Comparison of K-Means and K-Medoids Algorithms in Clustering Indonesian Provinces Using Stunting Handling Index
DOI:
https://doi.org/10.25217/numerical.v9i2.7194Keywords:
Stunting, K-Means, K-Medoids, DBI, Provincial ClusteringAbstract
Stunting remains a major public health problem and poses a significant challenge to human resource development in Indonesia. Differences in stunting management performance across provinces indicate regional disparities that require systematic and data-driven analysis. This study aims to cluster provinces in Indonesia based on the 2022 Stunting Management Specific Index (IKPS) and to compare the performance of the K-Means and K-Medoids clustering algorithms. The study uses secondary data from the Central Statistics Agency (BPS), covering 34 provinces and ten indicators composing the IKPS. Clustering was conducted using K-Means and K-Medoids algorithms. The optimal number of clusters was determined using the Elbow Method, while clustering quality was evaluated using the Davies–Bouldin Index (DBI). The results show that the optimal number of clusters is k = 5. Furthermore, the K-Medoids algorithm produces better clustering quality, as indicated by a lower DBI value compared to the K-Means algorithm, reflecting more compact clusters and clearer separation between provinces. The clustering results reveal distinct provincial groupings with varying stunting management characteristics, ranging from provinces with relatively strong and stable performance to those facing greater challenges related to geographical constraints and limited access to health services. Overall, this study demonstrates that cluster analysis is effective for identifying regional patterns in stunting management and can support policymakers in formulating more targeted, province-based strategies to improve the effectiveness of stunting prevention and intervention programs in Indonesia.
References
Abbas, S. A., Aslam, A., Rehman, A. U., Abbasi, W. A., Arif, S., & Kazmi, S. Z. H. (2020). K-Means and K-Medoids: Cluster Analysis on Birth Data Collected in City Muzaffarabad, Kashmir. IEEE Access, 8, 151847–151855. https://doi.org/10.1109/ACCESS.2020.3014021
Agbaje, M. B., Ezugwu, A. E., & Els, R. (2019). Automatic data clustering using hybrid firefly particle swarm optimization algorithm. IEEE Access, 7, 184963–184984. https://doi.org/10.1109/ACCESS.2019.2960925
Alam, M. S., Rahman, M. M., Hossain, M. A., Islam, M. K., Ahmed, K. M., Ahmed, K. T., Singh, B. C., & Miah, M. S. (2019). Automatic human brain tumor detection in mri image using template-based k means and improved fuzzy c means clustering algorithm. Big Data and Cognitive Computing, 3(2), 1–18. https://doi.org/10.3390/bdcc3020027
Arora, P., Deepali, & Varshney, S. (2016). Analysis of K-Means and K-Medoids Algorithm for Big Data. Physics Procedia, 78(December 2015), 507–512. https://doi.org/10.1016/j.procs.2016.02.095
BPS. (2023). Laporan Indeks Khusus Penanganan Stunting.
Brito Da Silva, L. E., Melton, N. M., & Wunsch, D. C. (2020). Incremental Cluster Validity Indices for Online Learning of Hard Partitions: Extensions and Comparative Study. IEEE Access, 8, 22025-22047. https://doi.org/10.1109/ACCESS.2020.2969849
Cabitza, F., Campagner, A., & Mattioli, M. (2022). The unbearable (technical) unreliability of automated facial emotion recognition. Big Data and Society, 9(2). https://doi.org/10.1177/20539517221129549
Dewi, S., Defit, S., & Yuhandri, Y. (2021). Akurasi Pemetaan Kelompok Belajar Siswa Menuju Prestasi Menggunakan Metode K-Means. Jurnal Sistim Informasi Dan Teknologi, 3, 28–33. https://doi.org/10.37034/jsisfotek.v3i1.40
Fatimah, & Nuryaningsih. (2018). Buku Ajar Buku Ajar.
Faujia, R. A., Setianingsih, E. S., & Pratiwi, H. (2022). Analisis Klaster K-Means Dan Agglomerative Nesting Pada Indikator Stunting Balita Di Indonesia. Seminar Nasional Official Statistics, 2022(1), 1249–1258. https://doi.org/10.34123/semnasoffstat.v2022i1.1511
Frayudi, F., Bahri, S., & Bakar, N. N. (2019). Menentukan Akar Persamaan Nonlinier Dengan Metode Approksimasi Lingkaran. Jurnal Matematika UNAND, 4(2), 38-45. https://doi.org/10.25077/jmu.4.2.38-45.2015
Herdiana, I., Kamal, M. A., Triyani, E., N, M., & Renny. (2025). A More Precise Elbow Method for Optimum K-means Clustering. 1–22. http://arxiv.org/abs/2502.00851
Luchia, N. T., Handayani, H., Hamdi, F. S., Erlangga, D., & Octavia, S. F. (2022). Perbandingan K-Means dan K-Medoids Pada Pengelompokan Data Miskin di Indonesia. Malcom: Indonesian Journal of Machine Learning and Computer Science, 2(2), 35-41. https://doi.org/10.57152/malcom.v2i2.422
Madbouly, M. M., Darwish, S. M., Bagi, N. A., & Osman, M. A. (2022). Clustering Big Data Based on Distributed Fuzzy K-Medoids: An Application to Geospatial Informatics. IEEE Access, 10, 20926–20936. https://doi.org/10.1109/ACCESS.2022.3149548
Mohemad, R., Mohd Muhait, N. N., Mohamad Noor, N. M., & Othman, Z. A. (2022). Performance analysis in text clustering using k-means and k-medoids algorithms for Malay crime documents. International Journal of Electrical and Computer Engineering (IJECE), 12(5). https://doi.org/10.11591/ijece.v12i5.pp5014-5026
Mughnyanti, M., Efendi, S., & Zarlis, M. (2020). Analysis of determining centroid clustering x-means algorithm with davies-bouldin index evaluation. IOP Conference Series: Materials Science and Engineering, 725(1). https://doi.org/10.1088/1757-899X/725/1/012128
Nirmal, S. (2019). Comparative study between k-means and k-medoids clustering algorithms. International Research Journal of Engineering and Technology, 839, 839–844. https://www.irjet.net/archives/V6/i3/IRJET-V6I3154.pdf
Omran, M. G. H., Engelbrecht, A. P., & Salman, A. (2007). An overview of clustering methods. Intelligent Data Analysis, 11(6), 583–605. https://doi.org/10.3233/ida-2007-11602
Prahara, A., Ismi, D. P., & Azhari, A. (2020). Parallelization of Partitioning Around Medoids (PAM) in K-Medoids Clustering on GPU. Knowledge Engineering and Data Science, 3(1), 40–49. https://doi.org/10.17977/um018v3i12020p40-49
Rahman, R. R. A., & Wijayanto, A. W. (2021). Pengelompokan Data Gempa Bumi Menggunakan Algoritma Dbscan. Jurnal Meteorologi Dan Geofisika, 22(1), 31. https://doi.org/10.31172/jmg.v22i1.738
Syukron, H., Fauzi Fayyad, M., Junita Fauzan, F., Ikhsani, Y., & Rizkya Gurning, U. (2022). Perbandingan K-Means K-Medoids dan Fuzzy C-Means untuk Pengelompokan Data Pelanggan dengan Model LRFM. Malcom: Indonesian Journal of Machine Learning and Computer Science, 2(2), 76-83. https://doi.org/10.57152/malcom.v2i2.442
Xu, D., & Tian, Y. (2015). A Comprehensive Survey of Clustering Algorithms. Annals of Data Science, 2(2), 165–193. https://doi.org/10.1007/s40745-015-0040-1
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Mayang Ariani Fitria Ananta, A'yunin Sofro

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

