Classifying student academic performance: C4.5 and SVM Methods in ITTP's Computer Engineering Program
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Published:
Feb 28, 2024
Keywords:
Classification Model, CRISP-DM, Data Mining, Graduation Performance, Support Vector Machine (SVM)
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Informtion System
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Abstract
The decline in the number of graduates from the Computer Engineering Program, based on the graduation percentages of the 2017 and 2018 cohorts, coupled with the imbalance between on-time and delayed graduates, poses various challenges. These challenges include suboptimal program accreditation and an excessive number of active students. This research aims to develop a classification model for student performance categorization into On-Time Graduation (LTW) and Delayed Graduation (LTTW) classes using SVM and C4.5 algorithms. The C4.5 algorithm will handle attribute selection, while SVM will be responsible for building the prediction model. The classification results will be visualized on a website using the Flask framework, allowing users to input relevant data. The classification accuracy on the test set, reaching 77.74%, indicates the model's precision in predicting student performance categories
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How to Cite
Gultom, E. ., Rafika Nur, Y. S. and Ramadhani, R. D. . (2024) “Classifying student academic performance: C4.5 and SVM Methods in ITTP’s Computer Engineering Program”, Jurnal Mantik, 7(4), pp. 3917-3926. doi: 10.35335/mantik.v7i4.4686.
References
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Sabna, E. (2021). DATA MINING DENGAN 2 (DUA) MODEL KLASIFIKASI UNTUK PREDIKSI KINERJA MAHASISWA. Jurnal Ilmu Komputer, 10(2), 128–131. https://doi.org/10.33060/jik/2021/vol10.iss2.229
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Suhaimi, N. M., Abdul-Rahman, S., Mutalib, S., Hamid, N. H. A., & Malik, A. M. A. (2019). Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms. International Journal of Modern Education and Computer Science, 11(7), 1–13. https://doi.org/10.5815/ijmecs.2019.07.01
Tahta, A., Karim, I., & Sudianto, S. (2023). Dominant Requirements for Student Graduation in the Faculty of Informatics using the C4.5 Algorithm. Journal of Dinda Data Science, Information Technology, and Data Analytics, 3(2), 50–58. http://journal.ittelkom-pwt.ac.id/index.php/dinda
Thaniket, R., & Taufik Luthf, E. (2020). PREDIKSI KELULUSAN MAHASISWA TEPAT WAKTU MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE. In Jurnal Teknologi dan Rekayasa (Vol. 5, Issue 2).
Wang, P., & Jia, Y. (2020). Research on Prediction of College Students’ Performance Based on Support Vector Machine.
Widaningsih, S. (2019). Perbandingan Metode Data Mining Untuk Prediksi Nilai Dan Waktu Kelulusan Mahasiswa Prodi Teknik Informatika Dengan Algoritma C4,5, Naïve Bayes, KNN Dan SVM. Jurnal Tekno Insentif, 13(1), 16–25. https://doi.org/10.36787/jti.v13i1.78
Yuliansyah, H., Imaniati, R. A. P., Wirasto, A., & Wibowo, M. (2021). Predicting Students Graduate on Time Using C4.5 Algorithm. Journal of Information Systems Engineering and Business Intelligence, 7(1), 67. https://doi.org/10.20473/jisebi.7.1.67-73
Bangun, O., Mawengkang, H., & Efendi, S. (2022). Metode Algoritma Support Vector Machine (SVM) Linier Dalam Memprediksi Kelulusan Mahasiswa. JURNAL MEDIA INFORMATIKA BUDIDARMA, 6(4), 2006. https://doi.org/10.30865/mib.v6i4.4572
Cardona, T. A., & Cudney, E. A. (2019). Predicting student retention using support vector machines. Procedia Manufacturing, 39, 1827–1833. https://doi.org/10.1016/j.promfg.2020.01.256
Dwi Sripamuji, A., Ramadhanti, I., Riski Amalia, R., Saputra, J., & Prihatnowo, B. (2022). Penerapan Algoritma Support Vector Machine Dan Multi-Layer Perceptron Pada Klasifikasi Topik Berita. Jurnal Nasional Pendidikan Teknik Informatika?: JANAPATI, 11(2), 84–91. https://doi.org/10.23887/janapati.v11i2.44151
Erny Herwindiati, D., & Hendryli, J. (2023). Performa Support Vector Machine Pada Klasifikasi Lahan dan Air Tanah. Jurnal Media Informatika Budidarma, 7(1), 231–241. https://doi.org/10.30865/mib.v7i1.5279
Fatma Ayu Rahman, A., Wartulas, S., Raya Pagojengan, J. K., & Brebes, P. (2020). Prediksi Kelulusan Mahasiswa Menggunakan Algoritma C4.5 (Studi Kasus Di Universitas Peradaban). Ade Fatma Ayu Rahman IJIR, 1(2), 70–77.
Iwan Nurhidayat, A., & Fatrianto, D. (2021). Prediksi Kinerja Akademik Mahasiswa Menggunakan Machine Learning dengan Sequential Minimal Optimization untuk Pengelola Program Studi. Journal Information Engineering and Educational Technology, 5(2), 84–91.
Janan, F., & Ghosh, S. K. (2021). Prediction of student’s performance using support vector machine classifier. Proceedings of the International Conference on Industrial Engineering and Operations Management, 7078–7088. https://doi.org/10.46254/an11.20211237
Kurniawan, D., Anggrawan, A., & Hairani, H. (2020). Graduation Prediction System On Students Using C4.5 Algorithm. MATRIK?: Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 19(2), 358–365. https://doi.org/10.30812/matrik.v19i2.685
Lestari, A. (2020). Increasing Accuracy of C4.5 Algorithm Using Information Gain Ratio and Adaboost for Classification of Chronic Kidney Disease. Journal of Soft Computing Exploration , 1(1), 32–38.
Maqfiroh, F., & Mujiyono, S. (2022). Penerapan Klasifikasi Algoritma Data Mining C4.5 Untuk Memprediksi Tingkat Kelulusan Siswa Di Lembaga Pelatihan Kerja Shinju Semarang. JAMASTIKA, 1(2), 35–49.
Mengash, H. A. (2020). Using data mining techniques to predict student performance to support decision making in university admission systems. IEEE Access, 8, 55462–55470. https://doi.org/10.1109/ACCESS.2020.2981905
Nalatissifa, H., Gata, W., Diantika, S., & Nisa, K. (2021). Perbandingan Kinerja Algoritma Klasifikasi Naive Bayes, Support Vector Machine (SVM), dan Random Forest untuk Prediksi Ketidakhadiran di Tempat Kerja. Jurnal Informatika Universitas Pamulang, 5(4), 578. https://doi.org/10.32493/informatika.v5i4.7575
Görtler, J., Hohman, F., Moritz, D., Wongsuphasawat, K., Ren, D., Nair, R., Kirchner, M., & Patel, K. (2022). Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels. Conference on Human Factors in Computing Systems - Proceedings, 1–13.
Pambudi, R. D., Supianto, A. A., & Setiawan, N. Y. (2019). Prediksi Kelulusan Mahasiswa Berdasarkan Kinerja Akademik Menggunakan Pendekatan Data Mining Pada Program Studi Sistem Informasi Fakultas Ilmu Komputer Universitas Brawijaya (Vol. 3, Issue 3). http://j-ptiik.ub.ac.id
Qisthiano, M. R., Kurniawan, T. B., Negara, E. S., & Akbar, M. (2021). Pengembangan Model Untuk Prediksi Tingkat Kelulusan Mahasiswa Tepat Waktu dengan Metode Naïve Bayes. JURNAL MEDIA INFORMATIKA BUDIDARMA, 5(3), 987. https://doi.org/10.30865/mib.v5i3.3030
Rotty, A. K., Dewayana, T. S., & Habyba, A. N. (2022). Cross-Industry Standard Process for Data Mining (CRISP-DM) Approach in Determining the Most Significant Employee Engagement Drivers to Sales at X Car Dealership. Proceedings of the 3rd Asia Pacific International Conference on Industrial Engineering and Operations Management, 3368–3379.
Sabna, E. (2021). DATA MINING DENGAN 2 (DUA) MODEL KLASIFIKASI UNTUK PREDIKSI KINERJA MAHASISWA. Jurnal Ilmu Komputer, 10(2), 128–131. https://doi.org/10.33060/jik/2021/vol10.iss2.229
Schröer, C., Kruse, F., & Gómez, J. M. (2021). A systematic literature review on applying CRISP-DM process model. Procedia Computer Science, 181, 526–534. https://doi.org/10.1016/j.procs.2021.01.199
Suhaimi, N. M., Abdul-Rahman, S., Mutalib, S., Hamid, N. H. A., & Malik, A. M. A. (2019). Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms. International Journal of Modern Education and Computer Science, 11(7), 1–13. https://doi.org/10.5815/ijmecs.2019.07.01
Tahta, A., Karim, I., & Sudianto, S. (2023). Dominant Requirements for Student Graduation in the Faculty of Informatics using the C4.5 Algorithm. Journal of Dinda Data Science, Information Technology, and Data Analytics, 3(2), 50–58. http://journal.ittelkom-pwt.ac.id/index.php/dinda
Thaniket, R., & Taufik Luthf, E. (2020). PREDIKSI KELULUSAN MAHASISWA TEPAT WAKTU MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE. In Jurnal Teknologi dan Rekayasa (Vol. 5, Issue 2).
Wang, P., & Jia, Y. (2020). Research on Prediction of College Students’ Performance Based on Support Vector Machine.
Widaningsih, S. (2019). Perbandingan Metode Data Mining Untuk Prediksi Nilai Dan Waktu Kelulusan Mahasiswa Prodi Teknik Informatika Dengan Algoritma C4,5, Naïve Bayes, KNN Dan SVM. Jurnal Tekno Insentif, 13(1), 16–25. https://doi.org/10.36787/jti.v13i1.78
Yuliansyah, H., Imaniati, R. A. P., Wirasto, A., & Wibowo, M. (2021). Predicting Students Graduate on Time Using C4.5 Algorithm. Journal of Information Systems Engineering and Business Intelligence, 7(1), 67. https://doi.org/10.20473/jisebi.7.1.67-73
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