Education disparities across regions often reflect differences in school density, teacher availability, and student dropout rates. This study aims to classifies junior high school education in West Java into more homogeneous groups to better understand these disparities. Two clustering approaches were applied: quartile grouping and the K-Means algorithm. Quartile grouping provided a simple categorization of each indicator into four levels (very high, high, low, very low), while K-Means offers a more flexible and data-driven segmentation. K-Means algorithm produced three distinct clusters: (1) Balanced and Stable regions with proportional ratios and low dropout rates, (2) High-Density but Stable regions concentrated in urban and periurban areas with high student-teacher and student-school ratios but controlled dropout levels, and (3) Elevated Dropout Risk regions, mostly in rural and southern areas, with lower density but higher dropout rates. The comparison shows that quartile grouping is easy to interpret for individual indicators, while K-Means provides more comprehensive insights into multidimensional patterns. This research highlights the potential of clustering methods to guide policymakers in designing differentiated strategies, from infrastructure expansion in dense regions to social support programs in dropout-prone areas.
Nurkhofifah et al. (Mon,) studied this question.