Clustering Regional Educational Performance in Indonesia Using K-Means - Herwindo Bagus Saputro; M. Mujiya Ulkhaq (Journal of Computing and Smart Ecosystems, 2025)
This study examines regional disparities in educational development across Indonesia by clustering 38 provinces based on indicators of SDG 4 (Quality Education). Using the k-means algorithm with k = 4, the analysis identifies groups of provinces with similar educational profiles to support evidence-based policymaking. The resulting clusters reveal substantial heterogeneity. Cluster 1 (highly disadvantaged) consists of Papua Pegunungan and Papua Tengah, which exhibit the lowest national performance across schooling and attendance indicators. Cluster 2 (disadvantaged) includes 20 provinces with low to moderate achievement levels, including short average schooling duration and low upper-secondary completion. Cluster 3 (advanced) comprises 15 provinces with relatively strong educational outcomes. Cluster 4 (highly advanced) is represented solely by the Special Region of Yogyakarta, demonstrating markedly superior performance. These findings highlight persistent educational inequality and suggest differentiated policy priorities. Interventions for lagging clusters should focus on improving access, teacher quality, and infrastructure, particularly in remote and disadvantaged regions. By providing an empirically derived typology of provincial education performance, this study contributes to better-targeted strategies for achieving SDG 4 and reducing regional disparities in Indonesia.