Analisis Principal Component Analysis (PCA) dan Factor Analysis (FA) pada Indikator SDGs ASEAN
Horasio Nissi Immanuel (24031554068)
Muhammad Zaidan Al anwary (24031554124)
2026-03-02
Pendahuluan
Analisis ini bertujuan untuk mengidentifikasi struktur utama yang mendasari indikator Sustainable Development Goals (SDGs) di negara-negara ASEAN menggunakan metode Principal Component Analysis (PCA) dan Factor Analysis (FA).
PCA digunakan untuk mereduksi dimensi data, sedangkan FA digunakan untuk mengidentifikasi faktor laten yang merepresentasikan kelompok indikator yang saling berkorelasi.
Library
library(readxl)
library(dplyr)
library(ggplot2)
library(psych)
library(corrplot)
library(factoextra)
library(reshape2)
library(knitr)Load Dataset
df <- read_excel("asean_sdg_final_clean.xlsx")
dim(df)## [1] 90 123head(df)## # A tibble: 6 × 123
## AMS Year [SDG.1.5.1] - Number of death…¹ [SDG.1.5.3] - ASEAN …²
## <chr> <dbl> <dbl> <dbl>
## 1 Brunei Darussalam 2016 2351. 1
## 2 Brunei Darussalam 2017 2351. 1
## 3 Brunei Darussalam 2018 0 1
## 4 Brunei Darussalam 2019 2351. 1
## 5 Brunei Darussalam 2020 2351. 1
## 6 Brunei Darussalam 2021 0 1
## # ℹ abbreviated names:
## # ¹​`[SDG.1.5.1] - Number of deaths, missing persons and directly affected persons attributed to climate-related disasters per 100,000 population (Number per 100,000 population)`,
## # ²​`[SDG.1.5.3] - ASEAN countries that adopt and implement national disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015–2030 (1 = Yes, 0 = No)`
## # ℹ 119 more variables:
## # `[SDG.1.5.4] - ASEAN countries that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies (1 = Yes, 0 = No)` <dbl>,
## # `[SDG.1.a.2] - Proportion of total government spending on education (%)` <dbl>,
## # `[SDG.1.a.2] - Proportion of total government spending on health (%)` <dbl>, …Dataset terdiri dari observasi negara ASEAN per tahun dengan sejumlah indikator SDGs yang telah dibersihkan dan direduksi.
Pilih Variabel Numerik
X <- df %>%
select(-AMS, -Year)
dim(X)## [1] 90 121Kolom AMS dan Year tidak disertakan dalam
analisis karena bukan variabel numerik.
Cek Variabel Numerik
X <- as.data.frame(lapply(X, as.numeric))
dim(X)## [1] 90 121Drop Variabel dengan Variansi Nol
varians <- apply(X, 2, var, na.rm = TRUE)
X <- X[, varians > 0]
dim(X)## [1] 90 103Variabel dengan variansi nol berarti memiliki nilai yang sama untuk seluruh observasi. Variabel seperti ini tidak memberikan informasi dalam analisis karena tidak memiliki variasi. Oleh karena itu, variabel dengan variansi nol dihapus dari dataset.
Drop variabel dengan korelasi terlalu tinggi (> 0.95)
corr_matrix <- cor(X)
upper <- corr_matrix
upper[lower.tri(upper, diag = TRUE)] <- NA
high_corr_cols <- colnames(upper)[
apply(upper, 2, function(x) any(abs(x) > 0.95, na.rm = TRUE))
]
X <- X[, !(colnames(X) %in% high_corr_cols)]
dim(X)## [1] 90 83Jika dua atau lebih variabel memiliki korelasi yang sangat tinggi (mendekati 1 atau -1), maka variabel tersebut mengandung informasi yang sangat mirip. Hal ini dapat menyebabkan multikolinearitas dan membuat matriks korelasi menjadi tidak stabil. Oleh karena itu, salah satu variabel dengan korelasi > 0.95 dihapus.
Batasi jumlah variabel (TOP 25 VARIANSI TERBESAR)
varians <- apply(X, 2, var, na.rm = TRUE)
top_vars <- names(sort(varians, decreasing = TRUE))[1:25]
X <- X[, top_vars]
dim(X)## [1] 90 25Karena jumlah observasi (90) lebih kecil dibanding jumlah variabel
awal, maka dilakukan pembatasan jumlah variabel. Dipilih 25 variabel
dengan variansi terbesar agar:
- Rasio observasi terhadap variabel lebih stabil
- Matriks korelasi tidak singular
- KMO dan Bartlett dapat dihitung dengan baik
Standardisasi Data
X_scaled <- scale(X)Standardisasi dilakukan karena PCA dan FA sensitif terhadap perbedaan skala antar variabel.
Uji Kelayakan Analisis (KMO dan Bartlett)
KMO(X_scaled)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = X_scaled)
## Overall MSA = 0.73
## MSA for each item =
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands.
## 0.61
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes.
## 0.74
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands.
## 0.78
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands.
## 0.62
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD.
## 0.64
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes.
## 0.58
## X.SDG.8.5.1....Average.hourly.earnings.of.employees..Current.local.currency.
## 0.63
## X.SDG.1.5.1....Number.of.deaths..missing.persons.and.directly.affected.persons.attributed.to.climate.related.disasters.per.100.000.population..Number.per.100.000.population.
## 0.63
## X.SDG.9.1.2....Passenger.volumes..8..by.rail..Million.Passenger.kilometer.
## 0.77
## X.SDG.9.1.2....Freight.volumes..1..by.air..Thousands.tonnes.
## 0.73
## X.SDG.3.3.2....Tuberculosis.incidence.per.100.000.population..Number.per.100.000.population.
## 0.70
## X.SDG.7.3.1....Energy.intensity.measured.in.terms.of.primary.energy.and.GDP..TOE...thousand.2010.USD.
## 0.73
## X.SDG.12.a.1....Installed.renewable.energy.generating.capacity.in.developing.countries..in.Watts.per.capita.
## 0.57
## X.SDG.3.1.1....Maternal.mortality.ratio..Number.per.100.000.live.births.
## 0.91
## X.SDG.4.a.1....Proportion.of.primary.schools.with.access.to..c..computer.for.pedagogical.purposes....
## 0.70
## X.SDG.8.10.1....Number.of.automatic.teller.machines..ATMs..per.100.000.adults..Number.per.100.000.adults.
## 0.79
## X.SDG.4.1.2....Completion.rate.in.upper.secondary.education....
## 0.79
## X.SDG.17.1.2....Proportion.of.domestic.budget.funded.by.domestic.taxes....
## 0.51
## X.SDG.3.c.1....Density.of.nursing.and.midwifery.personnel..Number.per.10.000.population.
## 0.74
## X.SDG.6.1.1....Proportion.of.population.using.safely.managed.drinking.water.services....
## 0.48
## X.SDG.4.a.1....Proportion.of.primary.schools.with.access.to..a..electricity....
## 0.78
## X.SDG.3.2.1....Under.five.mortality.rate..Number.per.1.000.live.births.
## 0.81
## X.SDG.4.2.2....Participation.rate.in.organized.learning..one.year.before.the.official.primary.entry.age...both.sexes....
## 0.84
## X.SDG.7.2.1....Renewable.energy.share.in.the.total.final.energy.consumption....
## 0.85
## X.SDG.3.b.1....Proportion.of.the.target.population.covered.by.Measles.containing.vaccines..2nd.dose.....
## 0.69cortest.bartlett(cor(X_scaled), n = nrow(X_scaled))## $chisq
## [1] 2305.416
##
## $p.value
## [1] 1.150098e-305
##
## $df
## [1] 300Interpretasi:
- Nilai KMO > 0.5 menunjukkan data layak untuk analisis faktor.
- Nilai p-value Bartlett < 0.05 menunjukkan terdapat korelasi
signifikan antar variabel.
Hasil uji KMO menunjukkan nilai sebesar 0.73, yang berada pada kategori cukup baik. Hal ini menunjukkan bahwa data memiliki kecukupan sampel untuk dilakukan analisis faktor.
Uji Bartlett menghasilkan nilai p-value sebesar 1.15e-305 (< 0.05), sehingga matriks korelasi secara signifikan berbeda dari matriks identitas. Artinya, terdapat korelasi antar variabel yang memadai untuk dilakukan PCA dan FA.
Statistik Deskriptif
desc_stats <- data.frame(
Mean = colMeans(X),
SD = apply(X, 2, sd),
Min = apply(X, 2, min),
Max = apply(X, 2, max)
)
kable(round(desc_stats,2), caption = "Tabel 1. Statistik Deskriptif Variabel Penelitian")| Mean | SD | Min | Max | |
|---|---|---|---|---|
| X.SDG.9.1.2….Number.of.passengers..3..by.rail..Thousands. | 325659.89 | 315452.25 | 0.60 | 1326000.00 |
| X.SDG.9.1.2….Freight.volumes..5..by.sea..Thousands.tonnes. | 272489.96 | 204663.21 | 1020.23 | 630125.30 |
| X.SDG.9.1.2….Number.of.passengers..1..by.air..Thousands. | 46874.90 | 40314.30 | 47.74 | 163701.00 |
| X.SDG.9.1.2….Number.of.passengers..5..by.sea..Thousands. | 19595.32 | 24122.21 | 0.54 | 106710.00 |
| X.SDG.9.2.1….Manufacturing.value.added.per.capita..constant.2015.USD…in.USD. | 12370.14 | 17706.99 | 179.03 | 68554.14 |
| X.SDG.9.1.2….Freight.volumes..3..by.rail..Thousands.tonnes. | 12878.08 | 14908.13 | 0.64 | 73496.00 |
| X.SDG.8.5.1….Average.hourly.earnings.of.employees..Current.local.currency. | 4087.15 | 4667.70 | 0.91 | 19027.00 |
| X.SDG.1.5.1….Number.of.deaths..missing.persons.and.directly.affected.persons.attributed.to.climate.related.disasters.per.100.000.population..Number.per.100.000.population. | 2350.56 | 4352.77 | 0.00 | 27300.00 |
| X.SDG.9.1.2….Passenger.volumes..8..by.rail..Million.Passenger.kilometer. | 3612.57 | 2569.61 | 2.30 | 10690.00 |
| X.SDG.9.1.2….Freight.volumes..1..by.air..Thousands.tonnes. | 679.65 | 645.91 | 0.64 | 2154.88 |
| X.SDG.3.3.2….Tuberculosis.incidence.per.100.000.population..Number.per.100.000.population. | 215.64 | 170.43 | 34.10 | 643.00 |
| X.SDG.7.3.1….Energy.intensity.measured.in.terms.of.primary.energy.and.GDP..TOE…thousand.2010.USD. | 47.28 | 90.43 | 0.13 | 438.00 |
| X.SDG.12.a.1….Installed.renewable.energy.generating.capacity.in.developing.countries..in.Watts.per.capita. | 91.15 | 71.27 | 0.20 | 290.23 |
| X.SDG.3.1.1….Maternal.mortality.ratio..Number.per.100.000.live.births. | 72.61 | 65.62 | 0.00 | 305.00 |
| X.SDG.4.a.1….Proportion.of.primary.schools.with.access.to..c..computer.for.pedagogical.purposes…. | 69.45 | 29.52 | 0.78 | 100.00 |
| X.SDG.8.10.1….Number.of.automatic.teller.machines..ATMs..per.100.000.adults..Number.per.100.000.adults. | 48.32 | 26.37 | 9.00 | 115.00 |
| X.SDG.4.1.2….Completion.rate.in.upper.secondary.education…. | 69.49 | 24.40 | 11.71 | 99.25 |
| X.SDG.17.1.2….Proportion.of.domestic.budget.funded.by.domestic.taxes…. | 67.05 | 23.66 | 16.99 | 160.60 |
| X.SDG.3.c.1….Density.of.nursing.and.midwifery.personnel..Number.per.10.000.population. | 33.21 | 23.33 | 2.00 | 83.00 |
| X.SDG.6.1.1….Proportion.of.population.using.safely.managed.drinking.water.services…. | 91.88 | 21.41 | 6.70 | 190.00 |
| X.SDG.4.a.1….Proportion.of.primary.schools.with.access.to..a..electricity…. | 86.07 | 19.57 | 28.17 | 100.00 |
| X.SDG.3.2.1….Under.five.mortality.rate..Number.per.1.000.live.births. | 23.02 | 18.82 | 2.10 | 71.70 |
| X.SDG.4.2.2….Participation.rate.in.organized.learning..one.year.before.the.official.primary.entry.age…both.sexes…. | 76.36 | 18.65 | 24.37 | 99.70 |
| X.SDG.7.2.1….Renewable.energy.share.in.the.total.final.energy.consumption…. | 23.14 | 17.79 | 0.00 | 61.10 |
| X.SDG.3.b.1….Proportion.of.the.target.population.covered.by.Measles.containing.vaccines..2nd.dose….. | 81.97 | 17.75 | 0.00 | 100.56 |
Tabel 1 menunjukkan bahwa nilai rata-rata (mean) antar indikator bervariasi cukup besar. Hal ini terlihat dari perbedaan nilai minimum dan maksimum yang signifikan pada beberapa variabel. Standar deviasi yang relatif tinggi pada beberapa indikator menunjukkan adanya variasi yang besar antar negara ASEAN.
Selain itu, perbedaan skala antar variabel cukup mencolok, sehingga diperlukan proses standardisasi sebelum dilakukan analisis PCA dan FA. Variasi yang tinggi ini juga mengindikasikan heterogenitas tingkat pembangunan antar negara ASEAN.
Visualisasi Distribusi
var_terbesar <- names(sort(apply(X,2,var), decreasing=TRUE))[1]
data_plot <- data.frame(Value = X[[var_terbesar]])
ggplot(data_plot, aes(x = Value)) +
geom_histogram(aes(y = after_stat(density)),
bins = 20,
fill = "skyblue",
color = "black",
alpha = 0.7) +
geom_density(color = "red", linewidth = 1) +
theme_minimal() +
labs(
x = var_terbesar,
y = "Density"
)
Gambar 1 menunjukkan bahwa distribusi nilai indikator tersebut tidak simetris dan cenderung menceng ke kanan (right-skewed). Sebagian besar observasi berada pada rentang nilai menengah, namun terdapat beberapa nilai yang jauh lebih besar dibandingkan mayoritas data. Hal ini mengindikasikan adanya perbedaan yang cukup signifikan antar negara ASEAN dalam indikator tersebut.
Keberadaan nilai ekstrem menunjukkan bahwa terdapat negara dengan tingkat indikator yang jauh lebih tinggi dibandingkan negara lainnya, sehingga memperkuat indikasi heterogenitas karakteristik pembangunan di kawasan ASEAN.
Principal Component Analysis (PCA)
pca_result <- prcomp(X_scaled, center = FALSE, scale. = FALSE)
summary(pca_result)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.8123 1.8294 1.7774 1.45648 1.41178 1.11173 0.99721
## Proportion of Variance 0.3164 0.1339 0.1264 0.08485 0.07973 0.04944 0.03978
## Cumulative Proportion 0.3164 0.4502 0.5766 0.66145 0.74118 0.79061 0.83039
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## Standard deviation 0.91952 0.77732 0.70996 0.65906 0.55871 0.54670 0.48630
## Proportion of Variance 0.03382 0.02417 0.02016 0.01737 0.01249 0.01196 0.00946
## Cumulative Proportion 0.86421 0.88838 0.90854 0.92592 0.93840 0.95036 0.95982
## PC15 PC16 PC17 PC18 PC19 PC20 PC21
## Standard deviation 0.46608 0.41039 0.39476 0.34663 0.29064 0.25617 0.22408
## Proportion of Variance 0.00869 0.00674 0.00623 0.00481 0.00338 0.00262 0.00201
## Cumulative Proportion 0.96851 0.97524 0.98148 0.98628 0.98966 0.99229 0.99430
## PC22 PC23 PC24 PC25
## Standard deviation 0.21829 0.19965 0.17839 0.15249
## Proportion of Variance 0.00191 0.00159 0.00127 0.00093
## Cumulative Proportion 0.99620 0.99780 0.99907 1.00000Hasil PCA menunjukkan bahwa PC1 menjelaskan 31.64% variasi data, PC2 sebesar 13.39%, dan hingga PC5 cumulative variance mencapai 71.02%. Oleh karena itu, lima komponen utama dipertahankan dalam analisis.
Scree Plot
fviz_eig(pca_result, addlabels = TRUE)## Warning in geom_bar(stat = "identity", fill = barfill, color = barcolor, :
## Ignoring empty aesthetic: `width`.
Scree plot digunakan untuk menentukan jumlah komponen utama yang optimal berdasarkan titik elbow.
Reduksi 5 Dimensi
pca_5_scores <- pca_result$x[,1:5]
dim(pca_5_scores)## [1] 90 5Berdasarkan scree plot dan cumulative variance, terlihat bahwa komponen pertama hingga kelima sudah mampu menjelaskan lebih dari 70% variasi data. Setelah komponen kelima, penambahan komponen memberikan peningkatan variasi yang relatif kecil. Dengan demikian, dimensi data berhasil direduksi dari 25 variabel menjadi 5 komponen utama tanpa kehilangan sebagian besar informasi (≥ 70% variasi).
Visualisasi PCA (Score Plot per Negara)
pca_scores <- as.data.frame(pca_result$x)
pca_scores$AMS <- df$AMS
pca_scores$Year <- df$Year
expl_var <- summary(pca_result)$importance[2,]
ggplot(pca_scores, aes(x = PC1, y = PC2, color = AMS)) +
geom_point(size = 3) +
theme_minimal() +
labs(
title = "Score Plot PCA Negara ASEAN",
x = paste0("PC1 (", round(expl_var[1]*100,2), "%)"),
y = paste0("PC2 (", round(expl_var[2]*100,2), "%)")
)
Visualisasi ini menunjukkan adanya pemisahan yang cukup jelas antar negara. Beberapa negara seperti Singapura terlihat terpisah dari kelompok lainnya, mengindikasikan struktur indikator yang berbeda signifikan. Negara-negara berkembang cenderung mengelompok pada area tertentu, menunjukkan kemiripan karakteristik pembangunan.
Matriks Loading PCA
loadings_pca <- pca_result$rotation
head(loadings_pca)## PC1
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. -0.252032624
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. -0.247140117
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. -0.173178789
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. -0.001363315
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. -0.109234230
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. -0.074229532
## PC2
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. 0.001959922
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. 0.103011410
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. 0.324448936
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. 0.374241010
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. 0.296008956
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. -0.218206259
## PC3
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. -0.03363348
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. -0.17721669
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. -0.18925830
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. -0.26321939
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. 0.08254687
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. -0.43138803
## PC4
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. 0.09387103
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. -0.01211972
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. 0.04353131
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. -0.27081524
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. 0.43152408
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. 0.13236363
## PC5
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. -0.16672965
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. -0.20432640
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. -0.10328825
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. 0.17105634
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. -0.09164961
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. 0.16976115
## PC6
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. 0.25844250
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. 0.38071366
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. -0.15885247
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. -0.01893301
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. -0.26428172
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. -0.09085826
## PC7
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. 0.51663273
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. -0.12362149
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. -0.15066331
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. 0.04684411
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. -0.08158736
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. 0.12792049
## PC8
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. 0.14668856
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. 0.18902639
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. -0.14322928
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. 0.02028617
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. -0.15902513
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. -0.12396114
## PC9
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. 0.086941587
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. 0.006719622
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. -0.404214699
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. 0.002968200
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. 0.151897674
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. 0.056660642
## PC10
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. -0.18392256
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. 0.04238062
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. -0.17993692
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. 0.01034851
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. 0.04904942
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. 0.15119249
## PC11
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. -0.15293212
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. 0.14521274
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. 0.02866117
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. -0.01309962
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. 0.04262003
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. 0.19406812
## PC12
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. 0.05731675
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. -0.07944389
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. 0.08444859
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. 0.16603942
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. -0.08862724
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. 0.20634517
## PC13
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. 0.004589302
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. -0.260850404
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. 0.205071484
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. 0.289195401
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. -0.176565450
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. -0.144680422
## PC14
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. 0.032266542
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. 0.108659167
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. -0.087950152
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. 0.175552680
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. -0.002956041
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. 0.034763925
## PC15
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. -0.12938520
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. -0.13319773
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. 0.10370835
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. -0.20686225
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. -0.19729944
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. -0.04979744
## PC16
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. 0.10516424
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. 0.15730372
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. 0.54165005
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. 0.18002781
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. -0.12448716
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. 0.06799947
## PC17
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. -0.006394079
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. -0.075992226
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. 0.116902523
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. -0.154149547
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. -0.164398994
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. 0.043589311
## PC18
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. -0.08326820
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. -0.23310261
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. -0.25413289
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. 0.42450319
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. -0.02728209
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. 0.24221690
## PC19
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. -0.078050061
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. -0.240386275
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. 0.230586756
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. -0.113946981
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. 0.008803942
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. -0.014630161
## PC20
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. 0.038380014
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. 0.182657879
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. 0.120675573
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. -0.363448180
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. -0.369086639
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. -0.005487957
## PC21
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. 0.26960704
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. -0.31590757
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. 0.09145498
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. -0.18047548
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. -0.18838303
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. 0.37321266
## PC22
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. -0.14731740
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. 0.46823666
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. -0.11708377
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. 0.14787448
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. -0.02803605
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. 0.06197676
## PC23
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. 0.23305985
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. 0.02342403
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. 0.11615988
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. -0.15657383
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. 0.51505202
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. 0.21317172
## PC24
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. 0.19130497
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. 0.09837044
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. -0.05411664
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. 0.05531855
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. -0.10332401
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. 0.34958036
## PC25
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands. -0.49673767
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. 0.16415683
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. 0.07606034
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. -0.19111051
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. -0.01299828
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. 0.40903605Loading menunjukkan kontribusi masing-masing variabel terhadap komponen utama.
Factor Analysis (FA)
fa_result <- fa(X_scaled, nfactors = 5, rotate = "varimax")
print(fa_result$loadings, cutoff = 0.5)##
## Loadings:
## MR1
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands.
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes.
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands.
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands.
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD.
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes.
## X.SDG.8.5.1....Average.hourly.earnings.of.employees..Current.local.currency.
## X.SDG.1.5.1....Number.of.deaths..missing.persons.and.directly.affected.persons.attributed.to.climate.related.disasters.per.100.000.population..Number.per.100.000.population.
## X.SDG.9.1.2....Passenger.volumes..8..by.rail..Million.Passenger.kilometer.
## X.SDG.9.1.2....Freight.volumes..1..by.air..Thousands.tonnes.
## X.SDG.3.3.2....Tuberculosis.incidence.per.100.000.population..Number.per.100.000.population.
## X.SDG.7.3.1....Energy.intensity.measured.in.terms.of.primary.energy.and.GDP..TOE...thousand.2010.USD.
## X.SDG.12.a.1....Installed.renewable.energy.generating.capacity.in.developing.countries..in.Watts.per.capita.
## X.SDG.3.1.1....Maternal.mortality.ratio..Number.per.100.000.live.births. -0.749
## X.SDG.4.a.1....Proportion.of.primary.schools.with.access.to..c..computer.for.pedagogical.purposes.... 0.820
## X.SDG.8.10.1....Number.of.automatic.teller.machines..ATMs..per.100.000.adults..Number.per.100.000.adults.
## X.SDG.4.1.2....Completion.rate.in.upper.secondary.education.... 0.644
## X.SDG.17.1.2....Proportion.of.domestic.budget.funded.by.domestic.taxes....
## X.SDG.3.c.1....Density.of.nursing.and.midwifery.personnel..Number.per.10.000.population. 0.597
## X.SDG.6.1.1....Proportion.of.population.using.safely.managed.drinking.water.services....
## X.SDG.4.a.1....Proportion.of.primary.schools.with.access.to..a..electricity.... 0.847
## X.SDG.3.2.1....Under.five.mortality.rate..Number.per.1.000.live.births. -0.760
## X.SDG.4.2.2....Participation.rate.in.organized.learning..one.year.before.the.official.primary.entry.age...both.sexes.... 0.686
## X.SDG.7.2.1....Renewable.energy.share.in.the.total.final.energy.consumption.... -0.749
## X.SDG.3.b.1....Proportion.of.the.target.population.covered.by.Measles.containing.vaccines..2nd.dose..... 0.560
## MR4
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands.
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes. 0.527
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands. 0.658
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands.
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD. 0.835
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes.
## X.SDG.8.5.1....Average.hourly.earnings.of.employees..Current.local.currency.
## X.SDG.1.5.1....Number.of.deaths..missing.persons.and.directly.affected.persons.attributed.to.climate.related.disasters.per.100.000.population..Number.per.100.000.population.
## X.SDG.9.1.2....Passenger.volumes..8..by.rail..Million.Passenger.kilometer. 0.725
## X.SDG.9.1.2....Freight.volumes..1..by.air..Thousands.tonnes. 0.678
## X.SDG.3.3.2....Tuberculosis.incidence.per.100.000.population..Number.per.100.000.population.
## X.SDG.7.3.1....Energy.intensity.measured.in.terms.of.primary.energy.and.GDP..TOE...thousand.2010.USD.
## X.SDG.12.a.1....Installed.renewable.energy.generating.capacity.in.developing.countries..in.Watts.per.capita. 0.544
## X.SDG.3.1.1....Maternal.mortality.ratio..Number.per.100.000.live.births.
## X.SDG.4.a.1....Proportion.of.primary.schools.with.access.to..c..computer.for.pedagogical.purposes....
## X.SDG.8.10.1....Number.of.automatic.teller.machines..ATMs..per.100.000.adults..Number.per.100.000.adults. 0.727
## X.SDG.4.1.2....Completion.rate.in.upper.secondary.education....
## X.SDG.17.1.2....Proportion.of.domestic.budget.funded.by.domestic.taxes....
## X.SDG.3.c.1....Density.of.nursing.and.midwifery.personnel..Number.per.10.000.population.
## X.SDG.6.1.1....Proportion.of.population.using.safely.managed.drinking.water.services....
## X.SDG.4.a.1....Proportion.of.primary.schools.with.access.to..a..electricity....
## X.SDG.3.2.1....Under.five.mortality.rate..Number.per.1.000.live.births.
## X.SDG.4.2.2....Participation.rate.in.organized.learning..one.year.before.the.official.primary.entry.age...both.sexes....
## X.SDG.7.2.1....Renewable.energy.share.in.the.total.final.energy.consumption....
## X.SDG.3.b.1....Proportion.of.the.target.population.covered.by.Measles.containing.vaccines..2nd.dose.....
## MR3
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands.
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes.
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands.
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands.
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD.
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes. 0.908
## X.SDG.8.5.1....Average.hourly.earnings.of.employees..Current.local.currency. 0.952
## X.SDG.1.5.1....Number.of.deaths..missing.persons.and.directly.affected.persons.attributed.to.climate.related.disasters.per.100.000.population..Number.per.100.000.population.
## X.SDG.9.1.2....Passenger.volumes..8..by.rail..Million.Passenger.kilometer.
## X.SDG.9.1.2....Freight.volumes..1..by.air..Thousands.tonnes.
## X.SDG.3.3.2....Tuberculosis.incidence.per.100.000.population..Number.per.100.000.population.
## X.SDG.7.3.1....Energy.intensity.measured.in.terms.of.primary.energy.and.GDP..TOE...thousand.2010.USD. 0.753
## X.SDG.12.a.1....Installed.renewable.energy.generating.capacity.in.developing.countries..in.Watts.per.capita.
## X.SDG.3.1.1....Maternal.mortality.ratio..Number.per.100.000.live.births.
## X.SDG.4.a.1....Proportion.of.primary.schools.with.access.to..c..computer.for.pedagogical.purposes....
## X.SDG.8.10.1....Number.of.automatic.teller.machines..ATMs..per.100.000.adults..Number.per.100.000.adults.
## X.SDG.4.1.2....Completion.rate.in.upper.secondary.education....
## X.SDG.17.1.2....Proportion.of.domestic.budget.funded.by.domestic.taxes....
## X.SDG.3.c.1....Density.of.nursing.and.midwifery.personnel..Number.per.10.000.population.
## X.SDG.6.1.1....Proportion.of.population.using.safely.managed.drinking.water.services....
## X.SDG.4.a.1....Proportion.of.primary.schools.with.access.to..a..electricity....
## X.SDG.3.2.1....Under.five.mortality.rate..Number.per.1.000.live.births.
## X.SDG.4.2.2....Participation.rate.in.organized.learning..one.year.before.the.official.primary.entry.age...both.sexes....
## X.SDG.7.2.1....Renewable.energy.share.in.the.total.final.energy.consumption....
## X.SDG.3.b.1....Proportion.of.the.target.population.covered.by.Measles.containing.vaccines..2nd.dose.....
## MR2
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands.
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes.
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands.
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands. 0.918
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD.
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes.
## X.SDG.8.5.1....Average.hourly.earnings.of.employees..Current.local.currency.
## X.SDG.1.5.1....Number.of.deaths..missing.persons.and.directly.affected.persons.attributed.to.climate.related.disasters.per.100.000.population..Number.per.100.000.population. 0.765
## X.SDG.9.1.2....Passenger.volumes..8..by.rail..Million.Passenger.kilometer.
## X.SDG.9.1.2....Freight.volumes..1..by.air..Thousands.tonnes.
## X.SDG.3.3.2....Tuberculosis.incidence.per.100.000.population..Number.per.100.000.population. 0.774
## X.SDG.7.3.1....Energy.intensity.measured.in.terms.of.primary.energy.and.GDP..TOE...thousand.2010.USD.
## X.SDG.12.a.1....Installed.renewable.energy.generating.capacity.in.developing.countries..in.Watts.per.capita.
## X.SDG.3.1.1....Maternal.mortality.ratio..Number.per.100.000.live.births.
## X.SDG.4.a.1....Proportion.of.primary.schools.with.access.to..c..computer.for.pedagogical.purposes....
## X.SDG.8.10.1....Number.of.automatic.teller.machines..ATMs..per.100.000.adults..Number.per.100.000.adults.
## X.SDG.4.1.2....Completion.rate.in.upper.secondary.education....
## X.SDG.17.1.2....Proportion.of.domestic.budget.funded.by.domestic.taxes....
## X.SDG.3.c.1....Density.of.nursing.and.midwifery.personnel..Number.per.10.000.population. -0.552
## X.SDG.6.1.1....Proportion.of.population.using.safely.managed.drinking.water.services....
## X.SDG.4.a.1....Proportion.of.primary.schools.with.access.to..a..electricity....
## X.SDG.3.2.1....Under.five.mortality.rate..Number.per.1.000.live.births.
## X.SDG.4.2.2....Participation.rate.in.organized.learning..one.year.before.the.official.primary.entry.age...both.sexes....
## X.SDG.7.2.1....Renewable.energy.share.in.the.total.final.energy.consumption....
## X.SDG.3.b.1....Proportion.of.the.target.population.covered.by.Measles.containing.vaccines..2nd.dose.....
## MR5
## X.SDG.9.1.2....Number.of.passengers..3..by.rail..Thousands.
## X.SDG.9.1.2....Freight.volumes..5..by.sea..Thousands.tonnes.
## X.SDG.9.1.2....Number.of.passengers..1..by.air..Thousands.
## X.SDG.9.1.2....Number.of.passengers..5..by.sea..Thousands.
## X.SDG.9.2.1....Manufacturing.value.added.per.capita..constant.2015.USD...in.USD.
## X.SDG.9.1.2....Freight.volumes..3..by.rail..Thousands.tonnes.
## X.SDG.8.5.1....Average.hourly.earnings.of.employees..Current.local.currency.
## X.SDG.1.5.1....Number.of.deaths..missing.persons.and.directly.affected.persons.attributed.to.climate.related.disasters.per.100.000.population..Number.per.100.000.population.
## X.SDG.9.1.2....Passenger.volumes..8..by.rail..Million.Passenger.kilometer.
## X.SDG.9.1.2....Freight.volumes..1..by.air..Thousands.tonnes.
## X.SDG.3.3.2....Tuberculosis.incidence.per.100.000.population..Number.per.100.000.population.
## X.SDG.7.3.1....Energy.intensity.measured.in.terms.of.primary.energy.and.GDP..TOE...thousand.2010.USD.
## X.SDG.12.a.1....Installed.renewable.energy.generating.capacity.in.developing.countries..in.Watts.per.capita.
## X.SDG.3.1.1....Maternal.mortality.ratio..Number.per.100.000.live.births.
## X.SDG.4.a.1....Proportion.of.primary.schools.with.access.to..c..computer.for.pedagogical.purposes....
## X.SDG.8.10.1....Number.of.automatic.teller.machines..ATMs..per.100.000.adults..Number.per.100.000.adults.
## X.SDG.4.1.2....Completion.rate.in.upper.secondary.education.... 0.604
## X.SDG.17.1.2....Proportion.of.domestic.budget.funded.by.domestic.taxes.... 0.651
## X.SDG.3.c.1....Density.of.nursing.and.midwifery.personnel..Number.per.10.000.population.
## X.SDG.6.1.1....Proportion.of.population.using.safely.managed.drinking.water.services....
## X.SDG.4.a.1....Proportion.of.primary.schools.with.access.to..a..electricity....
## X.SDG.3.2.1....Under.five.mortality.rate..Number.per.1.000.live.births.
## X.SDG.4.2.2....Participation.rate.in.organized.learning..one.year.before.the.official.primary.entry.age...both.sexes....
## X.SDG.7.2.1....Renewable.energy.share.in.the.total.final.energy.consumption....
## X.SDG.3.b.1....Proportion.of.the.target.population.covered.by.Measles.containing.vaccines..2nd.dose.....
##
## MR1 MR4 MR3 MR2 MR5
## SS loadings 5.698 3.950 3.029 2.686 1.809
## Proportion Var 0.228 0.158 0.121 0.107 0.072
## Cumulative Var 0.228 0.386 0.507 0.615 0.687Rotasi varimax digunakan untuk memperjelas interpretasi faktor.
Visualisasi Loading Faktor
load_matrix <- as.data.frame(unclass(fa_result$loadings))
load_matrix$Variable <- rownames(load_matrix)
load_long <- melt(load_matrix, id.vars = "Variable")
ggplot(load_long, aes(x = variable, y = reorder(Variable, desc(Variable)), fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0) +
theme_minimal() +
theme(
axis.text.y = element_text(size = 5),
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(size = 12)
) +
labs(
title = "Heatmap Factor Loadings",
x = "Faktor",
y = "Variabel"
)
Faktor 1 didominasi oleh indikator pendidikan dan kesehatan dasar, sehingga dapat diinterpretasikan sebagai dimensi pembangunan sosial.
Faktor 2 memiliki loading tinggi pada indikator manufaktur dan transportasi, sehingga merepresentasikan dimensi infrastruktur dan aktivitas ekonomi.
Faktor 3 berkaitan dengan indikator energi, menunjukkan dimensi efisiensi energi.
Faktor 4 mencerminkan risiko kesehatan dan kerentanan.
Faktor 5 berkaitan dengan kapasitas fiskal dan pendanaan domestik.
Kesimpulan
Berdasarkan hasil analisis:
- Komponen utama pertama (PC1) menjelaskan variasi terbesar dalam struktur indikator SDGs di ASEAN.
- Komponen kedua (PC2) menangkap variasi tambahan yang membedakan kelompok negara tertentu.
- Factor Analysis menunjukkan adanya beberapa faktor laten yang merepresentasikan dimensi pembangunan sosial, ekonomi, dan infrastruktur.
- Negara ASEAN memiliki karakteristik pembangunan yang heterogen, terlihat dari pemisahan pada score plot PCA.