Clustering Academic Data of Junior High School Students to Identify Learning Groups Using The DBSCAN Algorithm at SMP Muhammadiyah 5 Samarinda
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Mini H
*
STMIK Widya Cipta Dharma,
Indonesia
- Siti Lailiyah STMIK Widya Cipta Dharma, Indonesia
- Salmon STMIK Widya Cipta Dharma, Indonesia
Abstract
The formation of study groups at the junior high school level plays an important role in improving the quality of learning and promoting equality in student learning outcomes. However, the process of grouping students is still largely carried out manually based on teachers’ intuition, subjective observations, or attendance data, which may lead to mismatches in students’ abilities and hinder the optimal achievement of learning objectives within the school environment. This study aims to identify study groups based on students’ academic data at SMP Muhammadiyah 5 Samarinda. The data used include scores in science (exact) and non-science (non-exact) subjects, exam results, assignment scores, attendance records, and parents’ educational backgrounds. The research stages consist of data cleaning, feature engineering, standardization, the application of the DBSCAN algorithm, and evaluation using the Silhouette Score. The analysis results reveal three main clusters: cluster 0 with 89 students (medium achievement), cluster 1 with 50 students (high achievement), and cluster 2 with 5 students (low achievement). In addition, 14 students (8.9%) were identified as noise. The Silhouette Score value of 0.217 indicates that the cluster separation quality is relatively weak; however, DBSCAN successfully detected outliers that may not be identified by other algorithms. These findings suggest that, although the cluster quality is not yet optimal, the applied algorithm remains useful for exploring students’ learning patterns and can serve as a basis for more targeted learning interventions.
References
Nureki, Syamsuria, and Emmi Azis, “Pengaruh kelompok belajar terhadap peningkatan hasil belajar siswa,” BJPM, vol. 2, no. 1, pp. 293–301, Feb. 2024.
M. Jannah, F. Abdi Alam, and T. Taufik, “Pengaruh layanan bimbingan kelompok dalam meningkatkan disiplin belajar siswa UPTD SMP Negeri 33 Barru,” JBK, vol. 10, no. 1, pp. 27–38, Oct. 2023.
R. K. Hapsari, T. Indriyani, and D. H. Sulaksono, Buku Ajar Data Mining. Yogyakarta, Indonesia: Deepublish, 2025.
M. Mustika et al., Data Mining dan Aplikasinya, R. N. Rismawati, Ed. Bandung, Indonesia: CV Widina Media Utama, 2021.
Y. F. Sinurat et al., Data Mining Pengelompokan Siswa Berprestasi Menggunakan Metode Clustering. Jakarta, Indonesia: Penerbit NEM, 2024.
I. W. A. Suputra, I. M. Candiasa, and I. P. P. Suryawan, “Klasterisasi hasil ujian nasional SMA/MA dengan algoritma K-Means,” Wahana Matematika dan Sains: Jurnal Matematika, Sains, dan Pembelajarannya, vol. 15, no. 1, Mar. 2021.
M. Melizah, A. A. T. Susilo, N. Lestari, and E. Elmayati, “Implementasi algoritma K-Means clustering untuk analisis data nilai akademik mahasiswa,” Jurnal Teknologi Informasi dan Komputer; Jurnal Teknologi Informasi Mura, vol. 5, no. 2, Des. 2024.
A. A. Mila, R. T. Abineno, and A. A. Pekuwali, “Pengelompokan performa siswa dalam pembelajaran Bahasa Indonesia menggunakan algoritma K-Means clustering di SMPN Satap Lambakara,”, In Prosiding Seminar Nasional SATI,Vol. 3, No. 1, pp. 593-604.
A. Yudistira and R. Andika, “Pengelompokan data nilai siswa menggunakan metode K-Means clustering,” JAITI, vol. 1, no. 1, pp. 20–28, Mar. 2023, doi: 10.58602/jaiti.v1i1.22.
M. Syaefudulloh, A. Faqih, and F. Basysyar, “Clustering kelompok belajar siswa berdasarkan hasil ujian sekolah menggunakan algoritma K-Means,” JS, vol. 10, no. 1, pp. 195–199, Apr. 2022, doi: 10.47024/js.v10i1.397.
S. Suraya, M. Sholeh, and D. Andayati, “Penerapan metode clustering dengan algoritma K-Means pada pengelompokan indeks prestasi akademik mahasiswa,” SKANIKA, vol. 6, no. 1, pp. 51–60, Jan. 2023, doi: 10.36080/skanika.v6i1.2982.
E. R. K. Haryono, S. Lailiyah, and M. I. Sa’ad, “Implementation of data clustering for Informatics Engineering Study Program students at STMIK Widya Cipta Dharma using the K-Means method,” Sebatik, vol. 29, no. 1, Jun. 2025.
M. S. Hasibuan, A. H. Lubis, and M. N. Sari, “Perbandingan algoritma clustering DBSCAN dan K-Means dalam pengelompokan siswa terbaik,” INFOTECH: Jurnal Informatika, vol. 10, no. 2, pp. 101–110, 2024.
Andri and E. Riswanto, “Pengelompokkan jumlah kunjungan mahasiswa ke perpustakaan kampus menggunakan algoritma DBSCAN,” G-Tech, vol. 7, no. 1, pp. 75–81, Jan. 2023, doi: 10.33379/gtech.v7i1.1925.
H. Pratiwi, M. I. Sa’ad, and Salmon, “Strategi manajemen pendidikan berbasis machine learning untuk prediksi prestasi siswa,” BEduManagers Journal: Borneo Educational Management and Research Journal, vol. 6, no. 1, 2025, doi: 10.30872/bedu.v6i1.5016.
A. R. N. Toyyibin and Z. Fatah, Trans., “Analisis data mining menggunakan metode clustering terhadap prestasi siswa I’dadiyah Sukorejo,” JIMI, vol. 2, no. 1, pp. 96–105, Feb. 2025, doi: 10.69714/remqnx91.
M. Sholeh and K. Aeni, “Perbandingan evaluasi metode Davies–Bouldin, Elbow dan Silhouette pada model clustering dengan menggunakan algoritma K-Means,” STRING: Satuan Tulisan Riset dan Inovasi Teknologi, vol. 8, no. 1, 2023.
Y. Hasan, “Pengukuran Silhouette Score dan Davies-Bouldin Index pada Hasil Cluster K-Means dan DBSCAN”, KAKIFIKOM , vol. 6, no. 1, pp. 60–74, Apr. 2024.
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