Article Sidebar

Submitted
December 12, 2025
Published
March 30, 2026
Download
pdf
Statistic
Read Counter : 0 Download : 0

Main Article Content

Abstract

This study aims to compare the performance of five clustering algorithms, a K-Means, K-Medoids, Fuzzy C-Means (FCM), DBSCAN, and Gaussian Mixture Model (GMM) in profiling 239 students using quantitative data. The methodology includes data collection, refinement, transformation, application of clustering algorithms, and evaluation using the Silhouette Score, Davies–Bouldin Index, and execution time. The results indicate that K-Means provides the most balanced performance, achieving the highest Silhouette score with well-defined cluster separation. K-Medoids and GMM demonstrate competitive performance, while DBSCAN excels in detecting outliers but produces an excessive number of clusters, limiting its interpretability for profiling. FCM performs the weakest due to poor cluster separability. Overall, K-Means is recommended as the primary approach for student profiling, while other algorithms may complement specific analytical needs.

Keywords

Clustering Educational Data Mining Student Profiling

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Author Biographies

Noor Azizah, Universitas Diponegoro

-

Kusworo Adi, Universitas Diponegoro

-

Catur Edi Widodo, Universitas Diponegoro

-

References

  1. Abbas, S.A., Aslam, A., Rehman, A.U., Abbasi, W.A., Arif, S. and Kazmi, S.Z.H. (2020), “K-Means and K-Medoids: Cluster Analysis on Birth Data Collected in City Muzaffarabad, Kashmir”, IEEE Access, Vol. 8, pp. 151847–151855, doi: 10.1109/ACCESS.2020.3014021.
  2. Andre, Suciati, N., Fabroyir, H. and Pardede, E. (2023), “Educational Data Mining Clustering Approach: Case Study of Undergraduate Student Thesis Topic”, IEEE Access, IEEE, Vol. 11 No. September, pp. 130072–130088, doi: 10.1109/ACCESS.2023.3332818.
  3. Bezdek, J.C. (1981), “Objective Function Clustering BT - Pattern Recognition with Fuzzy Objective Function Algorithms”, in Bezdek, J.C. (Ed.), , Springer US, Boston, MA, pp. 43–93, doi: 10.1007/978-1-4757-0450-1_3.
  4. Ezugwu, A.E., Ikotun, A.M., Oyelade, O.O., Abualigah, L., Agushaka, J.O., Eke, C.I. and Akinyelu, A.A. (2022), “A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects”, Engineering Applications of Artificial Intelligence, Elsevier Ltd, Vol. 110 No. January, p. 104743, doi: 10.1016/j.engappai.2022.104743.
  5. Fahrudin, T., Asror, I. and Wibowo, Y.F.A. (2023), “Student Enrollment Performance of Telkom Schools in 23 / 24 schoolyear using k-Means Clustering”, International Conference on Informatics and Computing (ICIC), IEEE, pp. 1–5, doi: 10.1109/ICIC60109.2023.10381939.
  6. Ghosh, A., Sengupta, P., Chaudhuri, A.K., Mukherjee, D., Das, A.K. and De, P. (2025), “Analyzing Student Achievement with Clustering Techniques in Educational Analytics”, Panamerican Mathematical Journal, Vol. 35 No. 1, pp. 424–436, doi: https://doi.org/10.52783/pmj.v35.i4s.6014.
  7. Hosen, M.A., Moz, S.H., Kabir, S.S., Galib, S.M. and Adnan, M.N. (2023), “Enhancing Thyroid Patient Dietary Management with an Optimized Recommender System based on PSO and K-means”, Procedia Computer Science, Elsevier B.V., Vol. 230 No. 2023, pp. 688–697, doi: 10.1016/j.procs.2023.12.124.
  8. Huang, Z., Liang, Z., Zhou, S. and Zhang, S. (2025), “An Improved Density-Based Spatial Clustering of Applications with Noise Algorithm with an Adaptive Parameter Based on the Sparrow Search Algorithm”, Algorithms.
  9. Jain, M., Kaur, G. and Saxena, V. (2022), “A K-Means clustering and SVM based hybrid concept drift detection technique for network anomaly detection”, Expert Systems with Applications, Elsevier Ltd, Vol. 193 No. June 2020, p. 116510, doi: 10.1016/j.eswa.2022.116510.
  10. Jaiswal, S. (2025), “Clustering Students Based on Learning Styles : A Machine Learning Approach for Personalized Education”, 2025 World Skills Conference on Universal Data Analytics and Sciences (WorldSUAS), India, doi: 10.1109/WorldSUAS66815.2025.11198965.
  11. Kasa, S.R. and Rajan, V. (2023), “Avoiding inferior clusterings with misspecified Gaussian mixture models”, Scientific Reports, Nature Publishing Group UK, pp. 1–13, doi: 10.1038/s41598-023-44608-3.
  12. Ma, B., Yang, C., Li, A., Chi, Y. and Chen, L. (2023), “ScienceDirect ScienceDirect 10th International Conference on Information Technology and Quantitative Management A Faster DBSCAN Algorithm Based on Self-Adaptive A Faster DBSCAN Algorithm Based on Self-Adaptive Determination of Parameters Determination of Parameters”, Procedia Computer Science, Elsevier B.V., Vol. 221, pp. 113–120, doi: 10.1016/j.procs.2023.07.017.
  13. Priyambada, S.A., Er, M., Yahya, B.N. and Usagawa, T. (2021), “Profile-Based Cluster Evolution Analysis : Identification of Migration Patterns for Understanding Student Learning Behavior”, IEEE Access, IEEE, Vol. 9, pp. 101718–101728, doi: 10.1109/ACCESS.2021.3095958.
  14. Raya, S., Orabi, M., Afyouni, I. and Aghbari, Z. Al. (2024), “Neurocomputing Multi-modal data clustering using deep learning : A systematic review”, Neurocomputing, Elsevier B.V., Vol. 607 No. September 2022, p. 128348, doi: 10.1016/j.neucom.2024.128348.
  15. Xu, D. and Tian, Y. (2015), “A Comprehensive Survey of Clustering Algorithms”, Annals of Data Science, Springer Berlin Heidelberg, Vol. 2 No. 2, pp. 165–193, doi: 10.1007/s40745-015-0040-1.
  16. Yang, D., Wang, J., He, J. and Zhao, C. (2024), “A clustering mining method for sports behavior characteristics of athletes based on the ant colony optimization”, Heliyon, Elsevier Ltd, Vol. 10 No. 12, p. e33297, doi: 10.1016/j.heliyon.2024.e33297.
  17. Zhang, Z., Liu, H. and Wu, Z. (2023), “Student Profile Clustering Based Personalized Exercise Recommendation: Taking Data Structures Course as an Example”, 2023 IEEE International Conference on Advanced Learning Technologies (ICALT), IEEE, pp. 68–70, doi: 10.1109/ICALT58122.2023.00026.
  18. Zhao, K., Dai, Y., Jia, Z. and Ji, Y. (2022), “General Fuzzy C-Means Clustering Strategy : Using Objective Function to Control Fuzziness of”, IEEE Transaction on Fuzzy System, Vol. 30 No. 9, pp. 3601–3616, doi: 10.1109/TFUZZ.2021.3119240.