Comparison of K-Means and DBSCAN Algorithms in Regional Segmentation in Indonesia Based on Mortality and Fertility Indicators
DOI:
https://doi.org/10.24252/msa.v14i1.61961Keywords:
Clustering, DBSCAN, Fertility, K-Means, MortalityAbstract
Mortality and fertility indicators can be interpreted through maternal mortality rates, infant mortality rates, under-five mortality rates, child mortality rates, and crude birth rates. Disparities in mortality and fertility between regions indicate differences in health quality. In Indonesia, the maternal mortality rate has declined over the past decade, from 346 to 189 per 100000 live births, but this figure has not yet reached the global target set by the SDGs of 70 per 100000 live births. To reduce these mortality indicators, it is necessary to identify regions with low health quality so that interventions can be more targeted and focused. Therefore, this study aims to form regional segmentation based on mortality and fertility indicators by comparing two clustering algorithms, namely K-Means and DBSCAN. The data used is secondary data from the Indonesian Central Statistics Agency, taken from the 2020 Population Census. The results of the study found that the K-Means algorithm is better than DBSCAN based on the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index evaluation matrices, with values of 0.485, 0.804, and 36.34, respectively. This algorithm formed two clusters, with western Indonesia tending to have better health quality than eastern Indonesia. Based on the results of this study, it is hoped that the government can provide focused interventions in eastern Indonesia to improve maternal and child health quality and reduce mortality rates.
References
D. T. Saputro and W. P. Sucihermayanti, “Penerapan Klasterisasi Menggunakan K-Means untuk Menentukan Tingkat Kesehatan Bayi dan Balita di Kabupaten Bengkulu Utara,” Jurnal Buana Informatika, vol. 12, no. 2, pp. 146–155, 2021.
Badan Pusat Statistik, “Statistik Demografi Indonesia (Hasil Sensus Penduduk 2020),” Jakarta, 2024.
E. Sabna and Mitra, “Analysis of K-Means Clustering in Grouping Child Mortality Rate in Indonesia,” JOISSE, vol. 1, pp. 27–31, 2024, [Online]. Available: https://jurnal.htp.ac.id/index.php/joisse
M. Syairaji, D. S. Nurdiati, B. S. Wiratama, Z. D. Prüst, K. W. M. Bloemenkamp, and K. J. C. Verschueren, “Trends and causes of maternal mortality in Indonesia: a systematic review,” BMC Pregnancy Childbirth, vol. 24, no. 1, pp. 1–14, Dec. 2024, doi: 10.1186/s12884-024-06687-6.
M. F. Rizki and F. Sulianta, “Analisis Klasterisasi Data Peserta Asuransi PT Xyz Menggunakan Metode Density-Based Spatial Clustering of Applications with Noise (DBSCAN),” Jurnal Algoritma, vol. 22, no. 1, pp. 993–1001, May 2025, doi: 10.33364/algoritma/v.22-1.2417.
D. Jollyta, M. Siddik, H. Mawengkang, and S. Efendi, “Teknik Evaluasi Cluster,” in Teknik Evaluasi Cluster Solusi Menggunakan Phyton dan Rapidminer, Yogyakarta: Deepublish, 2021, ch. I, pp. 1–9.
D. A. Setiady and H. Leong, “Implementation of K-Means Algorithm Elbow Method and Silhouette Coefficient forRainfall Classification,” PROXIES, vol. 4, no. 1, pp. 18–25, 2020.
T. Tendean and W. Purba, “Analisis Cluster Provinsi Indonesia Berdasarkan Produksi Bahan Pangan Menggunakan Algoritma K-Means,” Jurnal Sains dan Teknologi), vol. 1, no. 2, pp. 5–11, 2020.
K. B. Aditya, P. Diyah, and Y. Setiawan, “Sistem Informasi Geografis Pemetaan Faktor-Faktor yang Mempengaruhi Angka Kematian Ibu (AKI) dan Angka Kematian Bayi (AKB) dengan Metode K-Means Clustering (Studi Kasus: Provinsi Bengkulu),” Jurnal Informatika, vol. 10, no. 1, pp. 59–66, 2017.
E. Mayoana Fitri, R. Randy Suryono, and A. Wantoro, “KLASTERISASI DATA PENJUALAN BERDASARKAN WILAYAH MENGGUNAKAN METODE K-MEANS PADA PT XYZ.”
D. Widyadhana, R. B. Hastuti, I. Kharisudin, and F. Fauzi, “Perbandingan Analisis Klaster K-Means dan Average Linkage untuk Pengklasteran Kemiskinan di Provinsi Jawa Tengah,” PRISMA, Prosiding Seminar Nasional Matematika, vol. 4, pp. 584–594, 2021, [Online]. Available: https://journal.unnes.ac.id/sju/index.php/prisma/
M. Sholeh and K. Aeni, “Perbandingan Evaluasi Metode Davies Bouldin, Elbow dan Silhouette pada Model Clustering dengan Menggunakan Algoritma K-Means,” STRING (Satuan Tulisan Riset dan Inovasi Teknologi), vol. 8, no. 1, pp. 56–65, 2023, [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Trave
I. N. Simbolon and P. D. Friskila, “Analisis dan Evaluasi Algoritma DBSCAN (Density-Based Spatial Clustering of Applications with Noise) pada Tuberkulosis,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 3S1, pp. 3778–3787, Oct. 2024, doi: 10.23960/jitet.v12i3s1.5206.
A. M. A. Tambunan, U. Khaira, and B. F. Hutabarat, “Implementasi Algoritma DBSCAN untuk Klasterisasi Koperasi Berdasarkan Indikator Keuangan,” NARATIF: Jurnal Ilmiah Nasional Riset Aplikasi dan Teknik Informatika, vol. 7, no. 1, pp. 38–55, 2025.
M. R. Mubarok, A. T. J. Harjanto, and R. Renaldy, “Peningkatan Performa DBSCAN dengan Reduksi Dimensi Principal Component Analysis (PCA) dalam Klasterisasi Tingkat Kemiskinan di Indonesia,” Jurnal Informatika Teknologi dan Sains, vol. 7, no. 3, pp. 1176–1184, 2025.
I. N. Setiawan, D. Krismawati, S. Pramana, and E. Tanur, “Clustering of Natural Vulnerability Areas in The Form of Soil Movement and Earthquakes in Indonesia,” Jakarta, 2022.
R. Adha, N. Nurhaliza, U. Soleha, and Mustakim, “Perbandingan Algoritma DBSCAN dan K-Means Clustering untuk Pengelompokan Kasus Covid-19 di Dunia,” Jurnal Sains, Teknologi dan Industri, vol. 18, no. 2, pp. 206–211, 2021, [Online]. Available: https://covid19.who.int.
R. Fadhlillah et al., “Analisis Klasterisasi Kepadatan Penduduk Kabupaten Muara Enim Menggunakan Algoritma DBSCAN,” Seminar Nasional Sains Data, vol. 4, no. 1, pp. 982–992, 2024.
T. D. Harjanto, A. Vatresia, and R. Faurina, “ANALISIS PENETAPAN SKALA PRIORITAS PENANGANAN BALITA STUNTING MENGGUNAKAN METODE DBSCAN CLUSTERING,” 2021. [Online]. Available: http://ejournal.unib.ac.id/index.php/rekursif/30
A. M. Sikana and A. W. Wijayanto, “Analisis Perbandingan Pengelompokan Indeks Pembangunan Manusia Indonesia Tahun 2019 dengan Metode Partitioning dan Hierarchical Clustering,” Jurnal Ilmu Komputer, vol. 14, no. 2, pp. 66–78, Sep. 2021, doi: 10.24843/jik.2021.v14.i02.p01.
M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining, vol. 96, no. 34, 1996.
N. E. Kondruk, “A Comparative Study of Cluster Validity Indices,” Radio Electronics, Computer Science, Control, vol. 0, no. 4, pp. 59–67, Nov. 2019, doi: 10.15588/1607-3274-2019-4-6.
A. Azzahra and A. W. Wijayanto, “Comparison of Agglomerative Hierarchical and K-Means in Grouping Provinces Based on Maternal Health Services,” Sistemasi: Jurnal Sistem Informasi, vol. 11, no. 2, pp. 481–495, 2022.
A. Astasia and Y. A. Faeni, “Maternal Mortality Risk di Indonesia Analisis Biplot dan Klaster Menggunakan Data Survei Kesehatan dan Demografi Indonesia (SDKI) Tahun 2017,” Jurnal Sistem Cerdas, vol. 3, no. 2, pp. 144–152, 2020.
T. P. Hapsari and L. A. Salim, “Efektivitas Asuhan Antenatal sebagai Upaya untuk Mencegah Komplikasi Obstetri yang Berdampak terhadap Kematian Ibu: Literature Review,” Jurnal Keperawatan dan Kesehatan Masyarakat Cendekia Utama, vol. 12, no. 2, pp. 115–122, 2023.
I. P. Sari et al., “Faktor Penyebab Angka Kematian Ibu dan Angka Kematian Bayi serta Strategi Penurunan Kasus (Studi Kasus di Negara Berkembang) : Sistematic Review,” PREPOTIF Jurnal Kesehatan Masyarakat, vol. 7, no. 3, pp. 16578–16593, 2023.
S. Wenang, O. Emilia, A. Wahyuni, A. Afdal, and J. Haier, “Obstetrics care in Indonesia: Determinants of maternal mortality and stillbirth rates,” PLoS One, vol. 19, no. 7, pp. 1–16, Jul. 2024, doi: 10.1371/journal.pone.0303590.
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