Prediksi Siswa Berprestasi di SMP Negeri 4 Kediri Menggunakan Algoritma Random Forest
DOI:
https://doi.org/10.29407/j6z2n208Abstract
Abstrak—Penentuan siswa berprestasi merupakan kegiatan penting di sekolah karena dapat menjadi dasar dalam pembinaan dan pemberian penghargaan kepada siswa. Namun, proses penilaian yang masih dilakukan secara manual sering membutuhkan waktu lama dan berpotensi menghasilkan keputusan yang kurang objektif. Penelitian ini menerapkan algoritma Random Forest pada sistem prediksi siswa berprestasi di SMP Negeri 4 Kediri. Data yang digunakan meliputi nilai rapor, kehadiran, kedisiplinan, prestasi lomba, dan keaktifan ekstrakurikuler. Tahapan penelitian meliputi pengumpulan data, preprocessing, pelatihan model, pengujian, evaluasi, dan implementasi sistem berbasis web. Hasil evaluasi menunjukkan bahwa model memperoleh accuracy sebesar 87,50%, precision sebesar 91,67%, recall sebesar 78,57%, dan F1-score sebesar 84,62%. Hasil tersebut menunjukkan bahwa sistem dapat membantu sekolah mengidentifikasi siswa berprestasi secara lebih cepat, objektif, dan berbasis data.
Keywords:
Kata Kunci— klasifikasi, machine learning, prediksi, Random Forest, siswa berprestasi##plugins.themes.default.displayStats.downloads##
References
[1] D. Hartina and A. Siahaan, "Manajemen Kegiatan Ekstrakurikuler dalam Meningkatkan Prestasi Non Akademik Siswa di SMA Negeri 1 Aek Natas," Journal of Education Research, vol. 5, no. 2, pp. 2024-2033, 2024, doi: 10.37985/jer.v5i2.1117.
[2] A. Muhaimin, M. Amin Hariyadi, and M. I. Imamudin, "Klasifikasi Prestasi Akademik Siswa Berdasarkan Nilai Rapor dan Kedisiplinan dengan Metode K-Nearest Neighbor," Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI), vol. 7, no. 1, pp. 193-202, 2024, doi: 10.55338/jikomsi.v7i1.2865.
[3] C.-G. Wong, G.-K. Tong, and S.-C. Haw, "Exploring Customer Segmentation in E-Commerce using RFM Analysis with Clustering Techniques," Journal of Telecommunications and the Digital Economy, vol. 12, no. 3, pp. 97-125, 2024, doi: 10.18080/jtde.v12n3.978.
[4] A. Ferdita Nugraha, R. F. A. Aziza, and Y. Pristyanto, "Penerapan metode Stacking dan Random Forest untuk Meningkatkan Kinerja Klasifikasi pada Proses Deteksi Web Phishing," Jurnal Infomedia, vol. 7, no. 1, p. 39, 2022, doi: 10.30811/jim.v7i1.2959.
[5] R. Amin and A. S. F. Utami, "Prediksi Nilai Ujian Berdasarkan Kebiasaan Siswa Menggunakan Algoritma Random Forest Regressor," INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS: Journal of Information System, vol. 10, no. 2, p. 149, 2025, doi: 10.51211/isbi.v10i2.3722.
[6] T. Pattiasina and D. Rosiyadi, "COMPARISON OF DATA MINING CLASSIFICATION ALGORITHM FOR PREDICTING THE PERFORMANCE OF HIGH SCHOOL STUDENTS," Jurnal Techno Nusa Mandiri, vol. 17, no. 1, pp. 22-30, 2020, doi: 10.33480/techno.v17i1.1226.
[7] S. Sobari, A. I. Purnamasari, A. Bahtiar, and K. Kaslani, "MENINGKATKAN MODEL PREDIKSI KELULUSAN SANTRI TAHFIDZ DI PONDOK PESANTREN AL-KAUTSAR MENGGUNAKAN ALGORITMA RANDOM FOREST," Jurnal Informatika dan Teknik Elektro Terapan, vol. 13, no. 1, 2025, doi: 10.23960/jitet.v13i1.5704.
[8] A. Darmawan, I. Yudhisari, A. Anwari, and M. Makruf, "Pola Prediksi Kelulusan Siswa Madrasah Aliyah Swasta dengan Support Vector Machine dan Random Forest," Jurnal Minfo Polgan, vol. 12, no. 1, pp. 387-400, 2023, doi: 10.33395/jmp.v12i1.12388.
[9] A. Sanjaya, Front-End Development: Membangun Antarmuka Pengguna yang interaktif dan Responsif. Malang, Indonesia: PT. Litnus, 2025.
[10] P. Ramadhan, Yuhandri, and J. Veri, "Eksplorasi Algoritma Decision Tree untuk Penentuan Siswa Berprestasi," bit-Tech, vol. 7, no. 3, pp. 826-833, 2025, doi: 10.32877/bt.v7i3.2210.
[11] H. Barus, I. N. Fajri, and Y. Pristyanto, "Sentiment Classification Analysis of Tokopedia Reviews Using TF-IDF, SMOTE, and Traditional Machine Learning Models," Journal of Applied Informatics and Computing, vol. 9, no. 5, pp. 2552-2561, 2025, doi: 10.30871/jaic.v9i5.10524.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Copyright on any article is retained by the author(s).
- The author grants the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution 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.
- The article and any associated published material is distributed under the Creative Commons Attribution-ShareAlike 4.0 International License
