K-Means Clustering for Adaptive Learning Recommendations Based on Student Academic Performance in Vocational Education - petti sijabat; Eviyanti Barus; Kristin Sitompul; Sinta Suwanda; Maira Mau Lydia (Universitas Budi Darma, 2026)
Differences in students' academic abilities create challenges in the learning process because many schools still apply uniform learning strategies without considering individual academic characteristics. This study aims to cluster students based on academic performance using the K-Means Clustering algorithm and to generate adaptive learning recommendations for each cluster. The dataset consisted of academic records from 50 students of SMK Swasta 2 Delima Sari in the 2024/2025 academic year, including assignment scores, Midterm Examination (UTS) scores, Final Examination (UAS) scores, and attendance rates. Data preprocessing was conducted through data cleaning and Min-Max normalization to ensure that each variable contributed proportionally to the clustering process. The K-Means algorithm was implemented with k = 3 to form high, medium, and low academic performance groups. The results show that 12 students (24%) were classified into the High Cluster, 28 students (56%) into the Medium Cluster, and 10 students (20%) into the Low Cluster. The clustering quality was supported by a WCSS value of 2.3714 and a Silhouette Score of 0.6843, indicating reasonably well-separated clusters. Based on the cluster profiles, students in the High Cluster are recommended to receive enrichment activities, students in the Medium Cluster receive regular instruction with periodic feedback, and students in the Low Cluster receive remedial learning and structured mentoring. These findings indicate that K-Means Clustering can support data-driven educational decision-making and provide a practical basis for adaptive learning recommendations in vocational education.