<![CDATA[The quality of education in Indonesia still exhibits disparities among provinces, reflecting differences in educational attainment and access. This study aims to segment the quality of education across Indonesian provinces based on the similarity of educational characteristics using the K-Means Clustering method. The data used consist of provincial-level education data that have undergone outlier detection and standardization to ensure comparability across variables. K-Means Clustering analysis was performed by forming three clusters representing provinces with low, medium, and high levels of educational quality. The clustering results indicate that most provinces fall into the medium education quality cluster, while a smaller number of provinces remain in the low education quality cluster. These findings demonstrate that the K-Means Clustering method is able to provide a clear representation of segmentation patterns and disparities in educational quality across Indonesian provinces and can serve as a basis for supporting more targeted and equity-oriented education policy formulation. Keywords: education; quality; K-Means; clustering; provinces]]>
Copyrights © 2026
