Clustering of High School Students Academic Scores Using K-Means Algorithm
DOI:
https://doi.org/10.51519/journalisi.v7i1.1029Keywords:
clustering, data mining, scores, K-Means.Abstract
The clustering of student subject scores in senior high school is conducted using the K-Means Clustering algorithm. The issue addressed in this study is how to optimally group students based on their academic scores to help schools understand the distribution of student abilities. This clustering is essential as a foundation for evaluating and improving the learning system. The research methodology includes data collection and preprocessing, determining the optimal number of clusters using the Davies-Bouldin Index (DBI), and applying the K-Means Clustering algorithm. The analysis results indicate that the optimal number of clusters is three, with an average DBI value of 1.226. Cluster 0 is categorized as "very good" (46 students), Cluster 1 as "good" (70 students), and Cluster 2 as "less good" (51 students).The clustering results can be utilized for more targeted learning interventions and curriculum adjustments. Schools can implement remedial programs or additional classes for students in the "less good" cluster to improve their academic performance. Meanwhile, students in the "very good" cluster can be provided with advanced learning materials or opportunities to participate in academic competitions. Additionally, clustering outcomes provide valuable insights for refining teaching strategies, allocating resources more effectively, and personalizing learning approaches to suit each student's needs. Furthermore, these clustering results support academic decision-making by enabling educators and administrators to identify student performance trends and address potential learning gaps. This data-driven approach helps schools enhance overall educational quality by adapting teaching methods and policies based on empirical findings.
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References
S. Ujud, T. D. Nur, Y. Yusuf, N. Saibi, and M. R. Ramli, “Penerapan Model Pembelajaran Discovery Learning Untuk Meningkatkan Hasil Belajar Siswa Sma Negeri 10 Kota Ternate Kelas X Pada Materi Pencemaran Lingkungan,” J. Bioedukasi, vol. 6, no. 2, pp. 337–347, 2023, doi: 10.33387/bioedu.v6i2.7305.
R. Kurniawan, M. M. M. Mukarrobin, and M. Mahradianur, “Klasterisasi Tingkat Pendidikan Di Dki Jakarta Pada Tingkat Kecamatan Menggunakan Algoritma K-Means,” Technol. J. Ilm., vol. 12, no. 4, p. 234, 2021, doi: 10.31602/tji.v12i4.5633.
B. Harahap and A. Rambe, “Implementasi K-Means Clustering Terhadap Mahasiswa yang Menerima Beasiswa Yayasan Pendidikan Battuta di Universitas Battuta Tahun 2020/2021 Studi Kasus Prodi Informatika,” Informatika, vol. 9, no. 3, pp. 90–97, 2021, doi: 10.36987/informatika.v9i3.2185.
D. Leman and E. Syahrin, “Sistem Cerdas Rekomendasi Klinik Pratama di Kota Medan Berbasis Data Mining Dengan Metode K-Means Untuk Pasien BPJS dan Umum,” no. September, pp. 204–214, 2024.
F. Asril, Pengembangan Kecerdasan Majemuk pada Pembelajaran Tematik Kelas V di MI Modern Al Azhary Ajibarang, Doctoral dissertation, UIN Prof. KH Saefuddin Zuhri, 2023.
J. Hutagalung, “Pemetaan Siswa Kelas Unggulan Menggunakan Algoritma K-Means Clustering,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 1, pp. 606–620, 2022, doi: 10.35957/jatisi.v9i1.1516.
A. Sulistiyawati and E. Supriyanto, “Implementasi Algoritma K-means Clustring dalam Penetuan Siswa Kelas Unggulan,” J. Tekno Kompak, vol. 15, no. 2, p. 25, 2021, doi: 10.33365/jtk.v15i2.1162.
M. Annas and S. N. Wahab, “Data Mining Methods: K-Means Clustering Algorithms,” Int. J. Cyber IT Serv. Manag., vol. 3, no. 1, pp. 40–47, 2023.
I. D. Setiawan and A. Triayudi, “Penerapan Data Mining Dengan Menggunakan Algoritma Clustering K-Means Untuk Pembagian Jurusan Pada Sekolah Menengah Atas,” J. Comput. Syst. Informatics, vol. 5, no. 2, pp. 380–392, 2024, doi: 10.47065/josyc.v5i2.4970.
Y. Andini, J. T. Hardinata, and Y. P. Purba, “Penerapan Data Mining Terhadap Tata Letak Buku Di Perpustakaan Sintong Bingei Pematangsiantar Menggunakan Metode Apriori,” J. TIMES, vol. 11, no. 1, pp. 9–15, 2022, doi: 10.51351/jtm.11.1.2022661.
N. Hendrastuty, “Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Dalam Evaluasi Hasil Pembelajaran Siswa,” J. Ilm. Inform. Dan Ilmu Komput., vol. 3, no. 1, pp. 46–56, 2024, doi: 10.58602/jima-ilkom.v3i1.26
N. Nursobah, S. Lailiyah, B. Harpad, and M. Fahmi, “Penerapan Data Mining Untuk Prediksi Perkiraan Hujan dengan Menggunakan Algoritma K-Nearest Neighbor,” Build. Informatics, Technol. Sci., vol. 4, no. 3, 2022, doi: 10.47065/bits.v4i3.2564.
S. N. Br Sembiring, H. Winata, and S. Kusnasari, “Pengelompokan Prestasi Siswa Menggunakan Algoritma K-Means,” J. Sist. Inf. Triguna Dharma (JURSI TGD), vol. 1, no. 1, p. 31, 2022, doi: 10.53513/jursi.v1i1.4784.
A. Yudhistira and R. Andika, “Pengelompokan Data Nilai Siswa Menggunakan Metode K-Means Clustering,” J. Artif. Intell. Technol. Inf., vol. 1, no. 1, pp. 20–28, 2023, doi: 10.58602/jaiti.v1i1.22.
S. Anwar, T. Suprapti, G. Dwilestari, and I. Ali, “Pengelompokkan Hasil Belajar Siswa dengan Metode Clustering K-Means,” JURSISTEKNI (Jurnal Sist. Inf. dan Teknol. Informasi), vol. 4, no. 2, pp. 60–72, 2022.
… Preddy, P. Marpaung, I. Pebrian, and W. Putri, “Penerapan Data Mining Untuk Pengelompokan Kepadatan Penduduk Kabupaten Deli Serdang Menggunakan Algoritma K-Means,” J. Ilmu Komput. dan Sist. Inf., vol. 6, no. 2, pp. 64–70, 2023.
Suharmanto, W. S. Utami, N. Pratiwi, and F. Muhammad, “Penerapan Data Mining Menggunakan Algoritma K-Means Untuk Clustering Perokok Usia Lebih dari 15 Tahun,” Bull. Inf. Technol., vol. 4, no. 4, pp. 501–507, 2023, doi: 10.47065/bit.v4i4.1067.
R. Mauliadi, “Data Mining Menggunakan Algoritma K-Means Clustering dalam Analisis Tingkat Potongan Harga Terhadap Harga Jual Sepeda Motor Honda,” J. Inform. Ekon. Bisnis, vol. 4, pp. 7–9, 2022, doi: 10.37034/infeb.v4i4.156.
A. Supriyadi, A. Triayudi, and I. D. Sholihati, “Perbandingan Algoritma K-Means Dengan K-Medoids Pada Pengelompokan Armada Kendaraan Truk Berdasarkan Produktivitas,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 6, no. 2, pp. 229–240, 2021, doi: 10.29100/jipi.v6i2.2008.
N. R. Saputra, G. Z. Muflih, and T. Informatika, “Pengelompokan Wilayah Indonesia Berdasarkan Komponen Indeks Pembangunan Manusia dengan Pendekatan Algoritma K-Means Clustering,” vol. 8, pp. 156–167, 2025.
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