Klasterisasi Siswa Berdasarkan Profil Akademik dan Karakteristik Belajar Menggunakan Algoritma K-Means untuk Mendukung Pembelajaran
-
Attaya Faiharani
*
Universitas Buana Perjuangan Karawang, Karawang,
Indonesia
- Baenil Huda Universitas Buana Perjuangan Karawang, Karawang, Indonesia
- Fitria Nurapriani Universitas Buana Perjuangan Karawang, Karawang, Indonesia
- April Lia Hananto Universitas Buana Perjuangan Karawang, Karawang, Indonesia (*) Corresponding Author
Abstract
Grouping students based on academic and non-academic characteristics is important to support the development of more targeted educational guidance strategies in schools. The main problem addressed in this study is the absence of objective data-based student mapping, which causes development programs to remain general and less targeted. This study aims to classify students using the K-Means clustering algorithm based on academic profiles and other supporting variables, and to evaluate cluster quality using the silhouette coefficient method. The research stages include data preprocessing, determining the optimal number of clusters, clustering using K-Means, and evaluating the clustering result. The results showed that four clusters were selected as the final configuration with a silhouette score of 0,1093, with cluster membership distributed into 12, 4, 2, and 2 students. Visualization using principal component analysis shows that most clusters are sufficiently well separeted. This study contributes a data-driven student grouping model that can be used as a basis for recommending student potential development according to the characteristics of each group.
Downloads
References
B. Huda, A. S. Amin, F. Nurapriani, and A. Damuri, “Aplikasi Monitoring Perkembangan Edukasi Anak Usia Dini Berbasis Web,” Jurnal Informatika Utama, vol. 1, no. 1, Jun. 2023, doi: 10.55903/jitu.v1i1.70.
F. Nurapriani et al., “Meningkatkan Kemampuan Berpikir Kritis dan Komunikasi Matematik Mahasiswa Sistem Informasi dengan Pembelajaran Missouri Mathematics Project (MMP),” RADIAN Journal: Research, vol. 4, no. 3, Sep. 2025, doi: 10.35706/radian.v4i3.13152.
S. N. Adzra, F. N. Hasan, and A. Y. Kuntoro, “Penerapan Data Mining dalam Penilaian Kinerja Akademik Siswa/I SMP YPI Pulogadung dengan Metode K-Means Clustering,” Jurnal Ilmiah Informatika, vol. 13, no. 2, 2025, doi: 10.33884/jif.v13i02.10396.
Z. Fatah and S. Maghfiroh, “Analisis Data Mining dengan Algoritma K-Means Clustering untuk Menentukan Siswa Berprestasi di MTs Miftahul Ulum Bengkak,” JAMASTIKA: Jurnal Mahasiswa Teknik Informatika, vol. 4, no. 2, Oct. 2025, doi: 10.35473/jamastika.v4i2.4527.
N. D. Rahayu, A. H. Anshor, I. Afriantoro, and A. Halim Anshor, “Penerapan Data Mining untuk Pemetaan Siswa Berprestasi menggunakan Metode Clustering K-Means,” JUKI: Jurnal Komputer dan Informatika, vol. 6, no. 1, 2024, doi: 10.53842/juki.v6i1.474.
N. Suarna, N. Rahaningsih, and A. A. Suarna, “Optimalisasi Prestasi Akademik Siswa Melalui Pengelompokan Indeks Prestasi Dengan K-Means Clustering,” Jurnal Kecerdasan Buatan dan Teknologi Informasi, vol. 4, no. 2, May 2025, doi: 10.69916/jkbti.v4i2.321.
N. Wahyudi, Y. Ardilla, and N. P. Hastuti, “Educational Data Clustering Menggunakan K-Means pada Seleksi Penerimaan Peserta Didik Baru Madrasah Aliyah Negeri Unggulan,” Information System and Informatics Journal, vol. 7, no. 2, Dec. 2022, doi: 10.29080/systemic.v7i2.1768.
D. A. Fajar Martha Subqi, “Data Mining Untuk Pemeliharaan Prediktif Mesin Produksi berdasarkan Database Kerusakan Mesin menggunakan Naïve Bayes Classifier,” Jurnal Ilmiah Komputasi, vol. 20, no. 2, Jun. 2021, doi: 10.32409/jikstik.20.2.368.
L. Fabrianto, F. Riza, and N. M. Faizah, “Korelasi Antara Profil dan Nilai Akademis Siswa Dengan Menggunakan Algoritma K-Means,” JTIIK: Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 8, no. 4, Aug. 2021, doi: 10.25126/jtiik.202183034.
D. Oktario Dacwanda and Y. Nataliani, “Implementasi K-Means Clustering untuk Analisis Nilai Akademik Siswa Berdasarkan Nilai Pengetahuan dan Keterampilan,” AITI: Jurnal Teknologi Informasi, vol. 18, no. 2, Aug. 2021, doi: 10.24246/aiti.v18i2.125-138.
Awalia Rifqa, “Penerapan Metode K-Means Clustering untuk Pengelompokan Prestasi Siswa Menggunakan Orange Data Mining: Studi Kasus di MTs Muhammadiyah Tallo Makassar,” MAPLE: Mechatronics Journal in Professional and Enterpreneur, vol. 6, no. 2, Dec. 2024, doi: 10.61141/maple.v6i2.579.
N. A. Rizki, K. Kurniawan, I. K. Hasan, and N. Sampe, “Implementasi Algoritma K-Means Untuk Mengelompokkan Mahasiswa Berdasarkan Sumber Belajarnya,” METIK: Media Teknologi Informasi dan Komputer, vol. 7, no. 2, Dec. 2023, doi: 10.47002/metik.v7i2.584.
M. Norshahlan, H. Jaya, and R. Kustini, “Penerapan Metode Clustering Dengan Algoritma K-means Pada Pengelompokan Data Calon Siswa Baru,” Jurnal Sistem Informasi Triguna Dharma, vol. 2, no. 6, Nov. 2023, doi: 10.53513/jursi.v2i6.9148.
A. R. Lashiyanti, I. Rasyid Munthe, F. A. Nasution, and E. P. Korespondensi, “Optimisasi Klasterisasi Nilai Ujian Nasional Dengan Pendekatan Algoritma K-Means, Elbow, dan Silhouette,” JIKOMSI: Jurnal Ilmu Komputer dan Sistem Informasi, vol. 6, no. 1, pp. 14–20, Mar. 2023, Accessed: Apr. 2026. [Online]. Available: https://ejournal.sisfokomtek.org/index.php/jikom/article/view/1550.
K. Setiawan, Y. Yanti, and A. Saputry, “Clustering Data Calon Siswa Baru Menggunakan Metode K-Means di Pusat Pengembangan Anak Fajar Baru Cengkareng,” JTIK: Jurnal Teknologi Informasi dan Komunikasi, vol. 8, no. 1, 2024, doi: 10.35870/jtik.v8i1.1426.
M. Milla, V. Aprila Kore Dima, and A. Purnami Setiawi, “Penerapan Algoritma K-Means untuk Mengidentifikasi Minat Belajar Siswa di Sekolah Dasar Negeri Puu Naga,” Jurnal Informatika dan Sains Teknologi, vol. 3, no. 4, 2025, doi: 10.62951/modem.v3i4.630.
S. Rahmah, M. Jamil, A. Aslindah, A. Fawait, and Y. F. Saputra, “Pengelompokan Hasil Pembelajaran Mahasiswa dengan Algoritma K-Means Clustering,” JERKIN: Jurnal Pengabdian Masyarakat dan Riset Pendidikan, vol. 3, no. 4, pp. 4215–4221, Jun. 2025, doi: 10.31004/jerkin.v3i4.1217.
N. B. Putri and A. W. Wijayanto, “Analisis Komparasi Algoritma Klasifikasi Data Mining Dalam Klasifikasi Website Phishing,” KOMPUTIKA: Jurnal Sistem Komputer, vol. 11, no. 1, Jan. 2022, doi: 10.34010/komputika.v11i1.4350.
Y. N. Dewi, H. Rianto, C. Budihartanti, and F. W. Fibriany, “Penerapan Metode K-Means Dalam Menentukan Kelompok Pendalaman Materi Ujian Nasional,” Journal of Information System, Applied, Management, Accounting and Research, vol. 6, no. 1, Feb. 2022, doi: 10.52362/jisamar.v6i1.670.
A. Fadlil, I. Riadi, and Y. Mulyana, “Penerapan Algoritma K-Means pada Pengelompokan Data Pendaftar Bantuan Biaya Pendidikan,” JTIK: Jurnal Teknologi Informasi dan Komputer, vol. 8, no. 2, pp. 352–366, Sep. 2022, doi: 10.37012/jtik.v8i2.1261.
R. Sudrajat, A. I. Hadiana, and M. Melina, “Evaluasi Kualitas Klaster Wilayah Rawan Bencana Menggunakan K-Means dengan Silhouette dan Elbow Method,” Jurnal Algoritma, vol. 22, no. 2, pp. 127–139, Nov. 2025, doi: 10.33364/algoritma/v.22-2.2379.
N. Nurahman, A. Purwanto, and S. Mulyanto, “Klasterisasi Sekolah Menggunakan Algoritma K-Means berdasarkan Fasilitas, Pendidik, dan Tenaga Pendidik,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 2, pp. 337–350, Mar. 2022, doi: 10.30812/matrik.v21i2.1411.
M. Qusyairi, Zul Hidayatullah, and Arnila Sandi, “Penerapan K-Means Clustering Dalam Pengelompokan Prestasi Siswa Dengan Optimasi Metode Elbow,” INFOTEK: Jurnal Informatika dan Teknologi, vol. 7, no. 2, pp. 500–510, Jul. 2024, doi: 10.29408/jit.v7i2.26375.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Klasterisasi Siswa Berdasarkan Profil Akademik dan Karakteristik Belajar Menggunakan Algoritma K-Means untuk Mendukung Pembelajaran
Pages: 718-276
Copyright (c) 2026 Attaya Faiharani, Baenil Huda, Fitria Nurapriani, April Lia Hananto
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
