UAS TPG
Fathoni Sabri
2025-12-11
library
library(dplyr)##
## 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(ggplot2)
library(readxl)
library(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(FactoMineR)
library(ggfortify)
library(ca)input Data
library(readxl)
data_uas <- read_xlsx("C:/Users/gamal/OneDrive/Documents/Fathoni/TPG/DATA UAS 2025.xlsx")## Warning: Coercing text to numeric in E6743 / R6743C5: '95'## Warning: Coercing text to numeric in F6743 / R6743C6: '91'## Warning: Coercing text to numeric in G6743 / R6743C7: '90'## Warning: Coercing text to numeric in H6743 / R6743C8: '69'## Warning: Coercing text to numeric in I6743 / R6743C9: '79'## Warning: Coercing text to numeric in J6743 / R6743C10: '87'## Warning: Coercing text to numeric in K6743 / R6743C11: '92'## Warning: Coercing text to numeric in L6743 / R6743C12: '97'head(data_uas)## # A tibble: 6 × 13
## No status `Kabupaten / Kota` jenjang standar_isi standar_proses
## <dbl> <chr> <chr> <chr> <dbl> <dbl>
## 1 1 Swasta KABUPATEN BOGOR SMA 95 94
## 2 2 Swasta KABUPATEN BOGOR SMK 95 88
## 3 3 Swasta KABUPATEN BOGOR SMK 97 100
## 4 4 Swasta KABUPATEN BOGOR SMK 91 95
## 5 5 Swasta KABUPATEN BOGOR SMK 95 97
## 6 6 Swasta KABUPATEN BOGOR SMK 97 91
## # ℹ 7 more variables: standar_kompetensi_lulusan <dbl>, standar_ptk <dbl>,
## # standar_sarpras <dbl>, standar_pengelolaan <dbl>, standar_pembiyaan <dbl>,
## # standar_penilaian <dbl>, peringkat <chr>data_uas## # A tibble: 6,742 × 13
## No status `Kabupaten / Kota` jenjang standar_isi standar_proses
## <dbl> <chr> <chr> <chr> <dbl> <dbl>
## 1 1 Swasta KABUPATEN BOGOR SMA 95 94
## 2 2 Swasta KABUPATEN BOGOR SMK 95 88
## 3 3 Swasta KABUPATEN BOGOR SMK 97 100
## 4 4 Swasta KABUPATEN BOGOR SMK 91 95
## 5 5 Swasta KABUPATEN BOGOR SMK 95 97
## 6 6 Swasta KABUPATEN BOGOR SMK 97 91
## 7 7 Swasta KABUPATEN BOGOR SMK 91 90
## 8 8 Swasta KABUPATEN BOGOR SMK 100 95
## 9 9 Swasta KABUPATEN BOGOR SMA 90 93
## 10 10 Swasta KABUPATEN SUKABUMI SMK 94 97
## # ℹ 6,732 more rows
## # ℹ 7 more variables: standar_kompetensi_lulusan <dbl>, standar_ptk <dbl>,
## # standar_sarpras <dbl>, standar_pengelolaan <dbl>, standar_pembiyaan <dbl>,
## # standar_penilaian <dbl>, peringkat <chr>1. Analisis Gerombol - Metode Ward
Rata-rata tiap standar
data_sma <- data_uas %>%
filter(jenjang == "SMA") %>%
group_by(`Kabupaten / Kota`) %>%
summarise(
rata2_isi = mean(standar_isi, na.rm = TRUE),
rata2_proses = mean(standar_proses, na.rm = TRUE),
rata2_kompetensi = mean(standar_kompetensi_lulusan, na.rm = TRUE),
rata2_ptk = mean(standar_ptk, na.rm = TRUE),
rata2_sarpras = mean(standar_sarpras, na.rm = TRUE),
rata2_pengelolaan = mean(standar_pengelolaan, na.rm = TRUE),
rata2_pembiyaan = mean(standar_pembiyaan, na.rm = TRUE),
rata2_penilaian = mean(standar_penilaian, na.rm = TRUE)
)
row.names(data_sma) <- data_sma$`Kabupaten / Kota`## Warning: Setting row names on a tibble is deprecated.data_sma_num <- scale(data_sma[, -1])K-Means
fviz_nbclust(data_sma_num, FUNcluster = kmeans, method = "silhouette")
Berdasarkan kriteria koefisien silhouette, maka k-means yang dpilih
adalah 2
Dendogram
dist_sma <- dist(data_sma_num, method = "euclidean")
hc_sma <- hclust(dist_sma, method = "ward.D2")
fviz_dend(hc_sma, k = 2, rect = TRUE, cex = 0.7)## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## ℹ The deprecated feature was likely used in the factoextra package.
## Please report the issue at <https://github.com/kassambara/factoextra/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the factoextra package.
## Please report the issue at <https://github.com/kassambara/factoextra/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## ℹ The deprecated feature was likely used in the factoextra package.
## Please report the issue at <https://github.com/kassambara/factoextra/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Hierarchical Clustering
hc_sma <- eclust(data_sma_num,stand = TRUE,FUNcluster = "hclust", k=2, hc_method = "ward.D2", hc_metric = "euclidean")
hc_sma$cluster## [1] 1 1 2 2 2 2 2 1 1 2 2 2 2 2 1 2 2 1 2 2 2 1 2 2 1 1Interpretasi gerombol yang terbentuk
aggregate(data_sma_num,by =list(gerombol=hc_sma$cluster), FUN = mean)## gerombol rata2_isi rata2_proses rata2_kompetensi rata2_ptk rata2_sarpras
## 1 1 -0.8984578 -1.0837038 -0.8751457 -0.8585306 -1.0621854
## 2 2 0.4756541 0.5737256 0.4633125 0.4545162 0.5623335
## rata2_pengelolaan rata2_pembiyaan rata2_penilaian
## 1 -1.0660022 -0.7006402 -0.8092117
## 2 0.5643541 0.3709272 0.4284062fviz_cluster(hc_sma)
pca_sma <- prcomp(data_sma_num)
pca_sma$rotation## PC1 PC2 PC3 PC4 PC5
## rata2_isi 0.3664070 0.30535691 0.23549816 -0.20498546 0.6664767
## rata2_proses 0.3942345 0.22773432 -0.11982057 -0.22477326 -0.3429716
## rata2_kompetensi 0.3672687 0.11281312 -0.29784139 0.75828387 -0.1321272
## rata2_ptk 0.2660299 -0.70927631 0.20996149 0.04769952 -0.1552069
## rata2_sarpras 0.3517402 -0.49354829 0.06708462 -0.18576402 0.1477664
## rata2_pengelolaan 0.4120520 0.01545753 -0.04694348 0.26010607 0.3419497
## rata2_pembiyaan 0.3459525 0.06173264 -0.56832194 -0.47732054 -0.2079148
## rata2_penilaian 0.3024495 0.30241493 0.68388337 -0.01378165 -0.4632919
## PC6 PC7 PC8
## rata2_isi 0.41154515 0.09833015 -0.227585696
## rata2_proses -0.15138816 0.76311587 0.070302728
## rata2_kompetensi 0.14901802 -0.04127811 -0.383816553
## rata2_ptk 0.53548547 0.13709008 0.223983439
## rata2_sarpras -0.56957175 -0.07995892 -0.490958243
## rata2_pengelolaan -0.35293786 -0.12983087 0.708353399
## rata2_pembiyaan 0.21577706 -0.48262744 0.054439837
## rata2_penilaian -0.05678296 -0.36236591 -0.003212464Pengelompokkan dengan menggunakan koefisien k-means sebesar 2, menghasilkan 2 gerombol utama yang terpisah cukup jelas. Di mana gerombol 1 memiliki nilai negatif di seluruh rata-rata standarnya, sedangkan gerombol 2 memiliki nilai positif di seluruh rata-rata standarnya. Yang mengartikan, SMA terpisah menjadi 2 gerombol berdasarkan rata-rata seluruh standarnya.
2. Analisis Biplot
data_smp <- data_uas %>%
filter(jenjang == "SMP") %>%
group_by(`Kabupaten / Kota`) %>%
summarise(
isi = mean(standar_isi, na.rm = TRUE),
proses = mean(standar_proses, na.rm = TRUE),
kl = mean(standar_kompetensi_lulusan, na.rm = TRUE),
ptk = mean(standar_ptk, na.rm = TRUE),
sarpras = mean(standar_sarpras, na.rm = TRUE),
pengelolaan = mean(standar_pengelolaan, na.rm = TRUE),
pembiyaan = mean(standar_pembiyaan, na.rm = TRUE),
penilaian = mean(standar_penilaian, na.rm = TRUE)
)row.names(data_smp) <- data_smp$`Kabupaten / Kota`## Warning: Setting row names on a tibble is deprecated.pca_smp <- prcomp(data_smp[, -1], scale. = TRUE)
fviz_pca_biplot(pca_smp, repel = TRUE)
) Kab/kota 20, 22, 19, 13 berada jauh di kanan yang berarti memiliki
standar tinggi, terutama pembiayaan, pengelolaan, penilaian, dan
kompetensi lulusan ) Titik di sisi kiri (misalnya 6, 2, 4, 16, 18,
8) memiliki nilai standar lebih rendah dan konsisten di hampir semua
standar. *) Variabel pembiayaan, kompetensi lulusan, pengelolaan, dan
penilaian memiliki panah paling panjang yang berarti berkontribusi
paling kuat terhadap perbedaan antar daerah
3. Analisis Korespondensi
tab_ca <- table(data_uas$jenjang, data_uas$peringkat)
ca_res <- CA(tab_ca, graph = FALSE)
fviz_ca_biplot(ca_res, repel = TRUE) 
) SMA dan SMP cenderung memiliki akreditasi A ) SD cenderung
memiliki akredtasi B *) Sementara SMK memilki akreditasi C