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All Journal Enthusiastic : International Journal of Applied Statistics and Data Science Journal of Mathematics, Computation and Statistics (JMATHCOS)
Agustin, Widya Saputri
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics

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Clustering of Provinces in Indonesia Based on Environmental Health Indicators Using K-Medoids Agustin, Widya Saputri; Mardiyyah, Safwah Ayu; Zahra , Qolbiyatus Syifa Az; Anggreany, Anggun Nur; Widodo, Edy
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 1, April 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss1.art9

Abstract

According to the Ministry of Health of the Republic of Indonesia, key environmental health indicators include access to safe drinking water, adequate sanitation, and healthy living environments. As of 2023, only 10.21% of Indonesian households had access to safe sanitation, far from the government’s 2045 target of 70%. Indonesia’s ranking at 164th out of 180 countries in the 2022 environment performance index (EPI), with a score of 28.20 out of 100, further underscores the need for targeted interventions. This study aims to classify Indonesian provinces based on environmental health indicators, thereby supporting more effective policy prioritization. The k-medoids clustering algorithm was employed due to its robustness to outliers and flexibility in handling mixed data types, making it well-suited for this context. This study utilized data from 34 provinces in 2023, sourced from the Ministry of Health. These provinces were grouped into two clusters, with cluster 2 representing provinces with stronger environmental health performance. The clustering results were validated using the silhouette coefficient, confirming the quality of the groupings. Provinces in cluster 1 require greater policy attention to improve environmental health conditions. This study demonstrates the potential of robust medoids-based clustering for guiding targeted environmental health strategies in developing countries.
Comparison of K-Means and K-Medoids Methods in Grouping Provinces in Indonesia Based on Economic Development Indicators Agustin, Widya Saputri; Armadhan, Rinda; Dini, Sekti Kartika; Murda, Handani
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcosv9n19866

Abstract

Economic growth in Indonesia varies significantly between provinces, reflecting disparities in welfare indicators such as poverty levels, education, and access to infrastructure. Understanding these disparities is crucial for formulating effective development policies. This study aims to cluster provinces in Indonesia based on economic development indicators 2023, with the dataset sourced from Badan Pusat Statistik (BPS). The research employs K-Means and K-Medoids clustering methods, with the optimal number of clusters determined using the Silhouette method. K-Means produced six clusters, while K-Medoids identified eight clusters. Performance evaluations using the Dunn Index (DI), Davies-Bouldin Index (DBI), and Xie-Beni Index (XBI) revealed that K-Means outperformed K-Medoids, achieving a higher DI (0.31) and lower XBI (1.78). These results indicate that K-Means with six clusters provides better separation and higher intra-cluster density compared to K-Medoids. Profiling of the clusters revealed substantial regional disparities, with some clusters exhibiting high welfare levels and others facing significant challenges in poverty, unemployment, and health issues. Cluster 1 has moderate income and development but high unemployment and health issues. Cluster 2 shows strong development and low poverty but unresolved crime. Cluster 3 has low income, minimal poverty, and health complaints. Cluster 4 excels in income and labor but struggles with poverty and crime. Cluster 5 is prosperous but faces health issues. Cluster 6 has low income, moderate poverty, and significant health challenges. This study aims to assist policymakers in designing tailored strategies to address specific weaknesses and capitalize on regional strengths to reduce disparities and enhance equitable development.
Co-Authors Anggreany, Anggun Nur Armadhan, Rinda Dini, Sekti Kartika Edy Widodo Mardiyyah, Safwah Ayu Murda, Handani Zahra , Qolbiyatus Syifa Az