<![CDATA[This study aims to compare the performance of the K-Means and Hierarchical Clustering algorithms in grouping scholarship applicants based on academic and socio-economic data. The methodology includes data preprocessing through normalization, determining the optimal number of clusters using the Elbow method, and applying both clustering algorithms. The evaluation is conducted using the Silhouette Score to measure cluster quality based on cohesion and separation. The results show that K-Means produces a higher Silhouette Score compared to Hierarchical Clustering, indicating that K-Means is able to form more compact clusters with clearer separation between them. In contrast, Hierarchical Clustering tends to produce less optimal cluster structures, especially when applied to datasets with a relatively large number of data points. Therefore, K-Means can be recommended as a more effective method for grouping scholarship applicants based on academic and socio-economic data. This study is expected to serve as a reference in selecting appropriate clustering methods in data mining, particularly to support more objective and accurate decision-making processes.]]>
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