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All Journal Artificial Intelligence in Educational Decision Sciences
Muhammad Fikri Aqil
Universitas Negeri Makassar
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences

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Clustering Multi-Indicator Learning Outcomes of Vocational High School Students: A Comparison of K-Means and DBSCAN Muhammad Fikri Aqil; Irwansyah Suwahyu
Artificial Intelligence in Educational Decision Sciences Vol 1 No 2 (2026): Artificial Intelligence in Educational Decision Sciences
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aieds.v1i2.24

Abstract

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.
Co-Authors Irwansyah Suwahyu