G1401231094_Kamal Aldimas_Tugas TPG individu
2025-11-27
Klasterisasi Pola pergerakan Harga komoditas Pangan di Provinsi Banten menggunakan Dynamic Time Warping dan K-medoids
Input data
# Membaca file CSV
data <- read.csv("C:/Users/Kamal Aldimas/Downloads/0_df_long.csv")
# Menampilkan 6 baris pertama
head(data)## KabKot.Tahun.Bulan.Produk.Harga.Kategori.KodeBPS.KodeProv.NamaProv
## 1 Kab. Aceh Barat;2022;Januari;Beras Premium;11429;Beras;11.05;11;Aceh
## 2 Kab. Aceh Barat;2022;Januari;Beras Medium;9979;Beras;11.05;11;Aceh
## 3 Kab. Aceh Barat;2022;Januari;Bawang Merah;31000;Bawang;11.05;11;Aceh
## 4 Kab. Aceh Barat;2022;Januari;Bawang Putih (Bonggol);28636;Bawang;11.05;11;Aceh
## 5 Kab. Aceh Barat;2022;Januari;Cabai Merah Keriting;19409;Cabai;11.05;11;Aceh
## 6 Kab. Aceh Barat;2022;Januari;Cabai Rawit Merah;45706;Cabai;11.05;11;Acehlibrary(dplyr)## Warning: package 'dplyr' was built under R version 4.4.3##
## 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(tidyr)## Warning: package 'tidyr' was built under R version 4.4.3data2 <- data %>%
separate(
KabKot.Tahun.Bulan.Produk.Harga.Kategori.KodeBPS.KodeProv.NamaProv,
into = c("Kabkot", "Tahun", "Bulan", "Produk", "Harga", "Kategori", "KodeBPS", "KodeProv", "Provinsi"),
sep = ";"
)
head(data2)## Kabkot Tahun Bulan Produk Harga Kategori KodeBPS
## 1 Kab. Aceh Barat 2022 Januari Beras Premium 11429 Beras 11.05
## 2 Kab. Aceh Barat 2022 Januari Beras Medium 9979 Beras 11.05
## 3 Kab. Aceh Barat 2022 Januari Bawang Merah 31000 Bawang 11.05
## 4 Kab. Aceh Barat 2022 Januari Bawang Putih (Bonggol) 28636 Bawang 11.05
## 5 Kab. Aceh Barat 2022 Januari Cabai Merah Keriting 19409 Cabai 11.05
## 6 Kab. Aceh Barat 2022 Januari Cabai Rawit Merah 45706 Cabai 11.05
## KodeProv Provinsi
## 1 11 Aceh
## 2 11 Aceh
## 3 11 Aceh
## 4 11 Aceh
## 5 11 Aceh
## 6 11 AcehFilter Data Banten
data_banten <- data2 %>%
filter(Provinsi == "Banten")
head(data_banten)## Kabkot Tahun Bulan Produk Harga Kategori KodeBPS
## 1 Kab. Lebak 2022 Januari Beras Premium 10789 Beras 36.02
## 2 Kab. Lebak 2022 Januari Beras Medium 9158 Beras 36.02
## 3 Kab. Lebak 2022 Januari Bawang Merah 27684 Bawang 36.02
## 4 Kab. Lebak 2022 Januari Bawang Putih (Bonggol) 24684 Bawang 36.02
## 5 Kab. Lebak 2022 Januari Cabai Merah Keriting 34579 Cabai 36.02
## 6 Kab. Lebak 2022 Januari Cabai Rawit Merah 55263 Cabai 36.02
## KodeProv Provinsi
## 1 36 Banten
## 2 36 Banten
## 3 36 Banten
## 4 36 Banten
## 5 36 Banten
## 6 36 BantenEksplorasi data
melihat struktur data
summary(data_banten)## Kabkot Tahun Bulan Produk
## Length:5520 Length:5520 Length:5520 Length:5520
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## Harga Kategori KodeBPS KodeProv
## Length:5520 Length:5520 Length:5520 Length:5520
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## Provinsi
## Length:5520
## Class :character
## Mode :characterstr(data_banten)## 'data.frame': 5520 obs. of 9 variables:
## $ Kabkot : chr "Kab. Lebak" "Kab. Lebak" "Kab. Lebak" "Kab. Lebak" ...
## $ Tahun : chr "2022" "2022" "2022" "2022" ...
## $ Bulan : chr "Januari" "Januari" "Januari" "Januari" ...
## $ Produk : chr "Beras Premium" "Beras Medium" "Bawang Merah" "Bawang Putih (Bonggol)" ...
## $ Harga : chr "10789" "9158" "27684" "24684" ...
## $ Kategori: chr "Beras" "Beras" "Bawang" "Bawang" ...
## $ KodeBPS : chr "36.02" "36.02" "36.02" "36.02" ...
## $ KodeProv: chr "36" "36" "36" "36" ...
## $ Provinsi: chr "Banten" "Banten" "Banten" "Banten" ...data_banten$Harga <- as.numeric(data_banten$Harga)## Warning: NAs introduced by coercionsummary(data_banten$Harga)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4375 14721 27167 35961 40000 153421 749data diatas, tidak ditemukan missing value pada variabel harga
data_clean <- na.omit(data_banten)
head(data_clean)## Kabkot Tahun Bulan Produk Harga Kategori KodeBPS
## 1 Kab. Lebak 2022 Januari Beras Premium 10789 Beras 36.02
## 2 Kab. Lebak 2022 Januari Beras Medium 9158 Beras 36.02
## 3 Kab. Lebak 2022 Januari Bawang Merah 27684 Bawang 36.02
## 4 Kab. Lebak 2022 Januari Bawang Putih (Bonggol) 24684 Bawang 36.02
## 5 Kab. Lebak 2022 Januari Cabai Merah Keriting 34579 Cabai 36.02
## 6 Kab. Lebak 2022 Januari Cabai Rawit Merah 55263 Cabai 36.02
## KodeProv Provinsi
## 1 36 Banten
## 2 36 Banten
## 3 36 Banten
## 4 36 Banten
## 5 36 Banten
## 6 36 Bantenlength(data_clean$Kabkot)## [1] 4771Preprocessing data
data_clean$Harga <- as.numeric(data_clean$Harga)
# Konversi variabel kategorikal menjadi faktor
data_clean$Produk <- as.factor(data_clean$Produk)
data_clean$Kategori <- as.factor(data_clean$Kategori)
data_clean$Kabkot <- as.factor(data_clean$Kabkot)
data_clean$Provinsi <- as.factor(data_clean$Provinsi)
# Standarisasi data
# Normalisasi data Harga, Beras Premium dan Beras Medium
data_scaled <- scale(data_clean[, c("Harga")])head(data_scaled)## [,1]
## [1,] -0.76351175
## [2,] -0.81298217
## [3,] -0.25106367
## [4,] -0.34205771
## [5,] -0.04192904
## [6,] 0.58544450K-Meloids clustering
library(cluster)## Warning: package 'cluster' was built under R version 4.4.3library(factoextra)## Warning: package 'factoextra' was built under R version 4.4.3## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.4.3## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(clusterCrit)## Warning: package 'clusterCrit' was built under R version 4.4.3library(dplyr)
library(ggplot2)
library(tidyr)
library(scales)## Warning: package 'scales' was built under R version 4.4.3library(RColorBrewer)Elbow method
fviz_nbclust(data_scaled, kmeans, method = "wss") +
labs(title = "Elbow Method for Optimal k")
## Silhouette Method
fviz_nbclust(data_scaled, kmeans, method = "silhouette") +
labs(title = "Silhouette Method for Optimal k")
Berdasarkan hasil dari kedua metode penentuan jumlah cluster, diperoleh
bahwa k = 2 merupakan jumlah cluster yang paling optimal. Pada metode
Elbow, terlihat penurunan nilai WSS yang sangat tajam hingga k = 2,
kemudian grafik mulai mendatar setelahnya, sehingga mengindikasikan
bahwa penambahan cluster tidak lagi memberikan peningkatan yang
signifikan. Sementara itu, metode Silhouette menunjukkan bahwa nilai
rata-rata silhouette mencapai titik tertinggi pada k = 2, yang
menandakan bahwa pemisahan antar cluster pada jumlah tersebut berada
pada kualitas terbaik. Oleh karena itu, pemilihan k = 2 menjadi pilihan
yang paling tepat untuk digunakan dalam analisis clustering, baik untuk
K-Means maupun metode clustering lainnya.
Menghitung jarak antar data dan mejalankan K-medoids(PAM)
dist_euc <- dist(data_scaled, method = "euclidean")
dist_man <- dist(data_scaled, method = "manhattan")
dist_can <- dist(data_scaled, method = "canberra")
pam_euc <- pam(dist_euc, k = 2)
pam_man <- pam(dist_man, k = 2)
pam_can <- pam(dist_can, k = 2)Melihat komponen output PAM
names(pam_euc)## [1] "medoids" "id.med" "clustering" "objective" "isolation"
## [6] "clusinfo" "silinfo" "diss" "call"melihat cluster setiap observasi
head(pam_euc$clustering)## [1] 1 1 1 1 1 1disini terlihat bahwa 6 baris pertama ditetapkan ke cluster 1 ## Melihat meloid (data representatif setiap cluster)
pam_euc$medoids## [1] 655 4340model PAM dengan k=2 berhasil membentuk dua cluster, tetapi dengan ukurna yg tidak seimbang
Melihat ID meloid
pam_euc$id.med## [1] 655 4340Melihat objective function
pam_euc$objective## build swap
## 0.3777344 0.3742766Nilai objektif PAM menunjukkan penurunan dari 0.3777 (BUILD) menjadi 0.3743 (SWAP). Penurunan ini menandakan bahwa proses optimasi berhasil menemukan medoid yang lebih baik dengan total dissimilarity yang lebih rendah. Nilai SWAP menjadi acuan akhir karena merepresentasikan konfigurasi cluster paling optimal.
Ringkasan karakteristik cluster
head(pam_euc$clusinfo,10)## size max_diss av_diss diameter separation
## [1,] 4344 1.657092 0.3803746 2.254499 0.02147459
## [2,] 427 1.670378 0.3122399 2.244793 0.02147459“Cluster 1 berukuran jauh lebih besar dibandingkan Cluster 2. Dilihat dari nilai av_diss yang lebih kecil, Cluster 2 lebih kompak dan homogen. Namun, nilai separation yang rendah menunjukkan bahwa kedua cluster berada cukup dekat sehingga pemisahannya kurang kuat.”
Silhoutte
head(pam_euc$silinfo,10)## $widths
## cluster neighbor sil_width
## 108 1 2 0.884830750
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##
## $clus.avg.widths
## [1] 0.8234457 0.8141928
##
## $avg.width
## [1] 0.8226176Nilai silhouette rata-rata untuk kedua cluster adalah 0.8226, dengan cluster 1 = 0.8234 dan cluster 2 = 0.8142. Nilai di atas 0.8 menunjukkan bahwa kedua cluster memiliki kualitas yang sangat baik, kompak, dan terpisah jelas satu sama lain. Ini menandakan bahwa struktur clustering yang terbentuk sangat stabil dan efektif.
pam_euc$call## pam(x = dist_euc, k = 2)Evaluasi Clustering
eval_pam <- function(data_scaled, pam_model, dist_matrix) {
cl <- pam_model$clustering
sil <- mean(silhouette(cl, dist_matrix)[, 3])
ch <- intCriteria(traj = as.matrix(data_scaled),
part = cl, crit = "Calinski_Harabasz")$calinski_harabasz
dunn <- intCriteria(traj = as.matrix(data_scaled),
part = cl, crit = "Dunn")$dunn
return(data.frame(Silhouette = sil, CH = ch, Dunn = dunn))
}
eval_result <- rbind(
Euclidean = eval_pam(data_scaled, pam_euc, dist_euc),
Manhattan = eval_pam(data_scaled, pam_man, dist_man),
Canberra = eval_pam(data_scaled, pam_can, dist_can)
)
eval_result## Silhouette CH Dunn
## Euclidean 0.8226176 17322.296 9.525219e-03
## Manhattan 0.8226176 17322.296 9.525219e-03
## Canberra 0.5740247 4704.246 2.554083e-05Secara keseluruhan, jarak Euclidean dan Manhattan merupakan metode terbaik untuk clustering pada dataset ini karena mampu menghasilkan struktur cluster yang jauh lebih stabil dan jelas dibandingkan Canberra. Nilai Silhouette yang tinggi pada kedua jarak tersebut menunjukkan bahwa hasil clustering sangat baik dan dapat digunakan untuk interpretasi lebih lanjut.
eval_long <- eval_result %>%
mutate(Jarak = rownames(eval_result)) %>%
pivot_longer(cols = c(Silhouette, CH, Dunn),
names_to = "Metrik", values_to = "Nilai")
eval_long ## # A tibble: 9 × 3
## Jarak Metrik Nilai
## <chr> <chr> <dbl>
## 1 Euclidean Silhouette 0.823
## 2 Euclidean CH 17322.
## 3 Euclidean Dunn 0.00953
## 4 Manhattan Silhouette 0.823
## 5 Manhattan CH 17322.
## 6 Manhattan Dunn 0.00953
## 7 Canberra Silhouette 0.574
## 8 Canberra CH 4704.
## 9 Canberra Dunn 0.0000255Hasil evaluasi menunjukkan bahwa penggunaan jarak Euclidean dan Manhattan memberikan kualitas clustering yang paling baik. Hal ini terlihat dari nilai Silhouette yang tinggi (0.8226) dan nilai Calinski–Harabasz (CH) terbesar (17.322), yang menandakan bahwa cluster terbentuk dengan sangat kompak dan terpisah jelas. Nilai Dunn yang lebih tinggi pada kedua jarak ini juga menunjukkan pemisahan antar cluster yang relatif baik.
Sebaliknya, jarak Canberra menghasilkan kualitas clustering yang lebih rendah. Hal ini terlihat dari nilai Silhouette yang menurun menjadi 0.574, nilai CH yang jauh lebih kecil, serta nilai Dunn yang sangat rendah, mengindikasikan bahwa cluster yang terbentuk kurang stabil dan kurang terpisah.
Secara keseluruhan, metrik evaluasi menunjukkan bahwa jarak Euclidean dan Manhattan adalah pilihan optimal untuk PAM, karena menghasilkan pembentukan cluster yang paling baik dibandingkan metrik jarak lainnya.
ggplot(eval_long, aes(x = Jarak, y = Nilai, fill = Metrik)) +
geom_bar(stat = "identity", position = "dodge", width = 0.7) +
scale_fill_brewer(palette = "Set2") +
labs(title = "Perbandingan Metrik Evaluasi",
x = "Jenis Jarak",
y = "Nilai Metrik",
fill = "Metrik") +
theme_minimal(base_size = 12) +
theme(
plot.title = element_text(face = "bold", hjust = 0.5),
axis.text.x = element_text(face = "bold")
)
Grafik memperlihatkan bahwa metrik CH sangat unggul pada jarak Euclidean
dan Manhattan, menunjukkan kualitas cluster yang lebih baik. Sementara
itu, nilai Dunn dan Silhouette yang rendah mengindikasikan bahwa
pemisahan cluster belum optimal pada semua jarak. Secara keseluruhan,
jarak Euclidean dan Manhattan memberikan evaluasi clustering yang lebih
baik dibanding Canberra
ggplot(filter(eval_long, Metrik == "Silhouette"),
aes(x = Jarak, y = Nilai, fill = Jarak)) +
geom_bar(stat = "identity", width = 0.6, show.legend = FALSE) +
scale_fill_brewer(palette = "Set2") +
labs(title = "Perbandingan Nilai Silhouette",
x = "Jenis Jarak", y = "Nilai") +
theme_minimal(base_size = 12) +
theme(plot.title = element_text(face = "bold", hjust = 0.5))
Grafik menunjukkan bahwa jarak Euclidean dan Manhattan menghasilkan
nilai silhouette tertinggi, yaitu sekitar 0.82, menandakan kualitas
clustering yang sangat baik dan pemisahan cluster yang jelas. Sementara
itu, jarak Canberra memiliki nilai silhouette lebih rendah (≈0.58),
sehingga menghasilkan struktur cluster yang kurang optimal dibanding dua
jarak lainnya. Dengan demikian, Euclidean dan Manhattan merupakan
pilihan jarak terbaik untuk model clustering pada dataset ini.
ggplot(filter(eval_long, Metrik == "CH"),
aes(x = Jarak, y = Nilai, fill = Jarak)) +
geom_bar(stat = "identity", width = 0.6, show.legend = FALSE) +
scale_fill_brewer(palette = "Set3") +
labs(title = "Perbandingan Nilai Calinski-Harabasz",
x = "Jenis Jarak", y = "Nilai") +
theme_minimal(base_size = 12) +
theme(plot.title = element_text(face = "bold", hjust = 0.5))
Grafik menunjukkan bahwa jarak Euclidean dan Manhattan memiliki nilai
Calinski–Harabasz (CH) yang jauh lebih tinggi dibandingkan Canberra.
Nilai CH yang besar menandakan pemisahan antar-cluster yang lebih baik
dan struktur cluster yang lebih jelas. Dengan demikian, Euclidean dan
Manhattan memberikan kualitas clustering yang lebih optimal, sedangkan
Canberra menghasilkan pemisahan cluster yang jauh lebih lemah.
ggplot(filter(eval_long, Metrik == "Dunn"),
aes(x = Jarak, y = Nilai, fill = Jarak)) +
geom_bar(stat = "identity", width = 0.6, show.legend = FALSE) +
scale_fill_brewer(palette = "Pastel1") +
labs(title = "Perbandingan Nilai Dunn Index",
x = "Jenis Jarak", y = "Nilai") +
theme_minimal(base_size = 12) +
theme(plot.title = element_text(face = "bold", hjust = 0.5))
“Nilai Dunn Index pada jarak Euclidean dan Manhattan berada pada kisaran
0.0095, yang lebih baik dibandingkan jarak Canberra yang hampir
mendekati nol. Hal ini menunjukkan bahwa cluster pada Euclidean dan
Manhattan lebih kompak dan lebih terpisah dibandingkan Canberra.”
Hasil Clustering
table(pam_euc$cluster)##
## 1 2
## 4344 427table(pam_man$cluster)##
## 1 2
## 4344 427table(pam_can$cluster)##
## 1 2
## 3135 1636Model PAM terbaik adalah menggunakan jarak Euclidean atau Manhattan karena menghasilkan cluster paling kompak dan terpisah jelas.
DTW (dynamic time warping)
buat variabel tangga;
bulan_id <- c("Januari","Februari","Maret","April","Mei","Juni",
"Juli","Agustus","September","Oktober","November","Desember")
df <- data_clean %>%
mutate(
Bulan_lc = tolower(Bulan),
bulan_match = match(Bulan_lc, tolower(bulan_id)),
month_num = bulan_match,
ym = as.Date(paste0(Tahun, "-", sprintf("%02d", month_num), "-01"))
)bentuk ID series untuk setiap komoditas per kabupaten
df <- df %>%
mutate(series_id = paste(Kabkot, Produk, sep = "_"))Ambil satu nilai harga per bulan per series
dfm <- df %>%
group_by(series_id, ym) %>%
summarise(Harga = mean(Harga), .groups = "drop")Pivot Long untuk bentuk wide
library(reshape2)## Warning: package 'reshape2' was built under R version 4.4.3##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smithswide <- dcast(dfm, series_id ~ ym, value.var = "Harga")Buat matrix numeric & imputasi NA
series_names <- wide$series_id
mat <- as.matrix(wide[ , -1])
rownames(mat) <- series_nameslibrary(zoo)## Warning: package 'zoo' was built under R version 4.4.3##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericimpute_row <- function(x){
x2 <- na.approx(x, na.rm = FALSE)
x2 <- na.locf(x2, na.rm = FALSE)
x2 <- na.locf(x2, fromLast = TRUE, na.rm = FALSE)
return(x2)
}
mat_imp <- t(apply(mat, 1, impute_row))Normalisasi ( z-score per series)
zscore <- function(x){
(x - mean(x))/sd(x)
}
mat_z <- t(apply(mat_imp, 1, zscore))Buat ts_list
ts_list <- apply(mat_z, 1, as.numeric)
names(ts_list) <- series_names# CARI series yang valid (minimal 2 nilai non-NA)
valid_idx <- apply(mat_z, 1, function(x) sum(!is.na(x)) >= 2)
cat("Series total :", nrow(mat_z), "\n")## Series total : 120cat("Series valid :", sum(valid_idx), "\n")## Series valid : 120cat("Series dibuang (karena NA terlalu banyak):", sum(!valid_idx), "\n")## Series dibuang (karena NA terlalu banyak): 0# FILTER hanya series yang valid
mat_z_clean <- mat_z[valid_idx, , drop = FALSE]
series_names_clean <- rownames(mat_z_clean)ts_list <- lapply(seq_len(nrow(mat_z_clean)),
function(i) as.numeric(mat_z_clean[i, ]))
names(ts_list) <- series_names_cleansum(is.na(unlist(ts_list)))## [1] 0downsample_ts <- function(x, target = 24){
if(sum(!is.na(x)) < 2){
return(rep(NA, target))
}
approx(x = seq_along(x), y = x, n = target)$y
}
ts_list_ds <- lapply(ts_list, downsample_ts)sum(is.na(unlist(ts_list_ds)))## [1] 0valid_ds_idx <- sapply(ts_list_ds, function(x) sum(!is.na(x)) == length(x))
ts_list_ds <- ts_list_ds[valid_ds_idx]Hitung matriks jarak
library(dtw)## Warning: package 'dtw' was built under R version 4.4.3## Loading required package: proxy## Warning: package 'proxy' was built under R version 4.4.2##
## Attaching package: 'proxy'## The following objects are masked from 'package:stats':
##
## as.dist, dist## The following object is masked from 'package:base':
##
## as.matrix## Loaded dtw v1.23-1. See ?dtw for help, citation("dtw") for use in publication.n <- length(ts_list_ds)
L <- length(ts_list_ds[[1]])
window_size <- round(0.1 * L)
dtw_dist <- function(a, b){
dtw(a, b,
distance.only = TRUE,
window.type = "sakoechiba",
window.size = window_size)$normalizedDistance
}
dist_mat <- matrix(0, n, n)
rownames(dist_mat) <- colnames(dist_mat) <- names(ts_list_ds)
for(i in 1:(n-1)){
for(j in (i+1):n){
d <- dtw_dist(ts_list_ds[[i]], ts_list_ds[[j]])
dist_mat[i,j] <- d
dist_mat[j,i] <- d
}
}
dist_obj <- as.dist(dist_mat)Jalankan K-medois(PAM)
library(cluster)
k <- 2
pam_res <- pam(dist_obj, k = k, diss = TRUE)
cluster_pam <- pam_res$clusteringVisualisasi Cluster Hasil DTW
library(ggplot2)
df_plot <- data.frame()
for(i in 1:length(ts_list)){
df_plot <- rbind(df_plot,
data.frame(
series = names(ts_list)[i],
cluster = cluster_pam[i],
time = 1:length(ts_list[[i]]),
value = ts_list[[i]]
))
}## Warning in data.frame(series = names(ts_list)[i], cluster = cluster_pam[i], :
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## row names were found from a short variable and have been discarded
## Warning in data.frame(series = names(ts_list)[i], cluster = cluster_pam[i], :
## row names were found from a short variable and have been discarded
## Warning in data.frame(series = names(ts_list)[i], cluster = cluster_pam[i], :
## row names were found from a short variable and have been discarded
## Warning in data.frame(series = names(ts_list)[i], cluster = cluster_pam[i], :
## row names were found from a short variable and have been discarded
## Warning in data.frame(series = names(ts_list)[i], cluster = cluster_pam[i], :
## row names were found from a short variable and have been discarded
## Warning in data.frame(series = names(ts_list)[i], cluster = cluster_pam[i], :
## row names were found from a short variable and have been discarded
## Warning in data.frame(series = names(ts_list)[i], cluster = cluster_pam[i], :
## row names were found from a short variable and have been discarded
## Warning in data.frame(series = names(ts_list)[i], cluster = cluster_pam[i], :
## row names were found from a short variable and have been discarded
## Warning in data.frame(series = names(ts_list)[i], cluster = cluster_pam[i], :
## row names were found from a short variable and have been discardedggplot(df_plot, aes(time, value, group = series)) +
geom_line(alpha = 0.4) +
facet_wrap(~cluster, scales = "free_y") +
theme_minimal() +
labs(title = "Hasil Klasterisasi DTW",
x = "Time Index",
y = "Z-score Harga")
“Dari hasil klasterisasi DTW dengan k=2, diperoleh dua kelompok utama pola harga pangan. Cluster 1 menunjukkan pola sangat fluktuatif dengan volatilitas tinggi, banyak spike, dan pergerakan tidak stabil. Cluster 2 menunjukkan pola yang lebih stabil, bergerak di sekitar nilai rata-rata, dengan amplitudo perubahan yang kecil. Hal ini mengindikasikan bahwa komoditas hortikultura cenderung masuk ke cluster volatil, sementara komoditas pokok seperti beras masuk ke cluster stabil.”
Kesimpulan
data_clean <- data_clean %>%
mutate(series_id = paste(Kabkot, Produk, sep = "_"))
cluster_euc <- pam_euc$clustering
data_clean$cluster_euc <- cluster_euc
Mode <- function(x) {
ux <- unique(x)
ux[which.max(tabulate(match(x, ux)))]
}
cluster_euc_series <- data_clean %>%
group_by(series_id) %>%
summarise(cluster_euc = Mode(cluster_euc))
cluster_dtw <- cluster_pam # hasil PAM dari dist_obj
df_compare <- data.frame(
series_id = names(cluster_dtw),
cluster_dtw = cluster_dtw
) %>%
left_join(cluster_euc_series, by = "series_id")
df_compare <- df_compare %>%
mutate(Status = ifelse(cluster_dtw == cluster_euc,
"SAMA", "BERBEDA"))
df_compare## series_id cluster_dtw cluster_euc
## 1 Kab. Lebak_Bawang Merah 1 1
## 2 Kab. Lebak_Bawang Putih (Bonggol) 2 1
## 3 Kab. Lebak_Beras Medium 2 1
## 4 Kab. Lebak_Beras Premium 2 1
## 5 Kab. Lebak_Cabai Merah Besar 1 1
## 6 Kab. Lebak_Cabai Merah Keriting 1 1
## 7 Kab. Lebak_Cabai Rawit Merah 1 1
## 8 Kab. Lebak_Daging Ayam Ras 2 1
## 9 Kab. Lebak_Daging Sapi Murni 1 2
## 10 Kab. Lebak_Gula Konsumsi 2 1
## 11 Kab. Lebak_Jagung Tk. Peternak 2 1
## 12 Kab. Lebak_Minyak Goreng Curah 1 1
## 13 Kab. Lebak_Minyak Goreng Kemasan 1 1
## 14 Kab. Lebak_Minyakita 1 1
## 15 Kab. Lebak_Telur Ayam Ras 1 1
## 16 Kab. Pandeglang_Bawang Merah 1 1
## 17 Kab. Pandeglang_Bawang Putih (Bonggol) 2 1
## 18 Kab. Pandeglang_Beras Medium 2 1
## 19 Kab. Pandeglang_Beras Premium 2 1
## 20 Kab. Pandeglang_Cabai Merah Besar 1 1
## 21 Kab. Pandeglang_Cabai Merah Keriting 1 1
## 22 Kab. Pandeglang_Cabai Rawit Merah 1 1
## 23 Kab. Pandeglang_Daging Ayam Ras 2 1
## 24 Kab. Pandeglang_Daging Sapi Murni 1 2
## 25 Kab. Pandeglang_Gula Konsumsi 2 1
## 26 Kab. Pandeglang_Jagung Tk. Peternak 2 1
## 27 Kab. Pandeglang_Minyak Goreng Curah 1 1
## 28 Kab. Pandeglang_Minyak Goreng Kemasan 1 1
## 29 Kab. Pandeglang_Minyakita 1 1
## 30 Kab. Pandeglang_Telur Ayam Ras 1 1
## 31 Kab. Serang_Bawang Merah 1 1
## 32 Kab. Serang_Bawang Putih (Bonggol) 2 1
## 33 Kab. Serang_Beras Medium 2 1
## 34 Kab. Serang_Beras Premium 2 1
## 35 Kab. Serang_Cabai Merah Besar 1 1
## 36 Kab. Serang_Cabai Merah Keriting 1 1
## 37 Kab. Serang_Cabai Rawit Merah 1 1
## 38 Kab. Serang_Daging Ayam Ras 1 1
## 39 Kab. Serang_Daging Sapi Murni 1 2
## 40 Kab. Serang_Gula Konsumsi 2 1
## 41 Kab. Serang_Jagung Tk. Peternak 2 1
## 42 Kab. Serang_Minyak Goreng Curah 1 1
## 43 Kab. Serang_Minyak Goreng Kemasan 1 1
## 44 Kab. Serang_Minyakita 1 1
## 45 Kab. Serang_Telur Ayam Ras 1 1
## 46 Kab. Tangerang_Bawang Merah 1 1
## 47 Kab. Tangerang_Bawang Putih (Bonggol) 2 1
## 48 Kab. Tangerang_Beras Medium 2 1
## 49 Kab. Tangerang_Beras Premium 2 1
## 50 Kab. Tangerang_Cabai Merah Besar 1 1
## 51 Kab. Tangerang_Cabai Merah Keriting 1 1
## 52 Kab. Tangerang_Cabai Rawit Merah 1 1
## 53 Kab. Tangerang_Daging Ayam Ras 1 1
## 54 Kab. Tangerang_Daging Sapi Murni 1 2
## 55 Kab. Tangerang_Gula Konsumsi 2 1
## 56 Kab. Tangerang_Jagung Tk. Peternak 2 1
## 57 Kab. Tangerang_Minyak Goreng Curah 1 1
## 58 Kab. Tangerang_Minyak Goreng Kemasan 1 1
## 59 Kab. Tangerang_Minyakita 1 1
## 60 Kab. Tangerang_Telur Ayam Ras 2 1
## 61 Kota Cilegon_Bawang Merah 1 1
## 62 Kota Cilegon_Bawang Putih (Bonggol) 2 1
## 63 Kota Cilegon_Beras Medium 2 1
## 64 Kota Cilegon_Beras Premium 2 1
## 65 Kota Cilegon_Cabai Merah Besar 1 1
## 66 Kota Cilegon_Cabai Merah Keriting 1 1
## 67 Kota Cilegon_Cabai Rawit Merah 1 1
## 68 Kota Cilegon_Daging Ayam Ras 1 1
## 69 Kota Cilegon_Daging Sapi Murni 1 2
## 70 Kota Cilegon_Gula Konsumsi 2 1
## 71 Kota Cilegon_Jagung Tk. Peternak 2 1
## 72 Kota Cilegon_Minyak Goreng Curah 2 1
## 73 Kota Cilegon_Minyak Goreng Kemasan 1 1
## 74 Kota Cilegon_Minyakita 1 1
## 75 Kota Cilegon_Telur Ayam Ras 1 1
## 76 Kota Serang_Bawang Merah 1 1
## 77 Kota Serang_Bawang Putih (Bonggol) 2 1
## 78 Kota Serang_Beras Medium 2 1
## 79 Kota Serang_Beras Premium 2 1
## 80 Kota Serang_Cabai Merah Besar 1 1
## 81 Kota Serang_Cabai Merah Keriting 1 1
## 82 Kota Serang_Cabai Rawit Merah 1 1
## 83 Kota Serang_Daging Ayam Ras 1 1
## 84 Kota Serang_Daging Sapi Murni 1 2
## 85 Kota Serang_Gula Konsumsi 2 1
## 86 Kota Serang_Jagung Tk. Peternak 1 1
## 87 Kota Serang_Minyak Goreng Curah 2 1
## 88 Kota Serang_Minyak Goreng Kemasan 1 1
## 89 Kota Serang_Minyakita 1 1
## 90 Kota Serang_Telur Ayam Ras 1 1
## 91 Kota Tangerang Selatan_Bawang Merah 1 1
## 92 Kota Tangerang Selatan_Bawang Putih (Bonggol) 2 1
## 93 Kota Tangerang Selatan_Beras Medium 2 1
## 94 Kota Tangerang Selatan_Beras Premium 2 1
## 95 Kota Tangerang Selatan_Cabai Merah Besar 1 1
## 96 Kota Tangerang Selatan_Cabai Merah Keriting 1 1
## 97 Kota Tangerang Selatan_Cabai Rawit Merah 1 1
## 98 Kota Tangerang Selatan_Daging Ayam Ras 1 1
## 99 Kota Tangerang Selatan_Daging Sapi Murni 1 2
## 100 Kota Tangerang Selatan_Gula Konsumsi 2 1
## 101 Kota Tangerang Selatan_Jagung Tk. Peternak 2 1
## 102 Kota Tangerang Selatan_Minyak Goreng Curah 1 1
## 103 Kota Tangerang Selatan_Minyak Goreng Kemasan 1 1
## 104 Kota Tangerang Selatan_Minyakita 1 1
## 105 Kota Tangerang Selatan_Telur Ayam Ras 1 1
## 106 Kota Tangerang_Bawang Merah 1 1
## 107 Kota Tangerang_Bawang Putih (Bonggol) 2 1
## 108 Kota Tangerang_Beras Medium 2 1
## 109 Kota Tangerang_Beras Premium 2 1
## 110 Kota Tangerang_Cabai Merah Besar 1 1
## 111 Kota Tangerang_Cabai Merah Keriting 1 1
## 112 Kota Tangerang_Cabai Rawit Merah 1 1
## 113 Kota Tangerang_Daging Ayam Ras 1 1
## 114 Kota Tangerang_Daging Sapi Murni 1 2
## 115 Kota Tangerang_Gula Konsumsi 2 1
## 116 Kota Tangerang_Jagung Tk. Peternak 2 1
## 117 Kota Tangerang_Minyak Goreng Curah 2 1
## 118 Kota Tangerang_Minyak Goreng Kemasan 1 1
## 119 Kota Tangerang_Minyakita 1 1
## 120 Kota Tangerang_Telur Ayam Ras 1 1
## Status
## 1 SAMA
## 2 BERBEDA
## 3 BERBEDA
## 4 BERBEDA
## 5 SAMA
## 6 SAMA
## 7 SAMA
## 8 BERBEDA
## 9 BERBEDA
## 10 BERBEDA
## 11 BERBEDA
## 12 SAMA
## 13 SAMA
## 14 SAMA
## 15 SAMA
## 16 SAMA
## 17 BERBEDA
## 18 BERBEDA
## 19 BERBEDA
## 20 SAMA
## 21 SAMA
## 22 SAMA
## 23 BERBEDA
## 24 BERBEDA
## 25 BERBEDA
## 26 BERBEDA
## 27 SAMA
## 28 SAMA
## 29 SAMA
## 30 SAMA
## 31 SAMA
## 32 BERBEDA
## 33 BERBEDA
## 34 BERBEDA
## 35 SAMA
## 36 SAMA
## 37 SAMA
## 38 SAMA
## 39 BERBEDA
## 40 BERBEDA
## 41 BERBEDA
## 42 SAMA
## 43 SAMA
## 44 SAMA
## 45 SAMA
## 46 SAMA
## 47 BERBEDA
## 48 BERBEDA
## 49 BERBEDA
## 50 SAMA
## 51 SAMA
## 52 SAMA
## 53 SAMA
## 54 BERBEDA
## 55 BERBEDA
## 56 BERBEDA
## 57 SAMA
## 58 SAMA
## 59 SAMA
## 60 BERBEDA
## 61 SAMA
## 62 BERBEDA
## 63 BERBEDA
## 64 BERBEDA
## 65 SAMA
## 66 SAMA
## 67 SAMA
## 68 SAMA
## 69 BERBEDA
## 70 BERBEDA
## 71 BERBEDA
## 72 BERBEDA
## 73 SAMA
## 74 SAMA
## 75 SAMA
## 76 SAMA
## 77 BERBEDA
## 78 BERBEDA
## 79 BERBEDA
## 80 SAMA
## 81 SAMA
## 82 SAMA
## 83 SAMA
## 84 BERBEDA
## 85 BERBEDA
## 86 SAMA
## 87 BERBEDA
## 88 SAMA
## 89 SAMA
## 90 SAMA
## 91 SAMA
## 92 BERBEDA
## 93 BERBEDA
## 94 BERBEDA
## 95 SAMA
## 96 SAMA
## 97 SAMA
## 98 SAMA
## 99 BERBEDA
## 100 BERBEDA
## 101 BERBEDA
## 102 SAMA
## 103 SAMA
## 104 SAMA
## 105 SAMA
## 106 SAMA
## 107 BERBEDA
## 108 BERBEDA
## 109 BERBEDA
## 110 SAMA
## 111 SAMA
## 112 SAMA
## 113 SAMA
## 114 BERBEDA
## 115 BERBEDA
## 116 BERBEDA
## 117 BERBEDA
## 118 SAMA
## 119 SAMA
## 120 SAMAtable(df_compare$Status)##
## BERBEDA SAMA
## 53 67library(ggplot2)
ggplot(df_compare, aes(x = Status, fill = Status)) +
geom_bar(width = 0.6) +
scale_fill_brewer(palette = "Set2") +
labs(title = "Perbandingan Cluster: DTW vs Euclidean",
x = "Kesesuaian Cluster",
y = "Jumlah Series") +
theme_minimal()
“Grafik ini menunjukkan bahwa terdapat 67 series yang memiliki hasil cluster yang sama antara metode DTW dan Euclidean, sedangkan 53 series memiliki hasil cluster yang berbeda. Artinya, hampir separuh komoditas memiliki pola pergerakan harga yang tidak dapat ditangkap oleh jarak Euclidean. Ini menegaskan bahwa DTW lebih sensitif dalam mengenali pola harga dari waktu ke waktu.”
Sebaliknya, DTW lebih peka terhadap pola fluktuasi dan pergeseran waktu, sehingga memberikan pengelompokan yang lebih sesuai untuk analisis dinamika harga pangan.
Metode K-Medoids Euclidean dan Manhattan membentuk cluster berdasarkan level harga, bukan dinamika waktu.
Metode DTW + K-Medoids membentuk cluster berdasarkan pola pergerakan harga, sehingga mampu mengidentifikasi komoditas volatil dan stabil.
Dari 120 series, 53 series (44.2%) berbeda cluster antara kedua metode. Artinya metode berbasis jarak Euclidean tidak dapat menangkap pola waktu.
DTW lebih sensitif terhadap perubahan musiman, fluktuasi ekstrem, dan shifting antar kabupaten.
Dengan demikian, DTW + K-Medoids adalah metode yang paling sesuai untuk menganalisis pola pergerakan harga pangan di Provinsi Banten.