K-Means Clustering for Adaptive Learning Recommendations Based on Student Academic Performance in Vocational Education
DOI:
https://doi.org/10.30865/ijics.v10i2.9806Keywords:
K-Means Clustering, Educational Data Mining, Adaptive Learning, Student Academic Performance, Vocational EducationAbstract
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.
References
[1] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Waltham, MA, USA: Morgan Kaufmann, 2012.
[2] C. Romero and S. Ventura, “Educational data mining: A review of the state of the art,” IEEE Transactions on Systems, Man, and Cybernetics, Part C, vol. 40, no. 6, pp. 601–618, 2010, doi: 10.1109/TSMCC.2010.2053532.
[3] J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proc. 5th Berkeley Symposium on Mathematical Statistics and Probability, 1967, pp. 281–297.
[4] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
[5] P. J. Rousseeuw, “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis,” Journal of Computational and Applied Mathematics, vol. 20, pp. 53–65, 1987, doi: 10.1016/0377-0427(87)90125-7.
[6] A. Akram, N. Risal, D. D. Andayani, M. I. Suherman, and A. B. Kaswar, “Utilizing the K-Means Clustering Algorithm for Analyzing Student Achievement Assessment at SMK Negeri 1 Gowa,” Journal of Embedded Systems, Security and Intelligent Systems, vol. 5, no. 1, pp. 60–67, 2024, doi: 10.59562/jessi.v5i1.2178.
[7] J. Hutagalung, Y. H. Syahputra, and Z. P. Tanjung, “Pemetaan siswa kelas unggulan menggunakan algoritma K-Means Clustering,” JATISI (Jurnal Teknik Informatika dan Sistem Informasi), vol. 9, no. 1, pp. 606–620, 2022, doi: 10.35957/jatisi.v9i1.1516.
[8] E. A. Saputra and Y. Nataliani, “Analisis pengelompokan data nilai siswa untuk menentukan siswa berprestasi menggunakan metode clustering K-Means,” Journal of Information Systems and Informatics, vol. 3, no. 3, pp. 424–439, 2021, doi: 10.51519/journalisi.v3i3.164.
[9] C. Azzahra and Sriani, “Clustering of high school students academic scores using K-Means Algorithm,” Journal of Information Systems and Informatics, vol. 7, no. 1, pp. 572–586, 2025, doi: 10.51519/journalisi.v7i1.1029.
[10] S. J. S. Alawi and I. N. M. Shaharanee, “Clustering student performance data using K-Means algorithms,” Journal of Computing Innovation and Analytics, vol. 2, no. 1, pp. 41–55, 2023.
[11] Tundo, S. Raihanah, T. Wahyudi, and Sugiyono, “K-Means Clustering of Social Studies Performance at Junior High School,” IJID (International Journal on Informatics for Development), vol. 13, no. 2, pp. 460–472, 2024, doi: 10.14421/ijid.2024.4632.
[12] A. A. N. Risal, D. Maryani, N. Fadillah, and A. A. Risal, “Implementasi K-Means Clustering untuk rekomendasi kelas unggulan di SMK 1 Teknologi dan Rekayasa Mimika,” Journal of Embedded Systems, Security and Intelligent Systems, vol. 5, no. 3, pp. 255–261, 2024.
[13] A. Paramythis and S. Loidl-Reisinger, “Adaptive learning environments and e-learning standards,” Electronic Journal of e-Learning, vol. 2, no. 1, pp. 181–194, 2004.
