Analisis Pemerataan Pendidikan di Indonesia Menggunakan Reduksi Dimensi PCA dan Klasterisasi K-Means - Kusuma, Erwin Arry; Dharmawati, Adani (Unversitas Muhammadiyah Riau, 2026)
Educational equity in Indonesia continues to face substantial challenges due to significant disparities in achievement across provinces. This study aims to map these gaps by combining Principal Component Analysis (PCA) for dimensionality reduction and K-Means Clustering for regional grouping. Utilizing 2023 data from the Indonesian Central Bureau of Statistics (BPS) with eight key indicators, the analysis reveals that three principal components effectively capture 91.85% of the data variance. The clustering procedure successfully categorizes provinces into two distinct groups: 36 provinces in the high-achievement cluster and two provinces that lag significantly (Central Papua and Papua Mountains). A Silhouette Score of 0.782 confirms the high validity and consistency of the clustering results. These findings serve as a critical alert for policymakers to implement targeted interventions in underperforming regions to prevent further widening of the educational gap.