PENERAPAN ALGORITMA K-MEANS UNTUK KLASTERISASI PROVINSI DI INDONESIA BERDASARKAN POLA ANGKA KELAHIRAN MENURUT KELOMPOK UMUR
DOI:
https://doi.org/10.54314/p2637h85Keywords:
ASFR, k-means, provincal clustering, fertility, SUPAS 2025Abstract
Abstract: Differences in fertility patterns across Indonesian provinces can be explored through age-specific fertility rates. This study clusters 38 provinces using seven Age-Specific Fertility Rate (ASFR) indicators for women aged 15–19 to 45–49, based on the 2025 Intercensal Population Survey (SUPAS) table published by Statistics Indonesia (BPS). The indicators represent births per 1,000 women in the corresponding age group. The workflow includes data checks, z-score standardization, K-Means clustering with 50 initializations for candidate values of k=2–7, and evaluation using inertia, silhouette coefficient, Davies–Bouldin index, and Calinski–Harabasz index. The k=2 solution achieved the highest silhouette score (0.295), resulting in clusters of 27 and 11 provinces. Centroid profiles indicate differences in ASFR distributions across age groups. However, the lowest Davies–Bouldin index occurred at k=6; therefore, k=2 is treated as a parsimonious primary solution rather than the only plausible partition. Findings are exploratory and do not establish causal relationships or rank provinces normatively. Further work should test cluster stability, compare alternative algorithms, and incorporate socio-demographic covariates.
Keyword: ASFR, K-Means, provincial clustering, fertility, SUPAS 2025.
Abstrak: Perbedaan pola fertilitas antarprovinsi dapat dipelajari melalui distribusi angka kelahiran menurut kelompok umur ibu. Penelitian ini bertujuan mengelompokkan 38 provinsi di Indonesia berdasarkan tujuh indikator Age-Specific Fertility Rate (ASFR) kelompok umur 15–19 hingga 45–49 tahun dari hasil Survei Penduduk Antar Sensus (SUPAS) 2025. Data bersumber dari tabel statistik Badan Pusat Statistik (BPS) dan dinyatakan sebagai jumlah kelahiran per 1.000 perempuan pada kelompok umur yang sama. Tahapan analisis meliputi pemeriksaan data, standardisasi z-score, penerapan K-Means dengan 50 inisialisasi untuk setiap kandidat jumlah klaster (k=2–7), serta evaluasi menggunakan inertia, silhouette coefficient, Davies–Bouldin index, dan Calinski–Harabasz index. Kandidat k=2 menghasilkan silhouette tertinggi sebesar 0.295 dan membentuk klaster beranggotakan 27 serta 11 provinsi. Profil centroid menunjukkan perbedaan pola ASFR lintas kelompok umur; klaster dengan nilai rata-rata lebih tinggi menonjol pada beberapa kelompok umur, terutama kelompok umur lebih tua. Namun, Davies–Bouldin index terendah diperoleh pada k=6, sehingga pemilihan k=2 diposisikan sebagai solusi utama yang lebih sederhana dan didukung silhouette, bukan sebagai satu-satunya struktur yang mungkin. Hasil ini bersifat eksploratif dan tidak menyiratkan hubungan sebab-akibat atau penilaian normatif terhadap provinsi. Penelitian lanjutan disarankan menguji kestabilan klaster, membandingkan algoritma alternatif, dan menggabungkan kovariat sosial-demografi.
Kata kunci: ASFR, K-Means, klasterisasi provinsi, fertilitas, SUPAS 2025.
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