Penerapan Algoritma Decision Tree untuk Klasifikasi Prestasi Akademik Mahasiswa Berdasarkan Indeks Prestasi Semester dan Kumulatif
-
Nahot Marganda Simanjuntak
*
Universitas HKBP Nommensen Pematangsiantar, Sumatera Utara,
Indonesia
(*) Corresponding Author
Abstract
Higher education institutions generally have not optimally utilized student academic data as an early detection tool, so students at risk of declining academic performance are often identified too late. This study addresses this problem by applying the Decision Tree algorithm, based on entropy and information gain calculations, to classify student academic performance into three categories (Good, Sufficient, and Poor) using Grade Point per Semester (IPS1–IPS8) and Cumulative Grade Point Average (IPK) features from 518 student records. The purpose of this study is to build a classification model that is both accurate and interpretable, so that its contribution can be used by universities as a transparent decision-support tool for early academic intervention, unlike black-box models that are difficult to explain. The dataset was divided using a 70:30 training-testing ratio. Preliminary results show that the IPK attribute is the most influential factor, with the highest Information Gain of 0.8766, selected as the root node, while model evaluation on the testing data using a confusion matrix yields an accuracy of 96.15%, precision 94.71%, recall 95.10%, and F1-score 94.87%. These results indicate that the constructed Decision Tree model is suitable for use as an early-detection tool for students requiring academic attention.
References
S. Citra Esananda, B. Nugroho, and F. Tri Anggraeny, “PENERAPAN ALGORITMA DECISION TREE DALAM MENENTUKAN PRESTASI AKADEMIK SISWA,” 2021.
U. Indahyanti, N. L. Azizah, and H. Setiawan, “Educational Data Mining on Student Academic Performance Prediction: A Survey Educational Data Mining Pada Prediksi Kinerja Akademik Mahasiswa : Sebuah Survey,” 2022. [Online]. Available: https://ieeexplore.ieee.org/
A. Rahman, “Klasifikasi Performa Akademik Siswa Menggunakan Metode Decision Tree dan Naive Bayes,” Jurnal SAINTEKOM, vol. 13, no. 1, pp. 22–31, Mar. 2023, doi: 10.33020/saintekom.v13i1.349.
M. Chen and Z. Liu, “Predicting performance of students by optimizing tree components of random forest using genetic algorithm,” Heliyon, vol. 10, no. 12, Jun. 2024, doi: 10.1016/j.heliyon.2024.e32570.
Riska Rismaya, Dwi Yuniarto, and David Setiadi, “Penerapan Algoritma Machine Learning dalam Prediksi Prestasi Akademik Mahasiswa,” Router : Jurnal Teknik Informatika dan Terapan, vol. 3, no. 1, pp. 15–23, Feb. 2025, doi: 10.62951/router.v3i1.389.
M. Imran, S. Latif, D. Mehmood, and M. S. Shah, “Student academic performance prediction using supervised learning techniques,” International Journal of Emerging Technologies in Learning, vol. 14, no. 14, pp. 92–104, 2019, doi: 10.3991/ijet.v14i14.10310.
M. N. Gul, W. Abbasi, and M. Y. Wani, “Revolutionizing educational decision-making: a robust machine learning mechanism for predicting student performance,” Journal of Electrical Systems and Information Technology, vol. 12, no. 1, Jun. 2025, doi: 10.1186/s43067-025-00230-z.
T. Suprapti, B. Nurhakim, B. Warni Ayu Hermina, and V. A. Syahputra Simbolon, “A Decision Tree Model with Grid Search Optimization for Scholarship Recipient Classification,” Jurnal Teknik Informatika (Jutif), vol. 6, no. 5, pp. 3800–3813, Oct. 2025, doi: 10.52436/1.jutif.2025.6.5.5235.
M. M. Hussain, S. Akbar, S. A. Hassan, M. W. Aziz, and F. Urooj, “Prediction of Student’s Academic Performance through Data Mining Approach,” Journal of Informatics and Web Engineering, vol. 3, no. 1, pp. 241–251, Feb. 2024, doi: 10.33093/jiwe.2024.3.1.16.
“15”.
Z. Fatah and S. Bella, “Klasifikasi Status Akademik Mahasiswa Menggunakan Decision Tree.”
R. Hidayat, H. Gultom, and Y. Samudra, “Predicting Student Academic Performance Using Learning Activity Data: A Comparative Study of Random Forest and Decision Tree Models,” vol. 4, no. 3, pp. 2829–2529, 2025, doi: 10.52158/rjti.v4i3.4032.
M. B. Handoko, Z. W. Fauzi, H. Agung, and D. Sofia, “Prediction of Science and Social Science Students Interests Using Decision Tree Algorithm with CRISP-DM,” bit-Tech, vol. 8, no. 1, pp. 437–447, Aug. 2025, doi: 10.32877/bt.v8i1.2577.
S. Malik et al., “Advancing educational data mining for enhanced student performance prediction: a fusion of feature selection algorithms and classification techniques with dynamic feature ensemble evolution,” Sci. Rep., vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-92324-x.
U. Al Faruq, M. Ainun Naja Fauzi, I. Fatayasya, E. Daniati, A. Ristyawan, and N. PGRI Kediri, “Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) 131 Prediksi Data Kelulusan Mahasiswa Dengan Metode Decision Tree menggunakan Rapidminer,” Online, 2023.
T. Trisna, A. Putri, R. Rahmadani, R. Siregar, and H. Hasan, “Academic Performance Prediction of PTIK Students through Machine Learning Models at Universitas Negeri Medan,” Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE), vol. 7, no. 1, pp. 1128–1134, 2026, doi: 10.30596/jcositte.v7i1.29570.
N. Nailil Amani and U. Hayati, “PENGGUNAAN ALGORITMA DECISION TREE UNTUK PREDIKSI PRESTASI SISWA DI SEKOLAH DASAR NEGERI 3 BAYALANGU KIDUL,” 2024.
N. Khasanah, D. Uki, E. Saputri, T. Hidayat, F. Aziz, and U. N. Mandiri, “Prediksi Kelulusan Mahasiswa Menggunakan Algoritma C4.5 dengan RapidMiner: Studi Kasus Data Akademik Perguruan Tinggi XYZ,” Journal Computer Science, vol. 4, no. 2, 2025.
A. Hidayahtulloh and D. Prasetyo, “Penerapan Algoritma Decision Tree Untuk Prediksi Kelulusan Mahasiswa Berdasarkan Data Akademik.” [Online]. Available: https://jurnalmahasiswa.com/index.php/teknobis
Dofiyanto and Z. Fatah, “Penerapan Algoritma Decision Tree untuk Klasifikasi Kelulusan Mahasiswa Berdasarkan Faktor Akademik dan Sosial,” JISCO : Journal of Information System and Computing, vol. 3, no. 2, pp. 66–76, Dec. 2025, doi: 10.30631/jisco.v3i2.4030.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Penerapan Algoritma Decision Tree untuk Klasifikasi Prestasi Akademik Mahasiswa Berdasarkan Indeks Prestasi Semester dan Kumulatif
Pages: 146 - 154
Copyright (c) 2026 Nahot Marganda Simanjuntak
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).
