<![CDATA[The Academic Ability Test (TKA) serves as a tool for assessing students' competency levels and facilitates the comparison of academic performance among various provinces in Indonesia. The data from TKA, characterized by their multidimensional nature and intercorrelations, necessitate the use of a multivariate analytical strategy. This study aims to cluster the provinces of Indonesia based on TKA scores by using K-Means clustering in conjunction with Principal Component Analysis (PCA) and biplot visualization. The dataset comprises TKA scores collected from 16 subjects spanning 38 provinces. PCA is utilized to convert the variables into orthogonal principal components, effectively minimizing the influence of inter-variable correlations. Following this, clustering is executed through K-Means, where the ideal number of clusters is established by analyzing the pseudo-F statistic. The results indicate that the provinces can be divided into two distinct clusters: one characterized by relatively low academic achievement and the other by high academic achievement. The biplot visualization indicates that the differentiation of clusters is mainly driven by the first principal component, reflecting overall scholarly achievement. In conclusion, substantial disparities in academic achievement across Indonesian provinces are identified. The integration of PCA and clustering produces a more robust and interpretable grouping structure by accounting for inter-variable correlations. These findings provide a data-driven basis for designing targeted educational policies to enhance equity and improve overall education quality.]]>
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