UAS - Analisis Multivariat
Diyas Arya Nugroho
12/5/2023
Package yang digunakan
library(readxl)## Warning: package 'readxl' was built under R version 4.1.3library(ggplot2)
library(graphics)
library(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphalibrary(magrittr)## Warning: package 'magrittr' was built under R version 4.1.3library(dplyr)## Warning: package 'dplyr' was built under R version 4.1.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggpubr)## Warning: package 'ggpubr' was built under R version 4.1.3library(factoextra)## Warning: package 'factoextra' was built under R version 4.1.3## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(nFactors)## Warning: package 'nFactors' was built under R version 4.1.3## Loading required package: lattice##
## Attaching package: 'nFactors'## The following object is masked from 'package:lattice':
##
## parallellibrary(MVN)## Warning: package 'MVN' was built under R version 4.1.3library(mvnormtest)Analisis Faktor
Persiapan Data
data <- read_excel("Money Supply Data.xlsx") #Import Data
data## # A tibble: 48 x 7
## y x1 x2 x3 x5 x6 x7
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 -1273033. -0.199 -0.284 -0.0836 -0.0997 -0.171 -0.237
## 2 -1261402. -0.207 -0.280 -0.112 -0.0729 -0.176 -0.232
## 3 -1219294. -0.200 -0.268 -0.103 -0.0633 -0.171 -0.224
## 4 -1180894. -0.197 -0.246 -0.146 -0.0485 -0.150 -0.216
## 5 -1115518. -0.200 -0.252 -0.177 -0.0384 -0.151 -0.202
## 6 -1128444. -0.243 -0.245 -0.164 -0.0243 -0.149 -0.188
## 7 -1035249. -0.226 -0.207 -0.0858 -0.0136 -0.150 -0.178
## 8 -1039403. -0.205 -0.179 -0.148 -0.00430 -0.145 -0.169
## 9 -957742. -0.155 -0.149 -0.144 0.00962 -0.126 -0.154
## 10 -964953. -0.169 -0.172 -0.153 0.0221 -0.134 -0.152
## # i 38 more rows# Handling Missing data
databaru=na.omit(data)
summary(databaru)## y x1 x2 x3
## Min. :-1273033 Min. :-0.24325 Min. :-0.28428 Min. :-0.2316
## 1st Qu.: -893796 1st Qu.:-0.13949 1st Qu.:-0.12010 1st Qu.:-0.1448
## Median : -205979 Median :-0.03196 Median : 0.01906 Median :-0.0326
## Mean : -244129 Mean :-0.03774 Mean :-0.03414 Mean :-0.0200
## 3rd Qu.: 396569 3rd Qu.: 0.05478 3rd Qu.: 0.07341 3rd Qu.: 0.1087
## Max. : 877343 Max. : 0.15814 Max. : 0.10450 Max. : 0.2292
## x5 x6 x7
## Min. :-0.21328 Min. :-0.17583 Min. :-0.23672
## 1st Qu.:-0.18614 1st Qu.:-0.11127 1st Qu.:-0.13412
## Median : 0.01589 Median :-0.08092 Median :-0.02388
## Mean :-0.00484 Mean :-0.04935 Mean :-0.04124
## 3rd Qu.: 0.12220 3rd Qu.: 0.04301 3rd Qu.: 0.06101
## Max. : 0.19413 Max. : 0.10198 Max. : 0.13011Asumsi Normalitas
H0 : Data berdistribusi normal
H1 : Data tidak berdistribusi
normal
#uji normalitas
shapiro.test(data$x1)##
## Shapiro-Wilk normality test
##
## data: data$x1
## W = 0.94948, p-value = 0.03812shapiro.test(data$x2)##
## Shapiro-Wilk normality test
##
## data: data$x2
## W = 0.87964, p-value = 0.0001474shapiro.test(data$x3)##
## Shapiro-Wilk normality test
##
## data: data$x3
## W = 0.93133, p-value = 0.007635shapiro.test(data$x5)##
## Shapiro-Wilk normality test
##
## data: data$x5
## W = 0.89226, p-value = 0.0003571shapiro.test(data$x6)##
## Shapiro-Wilk normality test
##
## data: data$x6
## W = 0.90355, p-value = 0.0008203shapiro.test(data$x7)##
## Shapiro-Wilk normality test
##
## data: data$x7
## W = 0.92558, p-value = 0.004701Hasil pengujian asumsi normalitas menunjukkan bahwa data yang digunakan tidak berdistribusi normal hal ini dikarenakan p-value < 0,05 dimana H0 ditolak.
KMO dan Bartlett’s Test of Sphericity
#fungsi
kmo <- function(x)
{
x <- subset(x, complete.cases(x)) # menghilangkan data kosong (NA)
r <- cor(x) # Membuat matrix korelasi
r2 <- r^2 # nilai koefisien untuk r squared
i <- solve(r) # Inverse matrix dari matrix korelasi
d <- diag(i) # element diagonal dari inverse matrix
p2 <- (-i/sqrt(outer(d, d)))^2 # koefisien korelasi Parsial kuadrat
diag(r2) <- diag(p2) <- 0 # menghapus element diagonal
KMO <- sum(r2)/(sum(r2)+sum(p2))
MSA <- colSums(r2)/(colSums(r2)+colSums(p2))
return(list(KMO=KMO, MSA=MSA))
}
uji_bart <- function(x)
{
method <- "Bartlett's test of sphericity"
data.name <- deparse(substitute(x))
x <- subset(x, complete.cases(x))
n <- nrow(x)
p <- ncol(x)
chisq <- (1-n+(2*p+5)/6)*log(det(cor(x)))
df <- p*(p-1)/2
p.value <- pchisq(chisq, df, lower.tail=FALSE)
names(chisq) <- "Khi-squared"
names(df) <- "df"
return(structure(list(statistic=chisq, parameter=df, p.value=p.value,
method=method, data.name=data.name), class="htest"))
}
cor(databaru[,2:7])## x1 x2 x3 x5 x6 x7
## x1 1.0000000 0.88382829 0.7271341 -0.36671024 0.9585402 0.9825829
## x2 0.8838283 1.00000000 0.7792636 -0.03105515 0.7921972 0.9239690
## x3 0.7271341 0.77926357 1.0000000 -0.12416951 0.6309743 0.7717570
## x5 -0.3667102 -0.03105515 -0.1241695 1.00000000 -0.5168132 -0.3206505
## x6 0.9585402 0.79219720 0.6309743 -0.51681325 1.0000000 0.9497018
## x7 0.9825829 0.92396901 0.7717570 -0.32065053 0.9497018 1.0000000kmo(databaru[,2:7])## $KMO
## [1] 0.7823268
##
## $MSA
## x1 x2 x3 x5 x6 x7
## 0.9129754 0.7951975 0.8093099 0.5372378 0.7592669 0.7266169uji_bart(databaru[,2:7])##
## Bartlett's test of sphericity
##
## data: databaru[, 2:7]
## Khi-squared = 480.5, df = 15, p-value < 2.2e-16Nilai KMO = 0,78 lebih dari 0,5 yang artinya analisis faktor dapat dilanjutkan. Selain itu, pada Bartlett’s test of sphericity didapat nilai p-value = 2,2e^-15 lebih kecil dari 0,05 yang berarti tolak H0 atau terdapat korelasi antar variabel.
Penentuan Jumlah Faktor
R=cov(databaru[,2:7])
eigen_data=eigen(R)
ap=parallel(subject = 37, var = 6, rep = 100, cent = 0.05)
nfaktor = nScree(eigen$values, ap$eigen$qevpea)
plotnScree(nfaktor)
Berdasarkan scree plot, hasil acceleration factor adalah 2 yang artinya jumlah faktor yang digunakan sebanyak 2 faktor.
Analisis Faktor
#metode none (tanpa rotasi)
solusi <- fa(r = R, nfactors = 2, rotate = "none", fm = "pa")## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefullysolusi## Factor Analysis using method = pa
## Call: fa(r = R, nfactors = 2, rotate = "none", fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
## PA1 PA2 h2 u2 com
## x1 0.98 -0.04 0.97 0.0298 1.0
## x2 0.92 0.38 0.98 0.0166 1.3
## x3 0.75 0.21 0.61 0.3890 1.2
## x5 -0.35 0.74 0.66 0.3388 1.4
## x6 0.95 -0.26 0.98 0.0248 1.1
## x7 1.00 0.03 1.00 -0.0048 1.0
##
## PA1 PA2
## SS loadings 4.41 0.80
## Proportion Var 0.73 0.13
## Cumulative Var 0.73 0.87
## Proportion Explained 0.85 0.15
## Cumulative Proportion 0.85 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 2 factors are sufficient.
##
## df null model = 15 with the objective function = 10.88
## df of the model are 4 and the objective function was 1.75
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.02
##
## Fit based upon off diagonal values = 1Dapat dilihat pada nilai communalitaies yang dihasilkan lebih dari
0,5 berarti hal tersebut mengindikasikan bahwa variabel memiliki
keeratan yang kuat dengan faktor yang terbentuk.
Nilai loading (PA1 dan PA2) menjelaskan besarnya korelasi antar
variabel dengan faktor umum. Serta dapat ditentukan bahwa Faktor 1
berisi variabel x1, x2, x3, x6 dan x7 dengan melihat nilai PA1 > 0,5
dan Faktor 2 berisi variabel x5 karena nilai PA2 > 0,5.
Selanjutnya nilai SS Loading PA1 sebesar 4.41 dan PA2 sebesar
0.80 yang berarti total varians keseluruhan variabel dapat dijelaskan
oleh faktor 1 sebesar 4.41 sedangkan faktor 2 sebesar 0.80. Sedangkan,
Propotion var PA1 sebesar 0.73 dan PA2 sebesar 0.13 yang artinya bahwa
proposi total varians variabel asal dapat dijelaskan oleh faktor 1
sebesar 73% dan faktor 2 sebesar 13%.
Analisis MDS dan Clustering
Persiapan Data
data=read.csv("DataMDS_1.csv", header=T, sep=";")
PintuMasuk=data[, 1]
dataMDS3=data[,2:13]
rownames(dataMDS3)= PintuMasuk
dataMDS3 #Data lengkap## Januari Februari Maret April Mei Juni Juli
## Ngurah Rai 18 249 1464 3560 6440 6590 9252
## Soekarno Hatta 34168 38339 85133 100895 176863 134091 158817
## Juanda 22 25 959 8037 18965 14201 17310
## Kualanamu 155 115 126 564 8473 13393 19057
## Husein Sastranegara 0 0 0 0 4 3 0
## Adi Sucipto/YIA 0 0 0 7 181 210 84
## Bandara Int'l Lombok 0 0 6 0 383 555 363
## Sam Ratulangi 77 45 119 224 436 669 626
## Minangkabau 0 0 0 0 0 0 0
## Sultan Syarif Kasim II 2 5 3 13 3 0 28
## Sultan Iskandar Muda 38 41 31 0 0 65 2008
## Ahmad Yani 47 51 48 61 53 51 35
## Supadio 0 3 0 4 0 0 0
## Hasanuddin 0 0 0 33 2217 1588 1580
## Sultan Badaruddin II 0 0 0 6 3 44 0
## LainnyaA 320 112 80 98 140 901 11070
## Batam 2435 2176 3414 9403 27810 42293 56258
## Tanjung Uban 222 166 278 585 700 758 1089
## Tanjung Pinang 262 170 329 896 2250 3304 2292
## Tanjung Balai Karimun 43 22 31 72 589 5037 8002
## Tanjung Benoa 16 12 17 19 24 23 19
## LainnyaB 441 384 590 907 3235 6814 9876
## Jayapura 0 0 0 0 5 3 7
## Atambua 248 259 478 1054 2085 2450 2500
## Entikong 34 30 46 563 2842 6345 2993
## Aruk 21 8 20 255 1961 3914 5151
## Nanga Badau 0 0 0 0 3 0 46
## LainnyaC 0 0 0 0 0 0 0
## Perbatasan Laut 1210 1066 1367 1126 858 787 912
## Perbatasan Darat 1045 1222 1376 1246 1295 1692 2123
## Agustus September Oktober November Desember
## Ngurah Rai 8769 9772 12266 15202 18477
## Soekarno Hatta 158352 222339 265517 260496 277290
## Juanda 29631 49357 57093 56338 53166
## Kualanamu 34078 18140 45053 50149 51052
## Husein Sastranegara 0 0 0 1 0
## Adi Sucipto/YIA 231 160 716 1958 2436
## Bandara Int'l Lombok 2558 412 1211 1152 431
## Sam Ratulangi 465 570 792 816 903
## Minangkabau 0 0 0 0 1543
## Sultan Syarif Kasim II 65 1136 3578 4671 5785
## Sultan Iskandar Muda 59 64 710 1097 1955
## Ahmad Yani 33 36 29 32 42
## Supadio 0 0 0 21 0
## Hasanuddin 9243 15961 15820 14454 10443
## Sultan Badaruddin II 2420 7 2 0 0
## LainnyaA 13204 676 1117 1262 1473
## Batam 49770 54577 56990 64464 79907
## Tanjung Uban 642 755 786 546 738
## Tanjung Pinang 3392 3435 3782 3461 4527
## Tanjung Balai Karimun 12270 13748 16024 16746 18635
## Tanjung Benoa 11 56 33 51 67
## LainnyaB 12966 14568 15688 15919 18202
## Jayapura 3 9 21 58 154
## Atambua 4189 3793 3992 4459 3968
## Entikong 0 623 9566 9831 16094
## Aruk 5675 5285 6210 5713 7518
## Nanga Badau 1001 1267 1552 1854 2188
## LainnyaC 0 0 0 0 0
## Perbatasan Laut 926 1068 1427 2113 4228
## Perbatasan Darat 1927 1045 1291 1559 6928attach(dataMDS3)Asumsi Normalitas
H0 : Data berdistribusi normal
H1 : Data tidak berdistribusi
normal
mvn(data = dataMDS3, mvnTest = "mardia")## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 1009.37348577646 4.96363982643645e-62 NO
## 2 Mardia Kurtosis 12.5601089622425 0 NO
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Anderson-Darling Januari 9.8691 <0.001 NO
## 2 Anderson-Darling Februari 10.0639 <0.001 NO
## 3 Anderson-Darling Maret 10.2852 <0.001 NO
## 4 Anderson-Darling April 9.3929 <0.001 NO
## 5 Anderson-Darling Mei 8.6173 <0.001 NO
## 6 Anderson-Darling Juni 7.4555 <0.001 NO
## 7 Anderson-Darling Juli 7.1034 <0.001 NO
## 8 Anderson-Darling Agustus 6.0986 <0.001 NO
## 9 Anderson-Darling September 7.0837 <0.001 NO
## 10 Anderson-Darling Oktober 6.8005 <0.001 NO
## 11 Anderson-Darling November 6.6114 <0.001 NO
## 12 Anderson-Darling Desember 6.4688 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th Skew
## Januari 30 1360.800 6217.047 28.0 0 34168 0.00 241.50 4.890494
## Februari 30 1483.333 6976.815 27.5 0 38339 0.00 169.00 4.906471
## Maret 30 3197.167 15492.220 38.5 0 85133 0.00 440.75 4.924529
## April 30 4320.933 18375.433 85.0 0 100895 4.50 904.25 4.830518
## Mei 30 8593.933 32351.271 512.5 0 176863 4.25 2241.75 4.686211
## Juni 30 8192.700 25142.663 772.5 0 134091 28.25 4756.25 4.248890
## Juli 30 10383.267 30084.098 1334.5 0 158817 29.75 7289.25 4.099574
## Agustus 30 11729.333 30057.223 1464.0 0 158352 39.50 9124.50 3.924918
## September 30 13961.967 41582.086 900.0 0 222339 58.00 8650.25 4.223103
## Oktober 30 17375.533 49503.417 1359.0 0 265517 202.25 11591.00 4.213530
## November 30 17814.100 48904.724 1906.0 0 260496 180.00 13298.25 4.100974
## Desember 30 19605.000 52119.868 3202.0 0 277290 507.75 14681.25 4.068359
## Kurtosis
## Januari 22.84919
## Februari 22.95927
## Maret 23.08207
## April 22.43377
## Mei 21.40428
## Juni 18.07540
## Juli 16.94253
## Agustus 15.98707
## September 18.07633
## Oktober 18.08334
## November 17.26882
## Desember 16.99655Berdasarkan hasil dengan menggunakan MVN test, dapat disimpulkan bahwa data tidak berdistribusi normal karena p-value < 0,05 atau H0 ditolak. Maka, perlu ada transformasi data sehingga data berdistribusi normal.
KMO Test
KMO.MSA<- function(x){
x <- subset(x, complete.cases(x))
korelasi <- cor(x)
r2 <- korelasi^2
i <- solve(korelasi)
d <- diag(i)
p2 <- (-i/sqrt(outer(d, d)))^2
diag(r2) <- diag(p2) <- 0
Kaiser_Meyer_Olkin <- sum(r2)/(sum(r2)+sum(p2))
Measure_of_Sampling_Adequacy <- colSums(r2)/(colSums(r2)+colSums(p2))
return(list(Kaiser_Meyer_Olkin=Kaiser_Meyer_Olkin, Measure_of_Sampling_Adequacy=Measure_of_Sampling_Adequacy))
}
KMO.MSA(dataMDS3)## $Kaiser_Meyer_Olkin
## [1] 0.744893
##
## $Measure_of_Sampling_Adequacy
## Januari Februari Maret April Mei Juni Juli Agustus
## 0.7633572 0.7094856 0.6794621 0.7148431 0.7178390 0.7607272 0.8018723 0.8096182
## September Oktober November Desember
## 0.7146623 0.8145471 0.8270387 0.6690085Pada nilai KMO = 0.74 > 0,5 maka analisis dapat dilanjutkan atau measure sample sudah dapat dianalisis.
Asumsi Multikolineartitas
VIF=function(x){
VIF=diag(solve(cor(x)))
result=ifelse(VIF>10,"mulicolinearity", "non multicolinearity")
data1=data.frame(VIF,result)
return(data1)
}
VIF(dataMDS3)## VIF result
## Januari 18923.0175 mulicolinearity
## Februari 48009.5272 mulicolinearity
## Maret 16356.3835 mulicolinearity
## April 11845.6393 mulicolinearity
## Mei 13079.5234 mulicolinearity
## Juni 2782.7921 mulicolinearity
## Juli 2004.3251 mulicolinearity
## Agustus 1223.1885 mulicolinearity
## September 907.6747 mulicolinearity
## Oktober 8524.0409 mulicolinearity
## November 11704.2015 mulicolinearity
## Desember 10522.1572 mulicolinearityBerdasarkan hasil uji multikolinearitas diperoleh bahwa, terdapat multikolinearitas antar variabel.
Transformasi Data
data=read.csv("DataMDS.csv", header=T, sep=";")
PM=data[, 1]
dataMDS=data[,2:4]
rownames(dataMDS)= PM
dataMDS #Data di transformasi berdasarkan rata-rata quarter## Q1 Q2 Q3
## Ngurah Rai 1323 7763 13929
## Soekarno Hatta 64634 157031 256411
## Juanda 2261 20027 53989
## Kualanamu 240 18750 41099
## Husein Sastranegara 0 2 0
## Adi Sucipto/YIA 2 177 1318
## Bandara Int'l Lombok 2 965 802
## Sam Ratulangi 116 549 770
## Minangkabau 0 0 514
## Sultan Syarif Kasim II 6 24 3793
## Sultan Iskandar Muda 28 533 957
## Ahmad Yani 52 43 35
## Supadio 2 0 5
## Hasanuddin 8 3657 14170
## Sultan Badaruddin II 2 617 2
## LainnyaA 153 6329 1132
## Batam 4357 44033 63985
## Tanjung Uban 313 797 706
## Tanjung Pinang 414 2810 3801
## Tanjung Balai Karimun 42 6475 16288
## Tanjung Benoa 16 19 52
## LainnyaB 581 8223 16094
## Jayapura 0 5 61
## Atambua 510 2806 4053
## Entikong 168 3045 9029
## Aruk 76 4175 6182
## Nanga Badau 0 263 1715
## Perbatasan Laut 1192 871 2209
## Perbatasan Darat 1222 1759 2706attach(dataMDS)logdataMDS <- log10(dataMDS)
srdataMDS <- sqrt(dataMDS)
crdataMDS <- dataMDS^(1/3)
logdataMDS #Data di transformasi dengan Log## Q1 Q2 Q3
## Ngurah Rai 3.1215598 3.890030 4.143920
## Soekarno Hatta 4.8104610 5.195985 5.408937
## Juanda 3.3543006 4.301616 4.732305
## Kualanamu 2.3802112 4.273001 4.613831
## Husein Sastranegara -Inf 0.301030 -Inf
## Adi Sucipto/YIA 0.3010300 2.247973 3.119915
## Bandara Int'l Lombok 0.3010300 2.984527 2.904174
## Sam Ratulangi 2.0644580 2.739572 2.886491
## Minangkabau -Inf -Inf 2.710963
## Sultan Syarif Kasim II 0.7781513 1.380211 3.578983
## Sultan Iskandar Muda 1.4471580 2.726727 2.980912
## Ahmad Yani 1.7160033 1.633468 1.544068
## Supadio 0.3010300 -Inf 0.698970
## Hasanuddin 0.9030900 3.563125 4.151370
## Sultan Badaruddin II 0.3010300 2.790285 0.301030
## LainnyaA 2.1846914 3.801335 3.053846
## Batam 3.6391876 4.643778 4.806078
## Tanjung Uban 2.4955443 2.901458 2.848805
## Tanjung Pinang 2.6170003 3.448706 3.579898
## Tanjung Balai Karimun 1.6232493 3.811240 4.211868
## Tanjung Benoa 1.2041200 1.278754 1.716003
## LainnyaB 2.7641761 3.915030 4.206664
## Jayapura -Inf 0.698970 1.785330
## Atambua 2.7075702 3.448088 3.607777
## Entikong 2.2253093 3.483587 3.955640
## Aruk 1.8808136 3.620656 3.791129
## Nanga Badau -Inf 2.419956 3.234264
## Perbatasan Laut 3.0762763 2.940018 3.344196
## Perbatasan Darat 3.0870712 3.245266 3.432328srdataMDS #Data di transformasi dengan Square Root## Q1 Q2 Q3
## Ngurah Rai 36.373067 88.107888 118.021185
## Soekarno Hatta 254.232177 396.271372 506.370418
## Juanda 47.549974 141.516783 232.355331
## Kualanamu 15.491933 136.930639 202.728883
## Husein Sastranegara 0.000000 1.414214 0.000000
## Adi Sucipto/YIA 1.414214 13.304135 36.304270
## Bandara Int'l Lombok 1.414214 31.064449 28.319605
## Sam Ratulangi 10.770330 23.430749 27.748874
## Minangkabau 0.000000 0.000000 22.671568
## Sultan Syarif Kasim II 2.449490 4.898979 61.587336
## Sultan Iskandar Muda 5.291503 23.086793 30.935417
## Ahmad Yani 7.211103 6.557439 5.916080
## Supadio 1.414214 0.000000 2.236068
## Hasanuddin 2.828427 60.473135 119.037809
## Sultan Badaruddin II 1.414214 24.839485 1.414214
## LainnyaA 12.369317 79.555012 33.645208
## Batam 66.007575 209.840416 252.952565
## Tanjung Uban 17.691806 28.231188 26.570661
## Tanjung Pinang 20.346990 53.009433 61.652251
## Tanjung Balai Karimun 6.480741 80.467385 127.624449
## Tanjung Benoa 4.000000 4.358899 7.211103
## LainnyaB 24.103942 90.680759 126.862130
## Jayapura 0.000000 2.236068 7.810250
## Atambua 22.583180 52.971691 63.663176
## Entikong 12.961481 55.181519 95.021050
## Aruk 8.717798 64.614240 78.625696
## Nanga Badau 0.000000 16.217275 41.412558
## Perbatasan Laut 34.525353 29.512709 47.000000
## Perbatasan Darat 34.957117 41.940434 52.019227crdataMDS #Data di transformasi dengan Cube Root## Q1 Q2 Q3
## Ngurah Rai 10.977917 19.800517 24.060611
## Soekarno Hatta 40.131650 53.950458 63.530004
## Juanda 13.125026 27.156386 37.795065
## Kualanamu 6.214465 26.566464 34.509904
## Husein Sastranegara 0.000000 1.259921 0.000000
## Adi Sucipto/YIA 1.259921 5.614672 10.964070
## Bandara Int'l Lombok 1.259921 9.881945 9.290907
## Sam Ratulangi 4.876999 8.188244 9.165656
## Minangkabau 0.000000 0.000000 8.010403
## Sultan Syarif Kasim II 1.817121 2.884499 15.595320
## Sultan Iskandar Muda 3.036589 8.107913 9.854562
## Ahmad Yani 3.732511 3.503398 3.271066
## Supadio 1.259921 0.000000 1.709976
## Hasanuddin 2.000000 15.406654 24.198584
## Sultan Badaruddin II 1.259921 8.513243 1.259921
## LainnyaA 5.348481 18.497443 10.421946
## Batam 16.332871 35.312307 39.996875
## Tanjung Uban 6.789661 9.271559 8.904337
## Tanjung Pinang 7.453040 14.111357 15.606276
## Tanjung Balai Karimun 3.476027 18.638599 25.348713
## Tanjung Benoa 2.519842 2.668402 3.732511
## LainnyaB 8.344341 20.184133 25.247672
## Jayapura 0.000000 1.709976 3.936497
## Atambua 7.989570 14.104658 15.943813
## Entikong 5.517848 14.494251 20.823156
## Aruk 4.235824 16.102210 18.353110
## Nanga Badau 0.000000 6.406959 11.969832
## Perbatasan Laut 10.602918 9.550059 13.023626
## Perbatasan Darat 10.691133 12.071334 13.935074Setelah melakukan transformasi data, lalu uji asumsi normalitas pada setiap data transformasi.
mvn(data = dataMDS, mvnTest = "mardia")## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 221.657147366555 4.80809166773043e-42 NO
## 2 Mardia Kurtosis 19.8436896559753 0 NO
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Anderson-Darling Q1 9.4919 <0.001 NO
## 2 Anderson-Darling Q2 6.9835 <0.001 NO
## 3 Anderson-Darling Q3 6.4195 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th Skew Kurtosis
## Q1 29 2680.00 11950.83 76 0 64634 2 510 4.796147 21.90678
## Q2 29 10060.28 29693.32 965 0 157031 177 6329 4.213894 17.76269
## Q3 29 17786.45 48688.31 2209 0 256411 706 13929 4.080275 16.94530#mvn(data = logdataMDS, mvnTest = "mardia")mvn(data = srdataMDS, mvnTest = "mardia")## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 104.013920025033 8.54544698371292e-18 NO
## 2 Mardia Kurtosis 9.23756087103024 0 NO
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Anderson-Darling Q1 5.0097 <0.001 NO
## 2 Anderson-Darling Q2 2.6025 <0.001 NO
## 3 Anderson-Darling Q3 2.5037 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th Skew
## Q1 29 22.50345 47.44705 8.717798 0 254.2322 1.414214 22.58318 4.010613
## Q2 29 60.71424 81.25085 31.064449 0 396.2714 13.304135 79.55501 2.592000
## Q3 29 83.36956 105.93849 47.000000 0 506.3704 26.570661 118.02118 2.364583
## Kurtosis
## Q1 16.664172
## Q2 7.561177
## Q3 6.268713mvn(data = crdataMDS, mvnTest = "mardia")## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 50.8772980408918 1.83986991079988e-07 NO
## 2 Mardia Kurtosis 4.41786227101635 9.96819044085662e-06 NO
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Anderson-Darling Q1 2.2602 <0.001 NO
## 2 Anderson-Darling Q2 0.9697 0.0125 NO
## 3 Anderson-Darling Q3 1.0581 0.0075 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th Skew
## Q1 29 6.215639 7.806063 4.235824 0 40.13165 1.259921 7.98957 2.823033
## Q2 29 13.239916 11.713394 9.881945 0 53.95046 5.614672 18.49744 1.541645
## Q3 29 16.567568 13.941856 13.023626 0 63.53000 8.904337 24.06061 1.443223
## Kurtosis
## Q1 9.507048
## Q2 2.861226
## Q3 2.269059Ternyata setelah di transformasi pun data tidak juga berdistribusi normal. Maka, lanjut dengan model yang sederhana.
Analisis MDS
# Cmpute MDS
mds <- dataMDS3 %>%
dist() %>%
cmdscale() %>%
as_tibble()## Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if
## `.name_repair` is omitted as of tibble 2.0.0.
## i Using compatibility `.name_repair`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.colnames(mds) <- c("Dim.1", "Dim.2")
# Plot MDS
ggscatter(mds, x = "Dim.1", y = "Dim.2",
label = rownames(dataMDS3),
size = 1,
repel = TRUE)
Jarak Euclidean
euclidean.distance <- function(x) {
n=nrow(x)
dist.mat=matrix(0, n, n)
xj=x[1,]
for (i in 1:n) {
for (j in 1:n) {
yj=x[j,]
d=sqrt(as.matrix(xj - yj) %*% as.matrix(t(xj - yj)))
dist.mat[j,i]=d
}
xj=x[1+i,]
rownames(dist.mat)=PintuMasuk
colnames(dist.mat)=PintuMasuk
}
return(dist.mat)
}
D=euclidean.distance(dataMDS3) ;D## Ngurah Rai Soekarno Hatta Juanda Kualanamu
## Ngurah Rai 0.000 587886.9 84895.44 65002.57
## Soekarno Hatta 587886.941 0.0 509427.02 532486.28
## Juanda 84895.438 509427.0 0.00 36774.50
## Kualanamu 65002.574 532486.3 36774.50 0.00
## Husein Sastranegara 32872.503 619921.7 116191.46 96207.21
## Adi Sucipto/YIA 30169.005 617472.4 113598.03 93399.38
## Bandara Int'l Lombok 30634.037 617608.1 113850.96 93665.38
## Sam Ratulangi 30977.177 618025.5 114373.47 94406.38
## Minangkabau 32032.541 619235.3 115495.08 95398.69
## Sultan Syarif Kasim II 26125.295 613428.0 109100.41 88865.72
## Sultan Iskandar Muda 30444.598 617712.8 114090.25 93847.71
## Ahmad Yani 32766.189 619800.6 116095.60 96118.30
## Supadio 32864.188 619914.3 116182.50 96197.53
## Hasanuddin 15251.972 593091.7 86982.46 69882.71
## Sultan Badaruddin II 32300.034 619295.5 115589.23 95369.42
## LainnyaA 28276.085 611709.0 110057.42 88366.46
## Batam 126006.505 468328.7 60138.46 73792.58
## Tanjung Uban 30862.848 617795.0 114315.08 94417.53
## Tanjung Pinang 23637.850 610570.6 107127.27 87334.14
## Tanjung Balai Karimun 9852.783 586452.3 81170.08 61307.82
## Tanjung Benoa 32763.667 619811.8 116083.76 96107.22
## LainnyaB 8463.288 584737.3 80418.22 61049.91
## Jayapura 32746.283 619813.8 116077.30 96081.77
## Atambua 23240.211 610107.2 106519.57 86745.36
## Entikong 16298.391 601351.0 98144.73 76906.10
## Aruk 18265.509 605402.4 101765.33 81708.62
## Nanga Badau 29588.173 616781.4 112734.98 92758.41
## LainnyaC 32874.350 619923.9 116192.96 96208.50
## Perbatasan Laut 27776.309 614768.1 111431.16 91343.82
## Perbatasan Darat 25914.915 612964.7 110010.16 89600.20
## Husein Sastranegara Adi Sucipto/YIA Bandara Int'l Lombok
## Ngurah Rai 32872.50251 30169.005 30634.037
## Soekarno Hatta 619921.72910 617472.356 617608.050
## Juanda 116191.45566 113598.027 113850.958
## Kualanamu 96207.21196 93399.380 93665.376
## Husein Sastranegara 0.00000 3230.611 3204.737
## Adi Sucipto/YIA 3230.61062 0.000 3260.502
## Bandara Int'l Lombok 3204.73727 3260.502 0.000
## Sam Ratulangi 1934.52061 2126.396 2252.272
## Minangkabau 1543.00843 2303.648 3365.976
## Sultan Syarif Kasim II 8329.04142 5275.068 7345.437
## Sultan Iskandar Muda 3094.33806 2184.311 3469.765
## Ahmad Yani 150.71828 3165.653 3130.598
## Supadio 21.21320 3218.947 3198.608
## Hasanuddin 30303.65508 28063.562 27981.775
## Sultan Badaruddin II 2420.36609 3893.467 1922.232
## LainnyaA 17415.82266 17071.572 15148.015
## Batam 158371.35237 155856.680 155975.024
## Tanjung Uban 2275.60212 2733.623 2364.876
## Tanjung Pinang 9606.07745 7514.929 7370.295
## Tanjung Balai Karimun 36245.15268 33632.831 33832.654
## Tanjung Benoa 116.38728 3136.748 3142.841
## LainnyaB 36973.36604 34463.470 34517.921
## Jayapura 165.96988 3074.921 3155.069
## Atambua 10076.00005 8008.234 7601.893
## Entikong 22474.35470 19509.230 21067.910
## Aruk 15283.08604 12986.087 12893.554
## Nanga Badau 3638.55370 1633.047 2721.095
## LainnyaC 5.09902 3231.632 3206.090
## Perbatasan Laut 5855.22792 3499.492 4978.217
## Perbatasan Darat 8482.58893 6159.235 7376.906
## Sam Ratulangi Minangkabau Sultan Syarif Kasim II
## Ngurah Rai 30977.1774 32032.541 26125.295
## Soekarno Hatta 618025.5350 619235.298 613427.979
## Juanda 114373.4667 115495.084 109100.409
## Kualanamu 94406.3759 95398.692 88865.719
## Husein Sastranegara 1934.5206 1543.008 8329.041
## Adi Sucipto/YIA 2126.3958 2303.648 5275.068
## Bandara Int'l Lombok 2252.2715 3365.976 7345.437
## Sam Ratulangi 0.0000 1829.117 6927.839
## Minangkabau 1829.1170 0.000 7342.384
## Sultan Syarif Kasim II 6927.8393 7342.384 0.000
## Sultan Iskandar Muda 2034.0978 2434.174 6383.197
## Ahmad Yani 1822.1871 1508.218 8265.737
## Supadio 1927.5918 1543.151 8317.838
## Hasanuddin 28666.5105 29808.076 24106.423
## Sultan Badaruddin II 2698.8864 2870.415 8654.231
## LainnyaA 16499.1609 17353.820 18220.816
## Batam 156463.0284 157600.375 151930.322
## Tanjung Uban 805.5861 2300.643 7309.201
## Tanjung Pinang 7707.1373 8985.097 6443.176
## Tanjung Balai Karimun 34449.3977 35477.496 29379.607
## Tanjung Benoa 1828.4589 1479.202 8232.727
## LainnyaB 35122.0658 36240.003 30488.450
## Jayapura 1828.3709 1390.431 8180.147
## Atambua 8213.2475 9576.098 6742.937
## Entikong 20807.9226 21398.407 15047.950
## Aruk 13409.5274 14587.789 10254.769
## Nanga Badau 2263.4299 2978.500 5086.488
## LainnyaC 1936.8733 1543.000 8329.602
## Perbatasan Laut 4309.5970 4861.350 4707.065
## Perbatasan Darat 6959.6731 7278.665 5881.513
## Sultan Iskandar Muda Ahmad Yani Supadio Hasanuddin
## Ngurah Rai 30444.598 32766.1894 32864.18821 15251.97
## Soekarno Hatta 617712.795 619800.5680 619914.27881 593091.73
## Juanda 114090.246 116095.5960 116182.50199 86982.46
## Kualanamu 93847.706 96118.3016 96197.53143 69882.71
## Husein Sastranegara 3094.338 150.7183 21.21320 30303.66
## Adi Sucipto/YIA 2184.311 3165.6527 3218.94657 28063.56
## Bandara Int'l Lombok 3469.765 3130.5975 3198.60829 27981.77
## Sam Ratulangi 2034.098 1822.1871 1927.59176 28666.51
## Minangkabau 2434.174 1508.2178 1543.15100 29808.08
## Sultan Syarif Kasim II 6383.197 8265.7374 8317.83752 24106.42
## Sultan Iskandar Muda 0.000 3026.3741 3087.33413 28692.35
## Ahmad Yani 3026.374 0.0000 147.68886 30222.52
## Supadio 3087.334 147.6889 0.00000 30294.57
## Hasanuddin 28692.353 30222.5161 30294.56702 0.00
## Sultan Badaruddin II 3891.131 2390.8241 2420.50656 29648.77
## LainnyaA 16016.419 17355.4367 17414.53155 28547.51
## Batam 155923.082 158264.4327 158364.43758 132538.86
## Tanjung Uban 2208.022 2147.1383 2271.87962 28679.79
## Tanjung Pinang 7791.412 9493.8820 9600.43541 22277.72
## Tanjung Balai Karimun 33985.201 36156.3609 36236.38943 11927.14
## Tanjung Benoa 3013.254 94.7312 109.78160 30203.18
## LainnyaB 34702.863 36875.6334 36965.53368 13296.31
## Jayapura 2969.168 176.4653 160.38703 30206.64
## Atambua 8292.704 9965.9231 10068.22978 21408.80
## Entikong 20093.325 22391.5714 22466.86318 20938.17
## Aruk 13034.877 15183.2440 15276.83616 17952.67
## Nanga Badau 2743.837 3565.5253 3628.41246 26909.16
## LainnyaC 3094.751 153.2449 21.58703 30304.58
## Perbatasan Laut 4050.707 5734.6023 5847.71716 26413.20
## Perbatasan Darat 6311.992 8371.1964 8479.12773 25939.25
## Sultan Badaruddin II LainnyaA Batam Tanjung Uban
## Ngurah Rai 32300.034 28276.08 126006.50 30862.8481
## Soekarno Hatta 619295.470 611708.98 468328.72 617794.9629
## Juanda 115589.225 110057.42 60138.46 114315.0842
## Kualanamu 95369.422 88366.46 73792.58 94417.5314
## Husein Sastranegara 2420.366 17415.82 158371.35 2275.6021
## Adi Sucipto/YIA 3893.467 17071.57 155856.68 2733.6225
## Bandara Int'l Lombok 1922.232 15148.01 155975.02 2364.8759
## Sam Ratulangi 2698.886 16499.16 156463.03 805.5861
## Minangkabau 2870.415 17353.82 157600.38 2300.6434
## Sultan Syarif Kasim II 8654.231 18220.82 151930.32 7309.2005
## Sultan Iskandar Muda 3891.131 16016.42 155923.08 2208.0220
## Ahmad Yani 2390.824 17355.44 158264.43 2147.1383
## Supadio 2420.507 17414.53 158364.44 2271.8796
## Hasanuddin 29648.774 28547.51 132538.86 28679.7904
## Sultan Badaruddin II 0.000 15658.09 157613.67 2801.6236
## LainnyaA 15658.089 0.00 148788.04 16100.1811
## Batam 157613.669 148788.04 0.00 156277.8133
## Tanjung Uban 2801.624 16100.18 156277.81 0.0000
## Tanjung Pinang 9021.329 14599.23 148920.92 7550.3262
## Tanjung Balai Karimun 35490.096 30904.13 123892.06 34430.4570
## Tanjung Benoa 2411.604 17380.44 158265.07 2178.4781
## LainnyaB 36184.731 30773.64 122174.29 35023.9806
## Jayapura 2423.036 17391.02 158256.23 2202.1319
## Atambua 9318.793 14015.13 148521.74 8054.4928
## Entikong 22592.174 25207.48 139809.39 20954.0461
## Aruk 14544.987 14414.51 143189.74 13275.1340
## Nanga Badau 3772.587 16516.76 155198.10 2726.8509
## LainnyaC 2420.420 17416.08 158373.26 2278.0647
## Perbatasan Laut 5963.849 16353.66 153424.00 4306.6199
## Perbatasan Darat 8265.486 15618.78 151312.91 6860.2584
## Tanjung Pinang Tanjung Balai Karimun Tanjung Benoa
## Ngurah Rai 23637.850 9852.783 32763.6675
## Soekarno Hatta 610570.586 586452.336 619811.7719
## Juanda 107127.274 81170.083 116083.7564
## Kualanamu 87334.145 61307.823 96107.2243
## Husein Sastranegara 9606.077 36245.153 116.3873
## Adi Sucipto/YIA 7514.929 33632.831 3136.7485
## Bandara Int'l Lombok 7370.295 33832.654 3142.8409
## Sam Ratulangi 7707.137 34449.398 1828.4589
## Minangkabau 8985.097 35477.496 1479.2021
## Sultan Syarif Kasim II 6443.176 29379.607 8232.7269
## Sultan Iskandar Muda 7791.412 33985.201 3013.2541
## Ahmad Yani 9493.882 36156.361 94.7312
## Supadio 9600.435 36236.389 109.7816
## Hasanuddin 22277.717 11927.142 30203.1800
## Sultan Badaruddin II 9021.329 35490.096 2411.6040
## LainnyaA 14599.229 30904.131 17380.4381
## Batam 148920.925 123892.064 158265.0653
## Tanjung Uban 7550.326 34430.457 2178.4781
## Tanjung Pinang 0.000 27382.006 9500.6327
## Tanjung Balai Karimun 27382.006 0.000 36140.7141
## Tanjung Benoa 9500.633 36140.714 0.0000
## LainnyaB 27758.221 4136.569 36867.4026
## Jayapura 9499.364 36124.546 109.4989
## Atambua 1723.672 26753.127 9971.6994
## Entikong 15414.025 21234.797 22377.7509
## Aruk 6145.531 21634.504 15180.2283
## Nanga Badau 6742.545 32764.328 3536.4079
## LainnyaC 9608.405 36246.096 118.1186
## Perbatasan Laut 5669.708 31590.791 5747.3100
## Perbatasan Darat 5470.232 30082.975 8383.4536
## LainnyaB Jayapura Atambua Entikong Aruk
## Ngurah Rai 8463.288 32746.2831 23240.211 16298.39 18265.509
## Soekarno Hatta 584737.255 619813.8389 610107.224 601351.01 605402.387
## Juanda 80418.220 116077.3018 106519.573 98144.73 101765.331
## Kualanamu 61049.915 96081.7742 86745.357 76906.10 81708.616
## Husein Sastranegara 36973.366 165.9699 10076.000 22474.35 15283.086
## Adi Sucipto/YIA 34463.470 3074.9206 8008.234 19509.23 12986.087
## Bandara Int'l Lombok 34517.921 3155.0688 7601.893 21067.91 12893.554
## Sam Ratulangi 35122.066 1828.3709 8213.248 20807.92 13409.527
## Minangkabau 36240.003 1390.4312 9576.098 21398.41 14587.789
## Sultan Syarif Kasim II 30488.450 8180.1475 6742.937 15047.95 10254.769
## Sultan Iskandar Muda 34702.863 2969.1682 8292.704 20093.32 13034.877
## Ahmad Yani 36875.633 176.4653 9965.923 22391.57 15183.244
## Supadio 36965.534 160.3870 10068.230 22466.86 15276.836
## Hasanuddin 13296.309 30206.6421 21408.801 20938.17 17952.672
## Sultan Badaruddin II 36184.731 2423.0361 9318.793 22592.17 14544.987
## LainnyaA 30773.639 17391.0158 14015.129 25207.48 14414.508
## Batam 122174.294 158256.2316 148521.744 139809.39 143189.744
## Tanjung Uban 35023.981 2202.1319 8054.493 20954.05 13275.134
## Tanjung Pinang 27758.221 9499.3636 1723.672 15414.02 6145.531
## Tanjung Balai Karimun 4136.569 36124.5462 26753.127 21234.80 21634.504
## Tanjung Benoa 36867.403 109.4989 9971.699 22377.75 15180.228
## LainnyaB 0.000 36857.7377 27179.598 22136.44 21929.478
## Jayapura 36857.738 0.0000 9976.108 22329.04 15171.273
## Atambua 27179.598 9976.1081 0.000 15845.42 5803.272
## Entikong 22136.436 22329.0400 15845.423 0.00 12928.549
## Aruk 21929.478 15171.2730 5803.272 12928.55 0.000
## Nanga Badau 33603.493 3505.2582 7090.795 19492.95 12176.554
## LainnyaC 36974.699 166.4151 10077.998 22476.14 15284.740
## Perbatasan Laut 32256.819 5715.6853 6276.016 17644.96 11003.057
## Perbatasan Darat 30614.124 8339.8757 6393.887 15946.39 9680.821
## Nanga Badau LainnyaC Perbatasan Laut
## Ngurah Rai 29588.173 32874.34956 27776.309
## Soekarno Hatta 616781.427 619923.93939 614768.060
## Juanda 112734.982 116192.95996 111431.159
## Kualanamu 92758.413 96208.50299 91343.824
## Husein Sastranegara 3638.554 5.09902 5855.228
## Adi Sucipto/YIA 1633.047 3231.63163 3499.492
## Bandara Int'l Lombok 2721.095 3206.08999 4978.217
## Sam Ratulangi 2263.430 1936.87325 4309.597
## Minangkabau 2978.500 1543.00000 4861.350
## Sultan Syarif Kasim II 5086.488 8329.60209 4707.065
## Sultan Iskandar Muda 2743.837 3094.75136 4050.707
## Ahmad Yani 3565.525 153.24490 5734.602
## Supadio 3628.412 21.58703 5847.717
## Hasanuddin 26909.159 30304.58145 26413.205
## Sultan Badaruddin II 3772.587 2420.42021 5963.849
## LainnyaA 16516.764 17416.08173 16353.658
## Batam 155198.101 158373.26287 153424.002
## Tanjung Uban 2726.851 2278.06475 4306.620
## Tanjung Pinang 6742.545 9608.40486 5669.708
## Tanjung Balai Karimun 32764.328 36246.09624 31590.791
## Tanjung Benoa 3536.408 118.11858 5747.310
## LainnyaB 33603.493 36974.69908 32256.819
## Jayapura 3505.258 166.41514 5715.685
## Atambua 7090.795 10077.99826 6276.016
## Entikong 19492.952 22476.14426 17644.964
## Aruk 12176.554 15284.74046 11003.057
## Nanga Badau 0.000 3639.06293 3482.370
## LainnyaC 3639.063 0.00000 5856.576
## Perbatasan Laut 3482.370 5856.57579 0.000
## Perbatasan Darat 6197.319 8483.98014 3340.733
## Perbatasan Darat
## Ngurah Rai 25914.915
## Soekarno Hatta 612964.734
## Juanda 110010.161
## Kualanamu 89600.204
## Husein Sastranegara 8482.589
## Adi Sucipto/YIA 6159.235
## Bandara Int'l Lombok 7376.906
## Sam Ratulangi 6959.673
## Minangkabau 7278.665
## Sultan Syarif Kasim II 5881.513
## Sultan Iskandar Muda 6311.992
## Ahmad Yani 8371.196
## Supadio 8479.128
## Hasanuddin 25939.252
## Sultan Badaruddin II 8265.486
## LainnyaA 15618.776
## Batam 151312.910
## Tanjung Uban 6860.258
## Tanjung Pinang 5470.232
## Tanjung Balai Karimun 30082.975
## Tanjung Benoa 8383.454
## LainnyaB 30614.124
## Jayapura 8339.876
## Atambua 6393.887
## Entikong 15946.394
## Aruk 9680.821
## Nanga Badau 6197.319
## LainnyaC 8483.980
## Perbatasan Laut 3340.733
## Perbatasan Darat 0.000Score MDS
d <- dist(dataMDS3)
fit <- cmdscale(d, eig=TRUE, k=2)
fit## $points
## [,1] [,2]
## Ngurah Rai 7796.233 -1054.48980
## Soekarno Hatta -579878.898 14093.17202
## Juanda -71928.124 -18102.17465
## Kualanamu -49235.764 -28026.59314
## Husein Sastranegara 39965.041 4492.56337
## Adi Sucipto/YIA 37496.604 3345.55862
## Bandara Int'l Lombok 37634.854 3570.27269
## Sam Ratulangi 38065.465 4330.33776
## Minangkabau 39271.087 4060.39634
## Sultan Syarif Kasim II 33410.413 1563.11401
## Sultan Iskandar Muda 37737.384 3576.05398
## Ahmad Yani 39844.965 4561.94525
## Supadio 39957.538 4487.89474
## Hasanuddin 12856.647 -3123.35057
## Sultan Badaruddin II 39326.120 3846.05268
## LainnyaA 31488.212 67.40947
## Batam -113850.547 -24808.25587
## Tanjung Uban 37841.258 4823.11811
## Tanjung Pinang 30589.444 3411.99069
## Tanjung Balai Karimun 6147.852 -7776.45136
## Tanjung Benoa 39855.076 4493.85153
## LainnyaB 4515.930 -5338.70569
## Jayapura 39856.336 4434.50430
## Atambua 30123.039 3131.73464
## Entikong 21187.740 -959.36803
## Aruk 25361.706 653.32427
## Nanga Badau 36802.961 3146.10185
## LainnyaC 39967.225 4490.51496
## Perbatasan Laut 34810.313 4593.30346
## Perbatasan Darat 32983.889 4016.17435
##
## $eig
## [1] 3.855727e+11 2.330639e+09 1.140556e+09 4.213695e+08 2.400745e+08
## [6] 2.477316e+07 1.694645e+07 7.359355e+06 5.172673e+06 1.456834e+06
## [11] 4.533218e+05 1.913154e+04 4.843609e-06 1.389815e-06 1.221929e-06
## [16] 6.284558e-07 2.873081e-07 2.293425e-07 2.031804e-07 1.017458e-07
## [21] 9.384175e-08 1.894357e-08 -4.200348e-09 -5.135048e-09 -4.908882e-08
## [26] -1.962708e-07 -2.076993e-07 -2.406488e-07 -7.569048e-07 -3.045672e-06
##
## $x
## NULL
##
## $ac
## [1] 0
##
## $GOF
## [1] 0.9952325 0.9952325Penentuan Jumlah Cluster
a = fviz_nbclust(dataMDS3, kmeans, method = "gap_stat")
plot(a)
Analisis Clustering
# K-means clustering
clust <- kmeans(mds, 3)$cluster %>%
as.factor()
mds <- mds %>%
mutate(groups = clust)
# Plot and color by groups
ggscatter(mds, x = "Dim.1", y = "Dim.2",
label = rownames(dataMDS3),
color = "groups",
palette = "jco",
size = 1,
ellipse = TRUE,
ellipse.type = "convex",
repel = TRUE)