Tugas Kelompok UAS TPG
Nabil Ibni Nawawi
2024-11-24
Pernyiapan Data
jarak_kota <- matrix(ncol=21,nrow=21)
colnames(jarak_kota) <- c("Jakarta","Serang","Pandeglang","Lebak","Tangerang","Bekasi","Karawang","Purwakarta","Subang","Bogor","Sukabumi",
"Cianjur","Bandung","Sumedang","Garut","Tasikmalaya","Ciamis","Kuningan","Majalengka","Cirebon","Indramayu")
rownames(jarak_kota) <- c("Jakarta","Serang","Pandeglang","Lebak","Tangerang","Bekasi","Karawang","Purwakarta","Subang","Bogor","Sukabumi",
"Cianjur","Bandung","Sumedang","Garut","Tasikmalaya","Ciamis","Kuningan","Majalengka","Cirebon","Indramayu")
jarak_kota[lower.tri(jarak_kota)] <- c(90,111,131,25,29,71,113,161,58,119,122,187,232,250,293,308,293,278,258,205,21,41,65,119,161,203,251,148,209,212,277,322,340,383,398,383,368,348,295,20,86,140,182,224,272,118,179,182,298,343,361,404,419,404,389,369,316,106,160,202,244,292,98,159,172,227,272,290,333,348,392,318,357,336,54,96,138,186,83,144,147,212,257,275,318,333,318,303,283,230,42,84,132,87,148,151,154,199,217,260,279,261,239,229,176,42,90,96,172,147,112,157,175,218,233,219,203,184,134,48,163,136,99,70,115,133,176,191,235,161,200,130,186,42,39,58,61,121,164,179,191,149,156,102,61,74,129,174,192,235,250,194,220,259,313,32,96,141,159,202,217,261,187,226,260,65,110,128,171,186,230,156,195,249,45,63,106,121,165,91,180,184,72,115,130,120,46,85,139,57,74,192,118,157,211,17,185,101,120,174,68,84,103,157,51,35,89,61,82,54)
diag(jarak_kota) <- 0
jarak_kota <- as.dist(jarak_kota, diag = TRUE) # membuat data matriks menjadi kelas dist
class(jarak_kota)## [1] "dist"jarak_kota## Jakarta Serang Pandeglang Lebak Tangerang Bekasi Karawang
## Jakarta 0
## Serang 90 0
## Pandeglang 111 21 0
## Lebak 131 41 20 0
## Tangerang 25 65 86 106 0
## Bekasi 29 119 140 160 54 0
## Karawang 71 161 182 202 96 42 0
## Purwakarta 113 203 224 244 138 84 42
## Subang 161 251 272 292 186 132 90
## Bogor 58 148 118 98 83 87 96
## Sukabumi 119 209 179 159 144 148 172
## Cianjur 122 212 182 172 147 151 147
## Bandung 187 277 298 227 212 154 112
## Sumedang 232 322 343 272 257 199 157
## Garut 250 340 361 290 275 217 175
## Tasikmalaya 293 383 404 333 318 260 218
## Ciamis 308 398 419 348 333 279 233
## Kuningan 293 383 404 392 318 261 219
## Majalengka 278 368 389 318 303 239 203
## Cirebon 258 348 369 357 283 229 184
## Indramayu 205 295 316 336 230 176 134
## Purwakarta Subang Bogor Sukabumi Cianjur Bandung Sumedang Garut
## Jakarta
## Serang
## Pandeglang
## Lebak
## Tangerang
## Bekasi
## Karawang
## Purwakarta 0
## Subang 48 0
## Bogor 163 186 0
## Sukabumi 136 42 61 0
## Cianjur 99 39 74 32 0
## Bandung 70 58 129 96 65 0
## Sumedang 115 61 174 141 110 45 0
## Garut 133 121 192 159 128 63 72 0
## Tasikmalaya 176 164 235 202 171 106 115 57
## Ciamis 191 179 250 217 186 121 130 74
## Kuningan 235 191 194 261 230 165 120 192
## Majalengka 161 149 220 187 156 91 46 118
## Cirebon 200 156 259 226 195 180 85 157
## Indramayu 130 102 313 260 249 184 139 211
## Tasikmalaya Ciamis Kuningan Majalengka Cirebon Indramayu
## Jakarta
## Serang
## Pandeglang
## Lebak
## Tangerang
## Bekasi
## Karawang
## Purwakarta
## Subang
## Bogor
## Sukabumi
## Cianjur
## Bandung
## Sumedang
## Garut
## Tasikmalaya 0
## Ciamis 17 0
## Kuningan 185 68 0
## Majalengka 101 84 51 0
## Cirebon 120 103 35 61 0
## Indramayu 174 157 89 82 54 0Menghitung koordinat MMDS
dengan fungsi cmdscale()
mmds_coord <- cmdscale(jarak_kota, k = 2, eig = T)
coords <- mmds_coord$points
coords## [,1] [,2]
## Jakarta -118.87509 35.283137
## Serang -213.71877 42.295507
## Pandeglang -239.39338 16.656026
## Lebak -182.91709 -81.086426
## Tangerang -145.22055 37.231018
## Bekasi -83.83125 47.809112
## Karawang -38.61738 55.243033
## Purwakarta -4.71526 26.194977
## Subang 37.26853 23.389224
## Bogor -80.66635 -74.111595
## Sukabumi -40.36758 -79.253502
## Cianjur -20.41052 -71.519515
## Bandung 51.32532 -51.869971
## Sumedang 98.54920 -24.289506
## Garut 105.05671 -72.718716
## Tasikmalaya 153.96119 -54.154497
## Ciamis 179.02328 -36.410670
## Kuningan 165.42197 51.036463
## Majalengka 148.81046 -7.326521
## Cirebon 139.10840 63.826139
## Indramayu 90.20817 153.776284Plot MMDS
plot(coords[,1], coords[,2], type = "n", xlab = "", ylab = "", axes = FALSE,
main = "Jarak antar Kota/Kabupaten di Jawa Barat - MMDS")
text(coords[,1], coords[,2], labels(jarak_kota), cex = 0.9, xpd = TRUE)
Grafik hasil menunjukkan bahwa Jakarta dan Tangerang adalah kota yang berdekatan. Kedua, kita dapat melihat bahwa Purwakarta dan Subang berdekatan satu sama lain. Di sisi lain, Ciamis dan Pandeglang adalah kota yang paling jauh satu sama lain. Juga, Lebak dan Indramayu terisolasi sendiri, tidak terlalu dekat dengan Kota/Kab lain.
Evaluasi Kebaikan MMDS
#install.packages("seqhandbook")
library(seqhandbook)## Warning: package 'seqhandbook' was built under R version 4.3.3## Loading required package: TraMineR## Warning: package 'TraMineR' was built under R version 4.3.3##
## TraMineR stable version 2.2-10 (Built: 2024-11-22)## Website: http://traminer.unige.ch## Please type 'citation("TraMineR")' for citation information.stress <- seqmds.stress(jarak_kota, mmds_coord)
stress## [1] 0.2367879 0.1294367plot(stress, type='l', xlab='number of dimensions', ylab='stress')