Application of the K-Medoid Algorithm to Cluster Percentage Data Based on Urban and Rural Areas in Indonesia
DOI:
https://doi.org/10.59934/jaiea.v5i2.2113Keywords:
K-Medoids, Data Mining, Clustering, Maternal Health, Health FacilitiesAbstract
This study applies the K-Medoids clustering algorithm to group Indonesian regions based on the percentage of ever-married women aged 15–49 years who gave birth in health facilities. The data used are secondary data obtained from Statistics Indonesia (BPS). The K-Medoids algorithm was chosen due to its robustness against outliers compared to K-Means [1]. The results show that regions can be grouped into clusters representing high and moderate utilization of health facilities for childbirth. This clustering can assist policymakers in identifying regional disparities and improving maternal health services.
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References
.L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, New York: Wiley, 1990.
World Health Organization, Trends in Maternal Mortality, Geneva: WHO, 2019.
Kementerian Kesehatan RI, Profil Kesehatan Indonesia, Jakarta: Kemenkes RI, 2022.
A. N. Sari et al., “Analisis Kesenjangan Pelayanan Kesehatan Ibu di Indonesia,” Jurnal Kesehatan Masyarakat, vol. 15, no. 2, pp. 123–130, 2020.
J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed., San Francisco: Morgan Kaufmann, 2012.
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, New York: Springer, 2009.
Badan Pusat Statistik, “Persentase Perempuan Pernah Kawin yang Melahirkan di Fasilitas Kesehatan,” 2025.
A. Jain, “Data Clustering: 50 Years Beyond K-Means,” Pattern Recognition Letters, vol. 31, no. 8, pp. 651–666, 2010.
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