24 1290
## 138 6.350 54.3 2.59 7280
## 139 3.160 80.1 1.23 22100
## 140 0.160 81.9 1.37 30700
## 141 22.800 74.4 2.20 2810
## 142 4.440 71.6 2.07 6230
## 143 19.600 66.3 4.88 1480
## 144 7.200 70.3 2.52 8300
## 145 0.991 81.5 1.98 52100
## 146 0.317 82.2 1.52 74600
## 147 12.500 69.6 3.51 738
## 148 9.250 59.3 5.43 702
## 149 4.080 76.6 1.55 5080
## 150 26.500 71.1 6.23 3600
## 151 1.180 58.7 4.87 488
## 152 3.680 69.9 3.91 3550
## 153 3.820 76.9 2.14 4140
## 154 7.010 78.2 2.15 10700
## 155 2.310 67.9 2.83 4440
## 156 10.600 56.8 6.15 595
## 157 13.400 70.4 1.44 2970
## 158 12.500 76.5 1.87 35000
## 159 1.570 80.3 1.92 38900
## 160 1.220 78.7 1.93 48400
## 161 4.910 76.4 2.08 11900
## 162 16.500 68.8 2.34 1380
## 163 2.620 63.0 3.50 2970
## 164 45.900 75.4 2.47 13500
## 165 12.100 73.1 1.95 1310
## 166 23.600 67.5 4.67 1310
## 167 14.000 52.0 5.40 1460rownames(data_country) <- data_country$country
data_country_numeric <- data_country[, -1]
data_country_scaled <- scale(data_country_numeric)summary(data_country_scaled)## child_mort exports health imports
## Min. :-0.8845 Min. :-1.4957 Min. :-1.8223 Min. :-1.9341
## 1st Qu.:-0.7444 1st Qu.:-0.6314 1st Qu.:-0.6901 1st Qu.:-0.6894
## Median :-0.4704 Median :-0.2229 Median :-0.1805 Median :-0.1483
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.5909 3rd Qu.: 0.3736 3rd Qu.: 0.6496 3rd Qu.: 0.4899
## Max. : 4.2086 Max. : 5.7964 Max. : 4.0353 Max. : 5.2504
## income inflation life_expec total_fer
## Min. :-0.8577 Min. :-1.1344 Min. :-4.3242 Min. :-1.1877
## 1st Qu.:-0.7153 1st Qu.:-0.5649 1st Qu.:-0.5910 1st Qu.:-0.7616
## Median :-0.3727 Median :-0.2263 Median : 0.2861 Median :-0.3554
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.2934 3rd Qu.: 0.2808 3rd Qu.: 0.7021 3rd Qu.: 0.6157
## Max. : 5.5947 Max. : 9.1023 Max. : 1.3768 Max. : 3.0003
## gdpp
## Min. :-0.69471
## 1st Qu.:-0.63475
## Median :-0.45307
## Mean : 0.00000
## 3rd Qu.: 0.05924
## Max. : 5.02140Menentukan Jumlah Cluster Optimal
Penentuan jumlah cluster optimal dilakukan menggunakan metode Silhouette Analysis. Visualisasi menunjukkan bahwa nilai terbaik diperoleh pada k = 2, sehingga analisis selanjutnya menggunakan dua cluster.
fviz_nbclust(data_country_scaled, kmeans, method = "silhouette")
fviz_nbclust(data_country_scaled, pam, method = "silhouette")
## K-Means Clustering Metode K-Means diterapkan dengan jumlah cluster
sebanyak 2. Algoritma ini bekerja dengan membagi data ke dalam kelompok
berdasarkan jarak terdekat terhadap centroid. Hasil clustering
menunjukkan pembagian negara ke dalam dua kelompok utama.
set.seed(123)
kmeans_data <- kmeans(data_country_scaled, centers = 2, nstart = 25)
print(kmeans_data)## K-means clustering with 2 clusters of sizes 99, 68
##
## Cluster means:
## child_mort exports health imports income inflation life_expec
## 1 -0.6473874 0.2733858 0.1814275 0.09215345 0.4602372 -0.2155284 0.6677315
## 2 0.9425200 -0.3980176 -0.2641371 -0.13416458 -0.6700512 0.3137840 -0.9721385
## total_fer gdpp
## 1 -0.6650953 0.4115852
## 2 0.9683006 -0.5992197
##
## Clustering vector:
## Afghanistan Albania
## 2 1
## Algeria Angola
## 1 2
## Antigua and Barbuda Argentina
## 1 1
## Armenia Australia
## 1 1
## Austria Azerbaijan
## 1 1
## Bahamas Bahrain
## 1 1
## Bangladesh Barbados
## 2 1
## Belarus Belgium
## 1 1
## Belize Benin
## 1 2
## Bhutan Bolivia
## 1 2
## Bosnia and Herzegovina Botswana
## 1 2
## Brazil Brunei
## 1 1
## Bulgaria Burkina Faso
## 1 2
## Burundi Cambodia
## 2 2
## Cameroon Canada
## 2 1
## Cape Verde Central African Republic
## 1 2
## Chad Chile
## 2 1
## China Colombia
## 1 1
## Comoros Congo, Dem. Rep.
## 2 2
## Congo, Rep. Costa Rica
## 2 1
## Cote d'Ivoire Croatia
## 2 1
## Cyprus Czech Republic
## 1 1
## Denmark Dominican Republic
## 1 1
## Ecuador Egypt
## 1 2
## El Salvador Equatorial Guinea
## 1 2
## Eritrea Estonia
## 2 1
## Fiji Finland
## 1 1
## France Gabon
## 1 2
## Gambia Georgia
## 2 1
## Germany Ghana
## 1 2
## Greece Grenada
## 1 1
## Guatemala Guinea
## 2 2
## Guinea-Bissau Guyana
## 2 2
## Haiti Hungary
## 2 1
## Iceland India
## 1 2
## Indonesia Iran
## 2 1
## Iraq Ireland
## 2 1
## Israel Italy
## 1 1
## Jamaica Japan
## 1 1
## Jordan Kazakhstan
## 1 1
## Kenya Kiribati
## 2 2
## Kuwait Kyrgyz Republic
## 1 2
## Lao Latvia
## 2 1
## Lebanon Lesotho
## 1 2
## Liberia Libya
## 2 1
## Lithuania Luxembourg
## 1 1
## Macedonia, FYR Madagascar
## 1 2
## Malawi Malaysia
## 2 1
## Maldives Mali
## 1 2
## Malta Mauritania
## 1 2
## Mauritius Micronesia, Fed. Sts.
## 1 2
## Moldova Mongolia
## 1 2
## Montenegro Morocco
## 1 1
## Mozambique Myanmar
## 2 2
## Namibia Nepal
## 2 2
## Netherlands New Zealand
## 1 1
## Niger Nigeria
## 2 2
## Norway Oman
## 1 1
## Pakistan Panama
## 2 1
## Paraguay Peru
## 1 1
## Philippines Poland
## 2 1
## Portugal Qatar
## 1 1
## Romania Russia
## 1 1
## Rwanda Samoa
## 2 2
## Saudi Arabia Senegal
## 1 2
## Serbia Seychelles
## 1 1
## Sierra Leone Singapore
## 2 1
## Slovak Republic Slovenia
## 1 1
## Solomon Islands South Africa
## 2 2
## South Korea Spain
## 1 1
## Sri Lanka St. Vincent and the Grenadines
## 1 1
## Sudan Suriname
## 2 1
## Sweden Switzerland
## 1 1
## Tajikistan Tanzania
## 2 2
## Thailand Timor-Leste
## 1 2
## Togo Tonga
## 2 2
## Tunisia Turkey
## 1 1
## Turkmenistan Uganda
## 2 2
## Ukraine United Arab Emirates
## 1 1
## United Kingdom United States
## 1 1
## Uruguay Uzbekistan
## 1 2
## Vanuatu Venezuela
## 2 1
## Vietnam Yemen
## 1 2
## Zambia
## 2
##
## Within cluster sum of squares by cluster:
## [1] 643.3747 400.5512
## (between_SS / total_SS = 30.1 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"K-Median Clustering
Sebagai pembanding, digunakan metode K-Median (PAM) yang lebih robust terhadap outlier. Hasil yang diperoleh menunjukkan pola pengelompokan yang serupa dengan K-Means.
kmedian_data <- pam(data_country_scaled, k = 2)
print(kmedian_data)## Medoids:
## ID child_mort exports health imports income inflation
## Ghana 60 0.9033202 -0.42349963 -0.5809185 -0.04090177 -0.7306069 0.8342082
## Poland 122 -0.8001715 -0.03680781 0.2345648 -0.19786437 0.2414823 -0.5791319
## life_expec total_fer gdpp
## Ghana -0.9395623 0.8732953 -0.63584175
## Poland 0.6459238 -1.0159306 -0.01986805
## Clustering vector:
## Afghanistan Albania
## 1 2
## Algeria Angola
## 2 1
## Antigua and Barbuda Argentina
## 2 2
## Armenia Australia
## 2 2
## Austria Azerbaijan
## 2 2
## Bahamas Bahrain
## 2 2
## Bangladesh Barbados
## 1 2
## Belarus Belgium
## 2 2
## Belize Benin
## 2 1
## Bhutan Bolivia
## 2 1
## Bosnia and Herzegovina Botswana
## 2 1
## Brazil Brunei
## 2 2
## Bulgaria Burkina Faso
## 2 1
## Burundi Cambodia
## 1 1
## Cameroon Canada
## 1 2
## Cape Verde Central African Republic
## 2 1
## Chad Chile
## 1 2
## China Colombia
## 2 2
## Comoros Congo, Dem. Rep.
## 1 1
## Congo, Rep. Costa Rica
## 1 2
## Cote d'Ivoire Croatia
## 1 2
## Cyprus Czech Republic
## 2 2
## Denmark Dominican Republic
## 2 2
## Ecuador Egypt
## 2 1
## El Salvador Equatorial Guinea
## 2 1
## Eritrea Estonia
## 1 2
## Fiji Finland
## 2 2
## France Gabon
## 2 1
## Gambia Georgia
## 1 2
## Germany Ghana
## 2 1
## Greece Grenada
## 2 2
## Guatemala Guinea
## 2 1
## Guinea-Bissau Guyana
## 1 1
## Haiti Hungary
## 1 2
## Iceland India
## 2 1
## Indonesia Iran
## 1 2
## Iraq Ireland
## 1 2
## Israel Italy
## 2 2
## Jamaica Japan
## 2 2
## Jordan Kazakhstan
## 2 1
## Kenya Kiribati
## 1 1
## Kuwait Kyrgyz Republic
## 2 1
## Lao Latvia
## 1 2
## Lebanon Lesotho
## 2 1
## Liberia Libya
## 1 2
## Lithuania Luxembourg
## 2 2
## Macedonia, FYR Madagascar
## 2 1
## Malawi Malaysia
## 1 2
## Maldives Mali
## 2 1
## Malta Mauritania
## 2 1
## Mauritius Micronesia, Fed. Sts.
## 2 2
## Moldova Mongolia
## 2 1
## Montenegro Morocco
## 2 2
## Mozambique Myanmar
## 1 1
## Namibia Nepal
## 1 1
## Netherlands New Zealand
## 2 2
## Niger Nigeria
## 1 1
## Norway Oman
## 2 2
## Pakistan Panama
## 1 2
## Paraguay Peru
## 2 2
## Philippines Poland
## 1 2
## Portugal Qatar
## 2 2
## Romania Russia
## 2 2
## Rwanda Samoa
## 1 1
## Saudi Arabia Senegal
## 2 1
## Serbia Seychelles
## 2 2
## Sierra Leone Singapore
## 1 2
## Slovak Republic Slovenia
## 2 2
## Solomon Islands South Africa
## 1 1
## South Korea Spain
## 2 2
## Sri Lanka St. Vincent and the Grenadines
## 1 2
## Sudan Suriname
## 1 2
## Sweden Switzerland
## 2 2
## Tajikistan Tanzania
## 1 1
## Thailand Timor-Leste
## 2 1
## Togo Tonga
## 1 1
## Tunisia Turkey
## 2 2
## Turkmenistan Uganda
## 1 1
## Ukraine United Arab Emirates
## 2 2
## United Kingdom United States
## 2 2
## Uruguay Uzbekistan
## 2 1
## Vanuatu Venezuela
## 1 1
## Vietnam Yemen
## 2 1
## Zambia
## 1
## Objective function:
## build swap
## 2.320291 2.251688
##
## Available components:
## [1] "medoids" "id.med" "clustering" "objective" "isolation"
## [6] "clusinfo" "silinfo" "diss" "call" "data"Fuzzy C-Means Clustering
Metode Fuzzy C-Means digunakan untuk memberikan derajat keanggotaan pada setiap data terhadap masing-masing cluster. Dengan pendekatan ini, suatu negara dapat memiliki keanggotaan pada lebih dari satu cluster.
set.seed(123)
fcm_data <- cmeans(data_country_scaled, centers = 2, m = 2)
print(fcm_data)## Fuzzy c-means clustering with 2 clusters
##
## Cluster centers:
## child_mort exports health imports income inflation life_expec
## 1 -0.5683300 0.1976719 0.2653711 0.07641437 0.3813327 -0.2499684 0.5876800
## 2 0.7232094 -0.2872941 -0.2896684 -0.13470215 -0.4894780 0.2141617 -0.7270828
## total_fer gdpp
## 1 -0.5962277 0.3687191
## 2 0.7702226 -0.4485756
##
## Memberships:
## 1 2
## Afghanistan 0.15850014 0.84149986
## Albania 0.76889877 0.23110123
## Algeria 0.50482100 0.49517900
## Angola 0.23224422 0.76775578
## Antigua and Barbuda 0.89523318 0.10476682
## Argentina 0.60403123 0.39596877
## Armenia 0.58518643 0.41481357
## Australia 0.74981910 0.25018090
## Austria 0.80308252 0.19691748
## Azerbaijan 0.53179203 0.46820797
## Bahamas 0.91836173 0.08163827
## Bahrain 0.82509870 0.17490130
## Bangladesh 0.34662206 0.65337794
## Barbados 0.93019923 0.06980077
## Belarus 0.70465020 0.29534980
## Belgium 0.77773814 0.22226186
## Belize 0.61677373 0.38322627
## Benin 0.16204187 0.83795813
## Bhutan 0.50425421 0.49574579
## Bolivia 0.27411054 0.72588946
## Bosnia and Herzegovina 0.76110979 0.23889021
## Botswana 0.26928669 0.73071331
## Brazil 0.64120575 0.35879425
## Brunei 0.64909077 0.35090923
## Bulgaria 0.86921112 0.13078888
## Burkina Faso 0.16721943 0.83278057
## Burundi 0.26138187 0.73861813
## Cambodia 0.33690166 0.66309834
## Cameroon 0.15802500 0.84197500
## Canada 0.77235144 0.22764856
## Cape Verde 0.51530601 0.48469399
## Central African Republic 0.24565029 0.75434971
## Chad 0.22150274 0.77849726
## Chile 0.87976343 0.12023657
## China 0.63467330 0.36532670
## Colombia 0.65690648 0.34309352
## Comoros 0.12244638 0.87755362
## Congo, Dem. Rep. 0.20962888 0.79037112
## Congo, Rep. 0.28430593 0.71569407
## Costa Rica 0.77914235 0.22085765
## Cote d'Ivoire 0.15437322 0.84562678
## Croatia 0.91741445 0.08258555
## Cyprus 0.89516645 0.10483355
## Czech Republic 0.89619292 0.10380708
## Denmark 0.75892291 0.24107709
## Dominican Republic 0.55900458 0.44099542
## Ecuador 0.65998495 0.34001505
## Egypt 0.33359426 0.66640574
## El Salvador 0.67610179 0.32389821
## Equatorial Guinea 0.34318597 0.65681403
## Eritrea 0.19060434 0.80939566
## Estonia 0.83034040 0.16965960
## Fiji 0.46472596 0.53527404
## Finland 0.82441849 0.17558151
## France 0.76673121 0.23326879
## Gabon 0.23155373 0.76844627
## Gambia 0.11873546 0.88126454
## Georgia 0.70259905 0.29740095
## Germany 0.80023989 0.19976011
## Ghana 0.05711118 0.94288882
## Greece 0.82482682 0.17517318
## Grenada 0.66825277 0.33174723
## Guatemala 0.34120256 0.65879744
## Guinea 0.13004416 0.86995584
## Guinea-Bissau 0.19219182 0.80780818
## Guyana 0.42784407 0.57215593
## Haiti 0.34186150 0.65813850
## Hungary 0.79570998 0.20429002
## Iceland 0.84372701 0.15627299
## India 0.22696181 0.77303819
## Indonesia 0.37713655 0.62286345
## Iran 0.61221839 0.38778161
## Iraq 0.24558335 0.75441665
## Ireland 0.71803709 0.28196291
## Israel 0.84661451 0.15338549
## Italy 0.81677854 0.18322146
## Jamaica 0.62255053 0.37744947
## Japan 0.74610222 0.25389778
## Jordan 0.60339320 0.39660680
## Kazakhstan 0.47857838 0.52142162
## Kenya 0.11260012 0.88739988
## Kiribati 0.36117486 0.63882514
## Kuwait 0.66333289 0.33666711
## Kyrgyz Republic 0.43647635 0.56352365
## Lao 0.09985974 0.90014026
## Latvia 0.88065502 0.11934498
## Lebanon 0.86768564 0.13231436
## Lesotho 0.36795287 0.63204713
## Liberia 0.33917811 0.66082189
## Libya 0.71054014 0.28945986
## Lithuania 0.85958830 0.14041170
## Luxembourg 0.58881185 0.41118815
## Macedonia, FYR 0.81723674 0.18276326
## Madagascar 0.08886444 0.91113556
## Malawi 0.13767441 0.86232559
## Malaysia 0.70180854 0.29819146
## Maldives 0.75939907 0.24060093
## Mali 0.20608404 0.79391596
## Malta 0.59947107 0.40052893
## Mauritania 0.18269410 0.81730590
## Mauritius 0.82729923 0.17270077
## Micronesia, Fed. Sts. 0.48883802 0.51116198
## Moldova 0.62950460 0.37049540
## Mongolia 0.42117462 0.57882538
## Montenegro 0.83872159 0.16127841
## Morocco 0.52590818 0.47409182
## Mozambique 0.13831507 0.86168493
## Myanmar 0.34684425 0.65315575
## Namibia 0.19516516 0.80483484
## Nepal 0.26614938 0.73385062
## Netherlands 0.76035048 0.23964952
## New Zealand 0.82516070 0.17483930
## Niger 0.22745639 0.77254361
## Nigeria 0.43430663 0.56569337
## Norway 0.66807395 0.33192605
## Oman 0.66182286 0.33817714
## Pakistan 0.20255140 0.79744860
## Panama 0.75796218 0.24203782
## Paraguay 0.63505560 0.36494440
## Peru 0.61159936 0.38840064
## Philippines 0.32036520 0.67963480
## Poland 0.93386866 0.06613134
## Portugal 0.83925155 0.16074845
## Qatar 0.59922036 0.40077964
## Romania 0.80263561 0.19736439
## Russia 0.63036840 0.36963160
## Rwanda 0.27108988 0.72891012
## Samoa 0.37566494 0.62433506
## Saudi Arabia 0.67227288 0.32772712
## Senegal 0.10127453 0.89872547
## Serbia 0.79065368 0.20934632
## Seychelles 0.61394559 0.38605441
## Sierra Leone 0.31228889 0.68771111
## Singapore 0.57706991 0.42293009
## Slovak Republic 0.81721728 0.18278272
## Slovenia 0.88255790 0.11744210
## Solomon Islands 0.34848944 0.65151056
## South Africa 0.30975901 0.69024099
## South Korea 0.93839639 0.06160361
## Spain 0.82750313 0.17249687
## Sri Lanka 0.48198524 0.51801476
## St. Vincent and the Grenadines 0.59994630 0.40005370
## Sudan 0.16378063 0.83621937
## Suriname 0.70780702 0.29219298
## Sweden 0.79662293 0.20337707
## Switzerland 0.70854716 0.29145284
## Tajikistan 0.19542315 0.80457685
## Tanzania 0.10931885 0.89068115
## Thailand 0.72330004 0.27669996
## Timor-Leste 0.29627257 0.70372743
## Togo 0.15854521 0.84145479
## Tonga 0.35741854 0.64258146
## Tunisia 0.79528549 0.20471451
## Turkey 0.74432773 0.25567227
## Turkmenistan 0.40175065 0.59824935
## Uganda 0.19342658 0.80657342
## Ukraine 0.69927941 0.30072059
## United Arab Emirates 0.71619058 0.28380942
## United Kingdom 0.82261686 0.17738314
## United States 0.63707380 0.36292620
## Uruguay 0.80100038 0.19899962
## Uzbekistan 0.35646881 0.64353119
## Vanuatu 0.26002633 0.73997367
## Venezuela 0.49163266 0.50836734
## Vietnam 0.62375875 0.37624125
## Yemen 0.16780894 0.83219106
## Zambia 0.14700379 0.85299621
##
## Closest hard clustering:
## Afghanistan Albania
## 2 1
## Algeria Angola
## 1 2
## Antigua and Barbuda Argentina
## 1 1
## Armenia Australia
## 1 1
## Austria Azerbaijan
## 1 1
## Bahamas Bahrain
## 1 1
## Bangladesh Barbados
## 2 1
## Belarus Belgium
## 1 1
## Belize Benin
## 1 2
## Bhutan Bolivia
## 1 2
## Bosnia and Herzegovina Botswana
## 1 2
## Brazil Brunei
## 1 1
## Bulgaria Burkina Faso
## 1 2
## Burundi Cambodia
## 2 2
## Cameroon Canada
## 2 1
## Cape Verde Central African Republic
## 1 2
## Chad Chile
## 2 1
## China Colombia
## 1 1
## Comoros Congo, Dem. Rep.
## 2 2
## Congo, Rep. Costa Rica
## 2 1
## Cote d'Ivoire Croatia
## 2 1
## Cyprus Czech Republic
## 1 1
## Denmark Dominican Republic
## 1 1
## Ecuador Egypt
## 1 2
## El Salvador Equatorial Guinea
## 1 2
## Eritrea Estonia
## 2 1
## Fiji Finland
## 2 1
## France Gabon
## 1 2
## Gambia Georgia
## 2 1
## Germany Ghana
## 1 2
## Greece Grenada
## 1 1
## Guatemala Guinea
## 2 2
## Guinea-Bissau Guyana
## 2 2
## Haiti Hungary
## 2 1
## Iceland India
## 1 2
## Indonesia Iran
## 2 1
## Iraq Ireland
## 2 1
## Israel Italy
## 1 1
## Jamaica Japan
## 1 1
## Jordan Kazakhstan
## 1 2
## Kenya Kiribati
## 2 2
## Kuwait Kyrgyz Republic
## 1 2
## Lao Latvia
## 2 1
## Lebanon Lesotho
## 1 2
## Liberia Libya
## 2 1
## Lithuania Luxembourg
## 1 1
## Macedonia, FYR Madagascar
## 1 2
## Malawi Malaysia
## 2 1
## Maldives Mali
## 1 2
## Malta Mauritania
## 1 2
## Mauritius Micronesia, Fed. Sts.
## 1 2
## Moldova Mongolia
## 1 2
## Montenegro Morocco
## 1 1
## Mozambique Myanmar
## 2 2
## Namibia Nepal
## 2 2
## Netherlands New Zealand
## 1 1
## Niger Nigeria
## 2 2
## Norway Oman
## 1 1
## Pakistan Panama
## 2 1
## Paraguay Peru
## 1 1
## Philippines Poland
## 2 1
## Portugal Qatar
## 1 1
## Romania Russia
## 1 1
## Rwanda Samoa
## 2 2
## Saudi Arabia Senegal
## 1 2
## Serbia Seychelles
## 1 1
## Sierra Leone Singapore
## 2 1
## Slovak Republic Slovenia
## 1 1
## Solomon Islands South Africa
## 2 2
## South Korea Spain
## 1 1
## Sri Lanka St. Vincent and the Grenadines
## 2 1
## Sudan Suriname
## 2 1
## Sweden Switzerland
## 1 1
## Tajikistan Tanzania
## 2 2
## Thailand Timor-Leste
## 1 2
## Togo Tonga
## 2 2
## Tunisia Turkey
## 1 1
## Turkmenistan Uganda
## 2 2
## Ukraine United Arab Emirates
## 1 1
## United Kingdom United States
## 1 1
## Uruguay Uzbekistan
## 1 2
## Vanuatu Venezuela
## 2 2
## Vietnam Yemen
## 1 2
## Zambia
## 2
##
## Available components:
## [1] "centers" "size" "cluster" "membership" "iter"
## [6] "withinerror" "call"DBSCAN Clustering
Metode DBSCAN diterapkan untuk mengidentifikasi cluster berdasarkan kepadatan data serta mendeteksi outlier. Berdasarkan hasil analisis, ditemukan satu cluster utama serta beberapa data yang dikategorikan sebagai noise.
kNNdistplot(data_country_scaled, k = 5)
dbscan_data3 <- dbscan(data_country_scaled, eps = 2.2, minPts = 5)
print(dbscan_data3)## DBSCAN clustering for 167 objects.
## Parameters: eps = 2.2, minPts = 5
## Using euclidean distances and borderpoints = TRUE
## The clustering contains 1 cluster(s) and 10 noise points.
##
## 0 1
## 10 157
##
## Available fields: cluster, eps, minPts, metric, borderPointsrownames(data_country_scaled)[dbscan_data3$cluster == 0]## [1] "Haiti" "Lesotho" "Luxembourg" "Malta"
## [5] "Mongolia" "Nigeria" "Qatar" "Singapore"
## [9] "United States" "Venezuela"fviz_cluster(dbscan_data3, data = data_country_scaled, geom = "point", ellipse = TRUE,main = "DBSCAN: One Major Cluster & 10 Outliers") + theme_minimal()
Mean Shift Clustering
Metode Mean Shift digunakan untuk mengidentifikasi cluster tanpa menentukan jumlah cluster di awal. Hasil menunjukkan terbentuknya beberapa cluster dengan ukuran yang bervariasi, mencerminkan kompleksitas data.
ms_data <- meanShift(data_country_scaled, nNeighbor = 10)
ms_labels <- ms_data$assignment
table(ms_data$assignment)##
## 1 2 3 4 5 6 7 8 9 10 11
## 30 72 29 6 4 3 10 5 3 3 2Evaluasi Model Clustering
Evaluasi dilakukan menggunakan Average Silhouette Score untuk mengukur kualitas hasil clustering. Semakin tinggi nilai silhouette, semakin baik pemisahan antar cluster.
Hasil evaluasi menunjukkan bahwa metode K-Means memiliki nilai tertinggi dibandingkan metode lainnya, sehingga dianggap sebagai metode terbaik untuk dataset ini.
avg_km <- mean(silhouette(kmeans_data$cluster, dist(data_country_scaled))[, 3])
avg_kmed <- mean(silhouette(kmedian_data$clustering, dist(data_country_scaled))[, 3])
avg_fcm <- mean(silhouette(fcm_data$cluster, dist(data_country_scaled))[, 3])
avg_ms <- mean(silhouette(ms_data$assignment, dist(data_country_scaled))[, 3])eval_metrik <- data.frame(Model = c("K-Means", "K-Medians", "Fuzzy C-Means", "Mean Shift"),Silhouette = c(avg_km, avg_kmed, avg_fcm, avg_ms))
print(eval_metrik)## Model Silhouette
## 1 K-Means 0.28735669
## 2 K-Medians 0.28463921
## 3 Fuzzy C-Means 0.28382908
## 4 Mean Shift 0.08256764Visualisasi Perbandingan Model
ggplot(eval_metrik, aes(x = reorder(Model, -Silhouette), y = Silhouette)) + geom_bar(stat = "identity", width = 0.6, fill = "slategray") + geom_text(aes(label = round(Silhouette, 3)), vjust = -0.5, size = 4, fontface = "bold") + labs(
title = "Evaluasi Performa Model Clustering",
subtitle = "Berdasarkan Average Silhouette Score",
x = "Algoritma",
y = "Silhouette Score") + theme_minimal() + theme(panel.grid.major.x = element_blank(), plot.title = element_text(face = "bold", size = 14))
Visualisasi K-Means
Visualisasi cluster menggunakan metode K-Means menunjukkan pemisahan yang cukup jelas antara dua kelompok negara. Cluster pertama merepresentasikan negara dengan kondisi sosial-ekonomi yang lebih baik, sedangkan cluster kedua menunjukkan negara dengan kondisi yang lebih rendah.
fviz_cluster(kmeans_data, data = data_country_scaled,
palette = c("#E7B800","#2E9FDF"),
geom = "point",
ellipse.type = "convex",
ggtheme = theme_minimal(),
main = "Visualisasi Klasterisasi Negara (K-Means)")
rownames(data_country_scaled)[kmeans_data$cluster==2]## [1] "Afghanistan" "Angola"
## [3] "Bangladesh" "Benin"
## [5] "Bolivia" "Botswana"
## [7] "Burkina Faso" "Burundi"
## [9] "Cambodia" "Cameroon"
## [11] "Central African Republic" "Chad"
## [13] "Comoros" "Congo, Dem. Rep."
## [15] "Congo, Rep." "Cote d'Ivoire"
## [17] "Egypt" "Equatorial Guinea"
## [19] "Eritrea" "Gabon"
## [21] "Gambia" "Ghana"
## [23] "Guatemala" "Guinea"
## [25] "Guinea-Bissau" "Guyana"
## [27] "Haiti" "India"
## [29] "Indonesia" "Iraq"
## [31] "Kenya" "Kiribati"
## [33] "Kyrgyz Republic" "Lao"
## [35] "Lesotho" "Liberia"
## [37] "Madagascar" "Malawi"
## [39] "Mali" "Mauritania"
## [41] "Micronesia, Fed. Sts." "Mongolia"
## [43] "Mozambique" "Myanmar"
## [45] "Namibia" "Nepal"
## [47] "Niger" "Nigeria"
## [49] "Pakistan" "Philippines"
## [51] "Rwanda" "Samoa"
## [53] "Senegal" "Sierra Leone"
## [55] "Solomon Islands" "South Africa"
## [57] "Sudan" "Tajikistan"
## [59] "Tanzania" "Timor-Leste"
## [61] "Togo" "Tonga"
## [63] "Turkmenistan" "Uganda"
## [65] "Uzbekistan" "Vanuatu"
## [67] "Yemen" "Zambia"Kesimpulan
Dari lima metode clustering yang digunakan, K-Means memberikan performa terbaik berdasarkan nilai Silhouette Score tertinggi dan menghasilkan dua cluster utama. Cluster pertama terdiri dari 99 negara dengan kondisi ekonomi yang lebih baik, sedangkan cluster kedua berisi 68 negara dengan kondisi yang lebih rendah. Negara pada cluster kedua direkomendasikan sebagai prioritas penerima bantuan karena memiliki indikator kesejahteraan yang lebih rendah.