Comparison of C4.5 and Naive Bayes for Predicting Student Graduation Using Machine Learning Algorithms
Keywords:
Comparison Method, C4.5 Algorithm, Student Graduation, Naive Bayes Algorithm
Abstract
Student graduation is a very important element for universities because it relates to college accreditation assessment. One of them is at the Faculty of Engineering Nurul Jadid University, which has problems completing the study period within a predetermined time. So that it can be detrimental because accreditation is less than optimal, and the number of active students makes it less ideal in teaching and learning activities. This study aimed to compare the level of accuracy using the C4.5 algorithm and Naïve Bayes method in predicting graduation on time. The C4.5 and Naïve Bayes algorithms are one of the methods in the algorithm for classifying. Tests were carried out using the C4.5 and Naïve Bayes algorithms using Google Colab with Python programming language, then validated using 10-fold cross-validation. The results of this study indicate that the Naïve Bayes method has a higher accuracy value with an accuracy rate of 96.12%, while the C4.5 algorithm method is 93.82%.
References
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A. Anggrawan, H. Hairani, and C. Satria, “Improving SVM Classification Performance on Unbalanced Student Graduation Time Data Using SMOTE,” Int. J. Inf. Educ. Technol., vol. 13, no. 2, pp. 289–295, 2023, doi: 10.18178/ijiet.2023.13.2.1806.
D. Kurniawan, A. Anggrawan, and H. Hairani, “Graduation Prediction System On Students Using C4.5 Algorithm,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 19, no. 2, pp. 358–365, 2020, doi: 10.30812/matrik.v19i2.685.
L. Y. L. Gaol, M. Safii, and D. Suhendro, “Prediksi Kelulusan Mahasiswa Stikom Tunas Bangsa Prodi Sistem Informasi dengan Menggunakan Algoritma C4.5,” Brahmana J. Penerapan Kecerdasan Buatan, vol. 2, no. 2, pp. 97–106, 2021.
Endang Etriyanti, “Perbandingan Tingkat Akurasi Metode Knn Dan Decision Tree Dalam Memprediksi Lama Studi Mahasiswa,” J. Ilm. Bin. STMIK Bina Nusant. Jaya Lubuklinggau, vol. 3, no. 1, pp. 6–14, 2021, doi: 10.52303/jb.v3i1.40.
H. Hairani, M. Innuddin, and M. Rahardi, “Accuracy Enhancement of Correlated Naive Bayes Method by Using Correlation Feature Selection (CFS) for Health Data Classification,” in 2020 3rd International Conference on Information and Communications Technology (ICOIACT), 2020, pp. 51–55. doi: 10.1109/ICOIACT50329.2020.9332021.
H. Hairani, A. Anggrawan, A. I. Wathan, K. A. Latif, K. Marzuki, and M. Zulfikri, “The Abstract of Thesis Classifier by Using Naive Bayes Method,” in 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM), 2021, pp. 312–315. doi: 10.1109/ICSECS52883.2021.00063.
A. Suwarno, N. Ferawati, and P. A. Sari, “Penerapan Data Mining untuk Prediksi Kelulusan Siswa Menggunakan Algoritma Naive Bayes pada SMK Garuda,” J. Teknol. Pelita Bangsa, vol. 12, no. 4, pp. 33–40, 2021.
A. Armansyah and R. K. Ramli, “Model Prediksi Kelulusan Mahasiswa Tepat Waktu dengan Metode Naïve Bayes,” Edumatic J. Pendidik. Inform., vol. 6, no. 1, pp. 1–10, Jun. 2022, doi: 10.29408/edumatic.v6i1.4789.
H. Yuliansyah, R. A. P. Imaniati, A. Wirasto, and M. Wibowo, “Predicting Students Graduate on Time Using C4.5 Algorithm,” J. Inf. Syst. Eng. Bus. Intell., vol. 7, no. 1, pp. 67–73, 2021, doi: 10.20473/jisebi.7.1.67-73.
N. Hidayati and A. Hermawan, “K-Nearest Neighbor (K-NN) algorithm with Euclidean and Manhattan in classification of student graduation,” J. Eng. Appl. Technol., vol. 2, no. 2, pp. 86–91, 2021, doi: 10.21831/jeatech.v2i2.42777.
M. T. Sembiring and R. H. Tambunan, “Analysis of graduation prediction on time based on student academic performance using the Naïve Bayes Algorithm with data mining implementation (Case study: Department of Industrial Engineering USU),” in IOP Conference Series: Materials Science and Engineering, 2021, pp. 1–8. doi: 10.1088/1757-899x/1122/1/012069.
F. Solikhah, M. Febianah, A. L. Kamil, W. A. Arifin, and Shelly Janu Setyaning Tyas, “Analisis Perbandingan Algoritma Naive Bayes Dan C.45 Dalam Klasifikasi Data Mining Untuk Memprediksi Kelulusan,” TEMATIK, vol. 8, no. 1, pp. 96–103, Jun. 2021, doi: 10.38204/tematik.v8i1.576.
A. Anwarudin, W. Andriyani, B. P. DP, and D. Kristomo, “The Prediction on the Students’ Graduation Timeliness Using Naive Bayes Classification and K-Nearest Neighbor,” J. Intell. Softw. Syst., vol. 1, no. 1, pp. 75–88, Jul. 2022, doi: 10.26798/jiss.v1i1.597.
Published
2023-09-20
How to Cite
[1]
A. Tholib, M. N. Fadli Hidayat, S. yono, R. Wulanningrum, and E. Daniati, “Comparison of C4.5 and Naive Bayes for Predicting Student Graduation Using Machine Learning Algorithms”, International Journal of Engineering and Computer Science Applications (IJECSA), vol. 2, no. 2, pp. 71 - 78, Sep. 2023.
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Articles
