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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Intan Putri Septiyani
STMIK IKMI Cirebon
Automotive Engineering Computer Science & IT Control & Systems Engineering

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Comparative Analysis of K-Means Clustering and K-Medoids Clustering Methods in Clustering Neonatal Infant Mortality Rates in West Java Province Intan Putri Septiyani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2329

Abstract

Neonatal mortality rate is an important indicator in assessing public health conditions. This study aims to cluster neonatal mortality data in West Java Province using the K-Means Clustering and K-Medoids Clustering methods, as well as compare the performance of both methods in producing the best clusters. The study used secondary data obtained from Open Data West Java. The research stages included data selection, preprocessing, clustering, and evaluation using the Davies-Bouldin Index (DBI). The experiments were conducted using cluster variations (k) from 2 to 8. The results showed that the K-Means Clustering method produced the best performance with a DBI value of 0.430 at k = 3. The clustering results generated three categories: low-risk cluster with 408 data points, medium-risk cluster with 65 data points, and high-risk cluster with 13 data points. The differences in cluster characteristics indicate variations in neonatal mortality risk levels among regions in West Java Province. The findings of this study are expected to support decision-making and more targeted health policy planning.   Keywords: K-Means Clustering, K-Medoids Clustering, Davies-Bouldin Index, Neonatal Mortality.
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