<![CDATA[The disparity in education quality between regions remains a real challenge in Indonesia, necessitating accurate regional mapping as a basis for targeted policymaking. This study aims to cluster 38 provinces in Indonesia based on educational achievement indicators to identify regions with extreme disparities. The method used is the K-Means Clustering algorithm with attribute selection and Z-Score-based data normalization stages executed using RapidMiner software. Validation of the optimal number of clusters was carried out using the Elbow Method approach and dual evaluation metrics, namely the Davies-Bouldin Index (DBI) and Silhouette Score. The results showed that the 3-cluster configuration was the most optimal, with the lowest DBI value (0.58) and the highest Silhouette Score (0.78). This clustering successfully identified two New Autonomous Regions (DOB) in Papua, namely the Papua Mountains Province and the Papua Central Province, which are included in the low (critical) education quality cluster with a very extreme gap in educational ability and illiteracy rates compared to other regions. In conclusion, the application of the K-Means algorithm based on comprehensive validation has proven effective in highlighting regional priorities, so that it can become an empirical basis for the government in its efforts to equalize educational facilities specifically in Indonesia.]]>
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