Klasterisasi Prestasi Provinsi Dalam Kompetisi Sains Nasional Menggunakan K-Means - Ronald Hutasoit; Budi Chandra Hasiholan; Sardo Sipayung; Angelus Fanotona Luahambowo (GLOBAL SCIENTS PUBLISHER, 2026)
The National Science Olympiad (KSN) is one of strategic indicators to measure the quality of education throughout Indonesia. But historical evidence shows a wide gap in performance between provinces, not least between Java and beyond Java. This research aims to map and categorize provinces in Indonesia on their performance in OSN using K-Means clustering. The data employed was the number of participants and the weighted medal tallies per province. Preprocessing of Data: Z-Score standardization of data is performed to account for variances in scale between features. The best number of clusters is performed by Elbow Method and Silhouette Score. The results revealed three clusters: Cluster 1 (High Achievers) included 3 provinces with the highest Java; Cluster 2 (Potential Achiever), included four provinces with high participation and fair scores, and Cluster 3 (Developing Area), included 31 low-performing provinces. This map shows objective evidence of educational disparity, and can be used by the government as a guide for future development in low performing regions.