Application of K-Medoids for Regional Classification Based on Quality, Access, and Governance of Education in Indonesia - Robiati, Silfi; Hakim, Abdul; Dharmawan, Goldy; Khotimah, Chusnul (Proceedings of The International Conference on Data Science and Official Statistics, 2025)
Education is a fundamental foundation for individuals, yet substantial disparities persist across Indonesia, including both 3T (Disadvantaged, Frontier, and Outermost) and non3T regions. Addressing the limited research on systematic regional mapping based on education indicators, this study analyzes 514 regencies/cities at the senior secondary level using 13 indicators covering three latent dimensions identified through Factor Analysis: education quality, quality of the learning process, and governance and educational participation. Data were processed through outlier detection, standardization, dimensionality reduction using Principal Component Analysis, factor score extraction, and K-Medoids clustering in RStudio. The optimal solution with three clusters was validated with a Davies–Bouldin Index of 1.44, confirming its effectiveness in capturing regional variation. Results reveal distinct spatial patterns in educational characteristics, where some 3T regions perform comparably to non-3T areas, while certain remote regions face challenges across all dimensions. These findings provide a basis for targeted, cluster-based policy interventions to improve education quality, expand access, and strengthen governance, supporting equitable educational development nationwide. The study demonstrates the utility of combining dimensionality reduction and clustering for evidencebased policy planning and highlights the importance of addressing regional disparities in education.