<![CDATA[Purpose – This study aims to compare the performance of K-Means and DBSCAN algorithms in clustering vocational high school students’ learning outcomes in the Network Administration subject to support data-driven educational decision making.Methods – A quantitative experimental approach was employed using secondary academic data from vocational students. The variables analyzed included final examination scores, midterm examination scores, assignments, attendance, attitudes, and learning activities. Clustering was conducted using K-Means and DBSCAN algorithms implemented through data analysis software. Cluster quality and separation were evaluated using silhouette coefficients to assess the effectiveness of each algorithm in grouping student learning outcomes.Findings – The results show that K-Means produces relatively stable and interpretable clusters when student performance data exhibit more uniform distributions. In contrast, DBSCAN demonstrates stronger capability in handling noisy data and identifying students with extreme performance levels as outliers. Both algorithms successfully reveal meaningful patterns in student learning outcomes, but differ in their sensitivity to data distribution and noise.Research limitations – This study is limited to a single vocational subject and one institutional context, which may restrict the generalizability of the findings to other vocational domains.Originality – This study provides empirical evidence on the comparative performance of partition-based and density-based clustering algorithms using multi-indicator learning outcome data in vocational education.]]>
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