Prediksi Kelulusan Tepat Waktu Mahasiswa Menggunakan Algoritma Random Forest Berdasarkan Data Akademik Mahasiswa
- Misaria Laia Institut Teknologi dan Bisnis Indonesia, Deli Serdang, Indonesia
- Adri Wahyu Institut Teknologi dan Bisnis Indonesia, Deli Serdang, Indonesia
- Montanus Montanus Institut Teknologi dan Bisnis Indonesia, Deli Serdang, Indonesia
- Rinda AS Siahaan Institut Teknologi dan Bisnis Indonesia, Deli Serdang, Indonesia
-
David JM Sembiring
*
Institut Teknologi dan Bisnis Indonesia, Deli Serdang,
Indonesia
- Sumarlin Sumarlin Institut Teknologi dan Bisnis Indonesia, Deli Serdang, Indonesia (*) Corresponding Author
Abstract
Ontime graduation is one of the important indicators for measuring students’ success in completing their education within the prescribed study period. Higher education institutions face challenges in optimally utilizing historical academic data to identify students who are potentially at risk of delayed graduation at an early stage. This condition may limit the effectiveness of academic monitoring and the provision of appropriate assistance. This study aims to apply the Random Forest algorithm to predict students’ on-time graduation status based on academic characteristics and to evaluate the model’s ability to distinguish between students predicted to graduate on time and those predicted not to graduate on time. The dataset consists of 300 student records with attributes including Semester 1 to Semester 4 GPA, cumulative GPA, total credits, number of repeated courses, attendance percentage, and academic status. The research stages include data collection, preprocessing, dataset splitting into training and testing sets using an 80:20 proportion, Random Forest model development using 100 Decision Trees, classification, and model evaluation using a confusion matrix, accuracy, precision, recall, and F1-score. The contribution of this study is to provide a machine learning-based approach that utilizes historical academic data to generate predictive information regarding students’ graduation status and support more systematic academic monitoring. Testing on 60 records produced 29 True Positives, 23 True Negatives, 3 False Positives, and 5 False Negatives, with an accuracy of 86.67%, precision of 90.63%, recall of 85.29%, and F1-score of 87.88%. These results indicate that Random Forest is capable of classifying students’ graduation status with satisfactory performance on the dataset used.
References
A. Putri et al., “Komparasi Algoritma K-NN, Naive Bayes dan SVM untuk Prediksi Kelulusan Mahasiswa Tingkat Akhir,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 3, no. 1, pp. 20–26, 2023, doi: 10.57152/malcom.v3i1.610.
G. Gunawan, H. Hanes, and C. Catherine, “C4.5, K-Nearest Neighbor, Naïve Bayes, and Random Forest Algorithms Comparison to Predict Students’ On Time Graduation,” Indones. J. Artif. Intell. Data Min., vol. 4, no. 2, pp. 62–71, 2021, doi: 10.24014/ijaidm.v4i2.10833.
N. Suresh, V. Hashiyana, G. T. Nhinda, I. Stephanus, and P. Kautwima, “Graduates’ Prediction System Using Artificial Intelligence,” in Proceedings of the International Conference on Data Science, Machine Learning and Artificial Intelligence, 2021, pp. 317–327. doi: 10.1145/3484824.3484873.
R. Rismayati, I. Ismarmiaty, and S. Hidayat, “Ensemble Implementation for Predicting Student Graduation with Classification Algorithm,” Int. J. Eng. Comput. Sci. Appl., vol. 1, no. 1, pp. 35–42, 2022, doi: 10.30812/ijecsa.v1i1.1805.
H. S. Brdesee, W. Alsaggaf, N. Aljohani, and S.-U. Hassan, “Predictive Model Using a Machine Learning Approach for Enhancing the Retention Rate of Students At-Risk,” Int. J. Semant. Web Inf. Syst., vol. 18, no. 1, pp. 1–21, 2022, doi: 10.4018/IJSWIS.299859.
M. A. S. Pawitra, H.-C. Hung, and H. Jati, “A Machine Learning Approach to Predicting On-Time Graduation in Indonesian Higher Education,” Elinvo (Electronics, Informatics, Vocat. Educ., vol. 9, no. 2, pp. 294–308, 2024, doi: 10.21831/elinvo.v9i2.77052.
N. V. Darenoh, F. A. Bachtiar, and R. S. Perdana, “Prediction of On-time Student Graduation with Deep Learning Method,” J. ICT Res. Appl., vol. 18, no. 1, pp. 1–20, 2024, doi: 10.5614/itbj.ict.res.appl.2023.18.1.1.
A. Sadqui, M. Ertel, H. Sadiki, and S. Amali, “Evaluating Machine Learning Models for Predicting Graduation Timelines in Moroccan Universities,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 7, pp. 304–310, 2023, doi: 10.14569/IJACSA.2023.0140734.
H. S. Bako, F. U. Ambursa, B. S. Galadanci, and M. Garba, “Predicting Timely Graduation of Postgraduate Students Using Random Forests Ensemble Method,” FUDMA J. Sci., vol. 7, no. 3, pp. 177–185, 2023, doi: 10.33003/fjs-2023-0703-1773.
R. S. Muminin, A. Hadiana, and N. Natalia, “The Study of Neural Network Algorithm, Random Forest for Classification of Student Graduation,” Int. J. Sci. Res. Sci. Eng. Technol., pp. 517–522, 2023, doi: 10.32628/IJSRSET23103145.
A. Tholib, M. N. F. Hidayat, S. Yono, R. Wulanningrum, and E. Daniati, “Comparison of C4.5 and Naive Bayes for Predicting Student Graduation Using Machine Learning Algorithms,” Int. J. Eng. Comput. Sci. Appl., vol. 2, no. 2, pp. 71–78, 2023, doi: 10.30812/ijecsa.v2i2.3364.
A. Desfiandi and B. Soewito, “Student Graduation Time Prediction Using Logistic Regression, Decision Tree, Support Vector Machine, and Adaboost Ensemble Learning,” IJISCS (International J. Inf. Syst. Comput. Sci., vol. 7, no. 3, pp. 195–199, 2023, doi: 10.56327/ijiscs.v7i2.1579.
D. Hermanto, D. I. Ricoida, D. Pibriana, Rusbandi, and M. R. Pribadi, “Analysis of Student Graduation Prediction Using Machine Learning Techniques on an Imbalanced Dataset: An Approach to Address Class Imbalance,” Sci. J. Informatics, vol. 11, no. 3, pp. 559–568, 2024, doi: 10.15294/sji.v11i3.5528.
M. Putra and E. Harahap, “Machine Learning pada Prediksi Kelulusan Mahasiswa Menggunakan Algoritma Random Forest,” J. Ris. Mat., vol. 4, no. 2, pp. 127–136, 2024, doi: 10.29313/jrm.v4i2.5102.
D. P. Utomo, Mesran, Sarwandi, S. Aripin, M. Syahrizal, and Pristiwanto, “A comparative analysis of data normalization on data mining classification performance,” 2ND Int. Semin. Sci. Technol., vol. 3048, no. 1, 2024, doi: https://doi.org/10.1063/5.0208001.
Mesran, M. Syahrizal, Sarwandi, S. Aripin, D. P. Utomo, and A. Karim, “A comparison of the performance of data mining classification algorithms on medical datasets with the application of data normalization,” 2ND Int. Semin. Sci. Technol., vol. 3048, no. 1, 2024, doi: https://doi.org/10.1063/5.0207994.
M. Syahrizal, S. Aripin, D. P. Utomo, Mesran, Sarwandi, and N. A. Hasibuan, “The application of the K-NN imputation method for handling missing values in a dataset,” 2ND Int. Semin. Sci. Technol., vol. 3048, no. 1, 2024, doi: https://doi.org/10.1063/5.0207998.
M. F. R. Ahnaf, S. H. Wijoyo, and N. Hidayat, “Prediction of On-Time Graduation of Students Using Random Forest Algorithm (Case Study: Faculty of Computer Science, Universitas Brawijaya),” J. Inf. Technol. Comput. Sci., vol. 10, no. 3, pp. 207–219, 2025, doi: 10.25126/jitecs.2025103788.
M. C. Afandi, U. Indahyanti, H. Setiawan, and I. A. Kautsar, “Perbandingan Algoritma Machine Learning dalam Memprediksi Kelulusan Mahasiswa,” Semant. Tek. Inf., vol. 11, no. 2, 2025, doi: 10.55679/semantik.v11i2.154.
N. S. Ngaeni, “Comparative Analysis of Random Forest and Support Vector Classifier for Predicting Students’ On-Time Graduation,” J. Pilar Nusa Mandiri, vol. 21, no. 2, pp. 247–255, 2025, doi: 10.33480/pilar.v21i2.7048.
T.-J. Law, C.-Y. Ting, H. Ng, H.-N. Goh, and A. Quek, “Improving Embeddings Representation via QED Approach for Enhancing Graduate on Time and Employability Prediction,” Int. J. Comput. Intell. Syst., vol. 18, 2025, doi: 10.1007/s44196-025-01034-9.
Mundirin, D. Hedin, Idawati, I. Latief, and M. Lili, “Model Prediktif Kelulusan Mahasiswa Berbasis Machine Learning Menggunakan Pipeline Terintegrasi dan Hyperparameter Tuning,” J. Pendidik. Sains dan Komput., vol. 6, no. 1, 2026, doi: 10.47709/jpsk.v6i01.7795.
A. Rahman, D. Mahdiana, and A. Fauzi, “Predicting Student On-Time Graduation Using Particle Swarm Optimization and Random Forest Algorithms,” Indones. J. Artif. Intell. Data Min., vol. 8, no. 1, pp. 161–173, 2025, doi: 10.24014/ijaidm.v8i1.33577.
A. Efendi and S. Defit, “Addressing Class Imbalance in Machine Learning for Predicting On-Time Student Graduation at The Islamic University of Riau,” J. Tek. Inform., vol. 18, no. 2, pp. 226–235, 2025, doi: 10.15408/jti.v18i2.45913.
D. O. E. Wanto and D. S. Bahri, “Analysis and Prediction of Student Academic Performance Using the Random Forest Algorithm,” J. Inotera, vol. 11, no. 1, pp. 232–239, 2026, doi: 10.31572/inotera.Vol11.Iss1.2026.ID657.
V. Dermol and V. Skrbinjek, “Predicting Student Dropout in Higher Education Using Random Forest and Survival Analysis,” Discov. Educ., vol. 5, 2026, doi: 10.1007/s44217-026-01710-8.
R. D. Romdhoni, N. W. A. Majid, and A. F. Permatasari, “Machine Learning-Based Early Warning for Student Dropout: Evidence from LMS Behavioral Engagement Patterns in Online Higher Education,” J. Informatics Vocat. Educ., vol. 9, no. 2, pp. 55–66, 2026, doi: 10.20961/joive.v9i2.3508.
L. R. Pelima, Y. Sukmana, and Y. Rosmansyah, “Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review,” IEEE Access, vol. 12, pp. 23451–23465, 2024, doi: 10.1109/ACCESS.2024.3361479.
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Copyright (c) 2026 Misaria Laia, Adri Wahyu, Montanus Montanus, Rinda AS Siahaan, David JM Sembiring, Sumarlin Sumarlin
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