Comparison of K-Means and DBSCAN Algorithms in Regional Segmentation in Indonesia Based on Mortality and Fertility Indicators - Nafandra, Bunga; Sulistiowati, Dwi; Amalita, Nonong (Jurnal MSA (Matematika dan Statistika serta Aplikasinya), 2026)
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.