PENERAPAN MACHINE LEARNING MENGGUNAKAN ALGORITMA DECISION TREE UNTUK PREDIKSI TINGKAT KELULUSAN MAHASISWA
DOI:
https://doi.org/10.47080/ctjbgh21Keywords:
C5.0, Decision Tree, Graduation Prediction, Machine Learning, StudentsAbstract
Students are an integral part of higher education institutions, where graduation rates serve as a key indicator of academic quality and institutional effectiveness. To maintain accreditation and academic standards, universities must optimize student graduation rates. Evaluating the factors influencing graduation is crucial in identifying patterns and key determinants that contribute to academic success. This study aims to predict student graduation using Machine Learning, specifically the C5.0 Decision Tree algorithm. The findings indicate a high reliability in predicting student graduation, with an accuracy of 91.35%. The model's ability to identify on-time graduates is reflected in a recall of 93.85% for the On-Time category and 87.18% for the Delayed category. The prediction accuracy is further demonstrated by a precision of 92.42% for the On-Time category and 89.47% for the Delayed category. The F1-Score, which represents the balance between recall and precision, reaches 93.12% for the On-Time category and 88.32% for the Delayed category. These evaluation metrics indicate that the C5.0 algorithm effectively classifies students based on their likelihood of graduating with high accuracy. The predictions generated can serve as a reference for universities to identify at-risk students early, allowing the implementation of appropriate academic strategies to improve graduation rates, accreditation, and institutional quality.
References
Anggriani, G., Amiruddin, & Zulfrianto. (2024). Klasifikasi Waktu Kelulusan Mahasiswa Menggunakan Metode Decision Tree. Jurnal Ilmiah Ilmu Komputer, 3(1), 31–36.
Do Nascimento, D. V., Sousa, R. T., Costa Silva, D. F., Do Prado Pagotto, D., Coelho, C. J., & Galvao Filho, A. R. (2023). Live Birth Forecasting in Brazillian Health Regions with Tree-based Machine Learning Models. Proceedings - IEEE Symposium on Computer-Based Medical Systems, 2023-June, 85–90. https://doi.org/10.1109/CBMS58004.2023.00197
Fan, W., Liu, K., Liu, H., Ge, Y., Xiong, H., & Fu, Y. (2024). Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop. IEEE Transactions on Knowledge and Data Engineering, 35(2), 1624–1636. https://doi.org/10.1109/TKDE.2021.3102120
Firman Akbar, & Rahmaddeni. (2024). Komparasi Algoritma Machine Learning Untuk Memprediksi Penyakit Alzheimer. Jurnal Komputer Terapan, 8(2), 236–245. https://doi.org/10.35143/jkt.v8i2.5713
Madhyastha, M., Budavari, T., Braverman, V., Vogelstein, J., & Burns, R. (2024). T-Rex (Tree-Rectangles): Reformulating Decision Tree Traversal as Hyperrectangle Enclosure. Proceedings - International Conference on Data Engineering, 1792–1804. https://doi.org/10.1109/ICDE60146.2024.00145
Menteri Pendidikan, kebudayaan, R. dan teknologi R. I. (2023). Peraturan Menteri Pendidikan, Kebudayaan, Riset, Dan Teknologi Republik Indonesia Nomor 53 Tahun 2023 Tentang Penjaminan Mutu Pendidikan Tinggi. In jdih.kemdikbud.go.id.
Mukrimaa, S. S., Nurdyansyah, Fahyuni, E. F., YULIA CITRA, A., Schulz, N. D., غسان, د., Taniredja, T., Faridli, E. M., & Harmianto, S. (2024). Machine Learning Teori, Studi Kasus dan Implementasi Menggunakan Python. In Jurnal Penelitian Pendidikan Guru Sekolah Dasar (Vol. 6, Issue August, p. 128).
Mundok, G. A., Amiruddin, & Zulfrianto. (2024). Klasifikasi Waktu Kelulusan Mahasiswa Menggunakan Metode Decision Tree. Jurnal Ilmiah Ilmu Komputer, 3(1), 31–36.
Presiden Revublik Indonesia. (2022). Peraturan Presiden Republik Indonesia Nomor 85 Tahun 2022 Tentangg Universitas Islam Negeri Sjech M.Djamil Djambek Bukittinggii.
Qisthiano, M. R., Prayesy, P. A., & Ruswita, I. (2023). Penerapan Algoritma Decision Tree dalam Klasifikasi Data Prediksi Kelulusan Mahasiswa. G-Tech: Jurnal Teknologi Terapan, 7(1), 21–28. https://doi.org/10.33379/gtech.v7i1.1850
Saputra, I., & Kristiyanti, D. A. (2024). Machine Learning. Informatika Bandung.
Suyanto, R. V. A., Eduard Rusdianto, & Ernawati. (2024). Penerapan Algoritma Decision Tree C4.5 dan Metode AdaBoost Untuk Prediksi Kelulusan Mahasiswa. Jurnal Informatika Atma Jogja, 5(1), 75–86. https://doi.org/10.24002/jiaj.v5i1.8646
Umar, N., Susilo, A., Trianto, & Bagus, R. (2024). Prediksi Tingkat Kelulusan Mahasiswa S1 Universitas an Nuur Dengan Metode DECISION TREE C4.5. Julia: Jurnal Ilmu Komputer An Nuur, 4(Rohmawan 2018), 21–29.
Zebua, F. J., Br Manalu, R. P., & Nababan, M. N. K. (2024). Prediksi Kelulusan Mahasiswa Menggunakan Perbandingan Algoritma C5.0 Dengan Regression Linear. Jurnal Teknik Informasi Dan Komputer (Tekinkom), 4(2), 230. https://doi.org/10.37600/tekinkom.v4i2.400
Zhong, G., Xiang, K., & Mo, Q. (2024). Weather Forecast Model Based on Decision Tree and Neural Network. Proceedings - 2024 4th Asia-Pacific Conference on Communications Technology and Computer Science, ACCTCS 2024, 509–512. https://doi.org/10.1109/ACCTCS61748.2024.00096
