Classification of MAN 2 Labuhanbatu Student Achievement Through Learning Achievement Index Components Using K-Means Clustering
DOI:
https://doi.org/10.65371/metrokom.v2i2.135Keywords:
K-Means Clustering, Educational Data Mining, Learning Group, Student PerformanceAbstract
Optimal utilization of academic data is an important requirement in supporting data-based learning decision making. One approach that can be used is Educational Data Mining (EDM) through clustering techniques to map students' academic abilities. This study aims to apply the K-Means Clustering algorithm in grouping students based on exam score patterns in one subject at MAN 2 Labuhanbatu Utara. The data used consists of daily scores, midterm scores, and final exam scores of 11th grade students, which were processed through pre-processing, data normalization, and clustering analysis stages. The determination of the optimal number of clusters was carried out using the Elbow method with the Within Cluster Sum of Squares (WCSS) indicator. The results showed that the three-cluster configuration was the most representative grouping structure, which could be interpreted as groups of students with high, medium, and low academic performance, respectively. The differences in centroid values between clusters indicate significant and structured variations in academic achievement. These findings prove that the K-Means algorithm is effective for mapping student learning groups objectively without requiring initial labels. The clustering results are expected to serve as a basis for teachers and schools in designing more adaptive learning strategies tailored to students' ability characteristics.
References
Adzra, S. N., Hasan, F. N., & Kuntoro, A. Y. (2025). Salsa Nabilatul Adzra Penerapan Data Mining dalam Penerapan Data Mining dalam Penilaian Kinerja Akademik Siswa/I SMP YPI Pulogadung dengan Metode K-Means Clustering. Jurnal Ilmiah informatika (Vol. 98). http://ejournal.upbatam.ac.id/index.php/jif
Alalawi, S. J., Shaharanee, I. N., & Jamil, J. mohd. (2023). CLUSTERING STUDENT PERFORMANCE DATA USING k-MEANS ALGORITHMS. Journal of Computational Innovation and Analytics (JCIA), 2(1), 41–55. https://doi.org/10.32890/jcia2023.2.1.3
Alamgir, Z., Akram, H., Karim, S., & Wali, A. (2024a). Enhancing Student Performance Prediction via Educational Data Mining on Academic Data. Informatics in Education, 23(1), 1–24. https://doi.org/10.15388/infedu.2024.04
Alamgir, Z., Akram, H., Karim, S., & Wali, A. (2024). Enhancing Student Performance Prediction via Educational Data Mining on Academic Data. Informatics in Education, 23(1), 1–24. https://doi.org/10.15388/infedu.2024.04
Apriandi, D., Sari, R. M., & Sarif, M. I. (2024). Analisis Clustering Untuk Menentukan Siswa Berprestasi di SMK Swasta TI Panca Dharma Stungkit Menggunakan Metode K-Means. Jurnal Minfo Polgan, 13(1), 1117–1129. https://doi.org/10.33395/jmp.v13i1.13959
Darsono, V., & Andrianti, A. (2022). Jurnal Informatika Dan Rekayasa Komputer (JAKAKOM) Penerapan Data Mining Algoritma K-Means Untuk Rekomendasi Pemilihan Bidang Studi Perguruan Tinggi Pada Siswa SMKN 1 Kota Jambi. https://ejournal.unama.ac.id/index.php/jakakom
Habibie, A. F., & R, R. K. (2024). Implementasi K-Means Clustering dalam Mengklasifikasi Pengaruh Les Terhadap Prestasi Siswa dengan Metode Elbow. Journal of Computer System and Informatics (JoSYC), 6(1), 133–147. https://doi.org/10.47065/josyc.v6i1.6201
Hendrastuty, N. (2024). Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Dalam Evaluasi Hasil Pembelajaran Siswa. Jurnal Ilmiah Informatika Dan Ilmu Komputer (JIMA-ILKOM), 3(1), 46–56. https://doi.org/10.58602/jima-ilkom.v3i1.26
Kurniawan, R. P., & Ferdiansyah. (2023). 3 rd Seminar Nasional Mahasiswa Fakultas Teknologi Informasi (SENAFTI) 30 Agustus 2023-Jakarta. 2(2).
Maqsood, R., Ceravolo, P., Ahmad, M., & Sarfraz, M. S. (2023). Examining students’ course trajectories using data mining and visualization approaches. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00423-4
Nurahman, N., Purwanto, A., & Mulyanto, S. (2022). Klasterisasi Sekolah Menggunakan Algoritma K-Means berdasarkan Fasilitas, Pendidik, dan Tenaga Pendidik. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(2), 337–350. https://doi.org/10.30812/matrik.v21i2.1411
Rahma, F. A., & Ulfah, S. Z. (2025). Clustering Students Based on Academic Performance and Social Factors: An Unsupervised Learning Approach to Identify Student Patterns. International Journal for Applied Information Management, 5(3), 139–154. https://doi.org/10.47738/ijaim.v5i3.109
Risal, A. A. N., Andayani, D. D., Suherman, M. I., & Kaswar, A. B. (2024). Utilizing the K-Means Clustering Algorithm for Utilizing the K-Means Clustering Algorithm for Analyzing Student Achievement Assessment at SMK Negeri 1 Gowa.
Romlah, S. (2023). Management of Students Potential Development Using the Data Mining Clustering Method in MAN 2 Malang City. QALAMUNA: Jurnal Pendidikan, Sosial, Dan Agama, 15(1), 95–110. https://doi.org/10.37680/qalamuna.v15i1.2221
Yağcı, M. (2022). Educational data mining: prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9(1). https://doi.org/10.1186/s40561-022-00192-z
