Graduation Prediction System on Students Using C4.5 Algorithm
DOI:
https://doi.org/10.30812/matrik.v19i2.685Keywords:
C4.5 Algorithm, Data Mining, Student Graduation, PredictionAbstract
Bumigora University College there are several things that are not balanced between the entry and exit of students who have completed their studies. Students who enter in large numbers, but students who graduate on time below the specified standards. As result, there was a huge accumulation of students in each graduation period. One solution to overcome the problem above needs a data mining based system in monitoring or utilizing student development in predicting graduation using the C4.5 algorithm. The stages of this research began with problem analysis, data collection, data requirement analysis, data design, coding, and testing. The results of this study are the implementation of the C4.5 algorithm for predicting student graduation on time or not. The data used is the data of students who have graduated from 2010 to 2012. The level of acceptance generated using the confusion matrix is ​​93,103% accuracy using 163 training data and 29 testing data or 85% training data and 15% testing data. The results of research and testing that has been done, C4.5 algorithm is very suitable to be used in student graduation prediction.
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References
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[3] Y. Yulia and N. Azwanti, “Penerapan Algoritma C4.5 Untuk Memprediksi Besarnya Penggunaan Listrik Rumah Tangga di Kota Batam,†J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 2, no. 2, pp. 584–590, 2018, doi: 10.29207/resti.v2i2.503.
[4] R. H. Pambudi and B. D. Setiawan, “Penerapan Algoritma C4 . 5 Untuk Memprediksi Nilai Kelulusan Siswa Sekolah Menengah Berdasarkan Faktor Eksternal,†J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 7, pp. 2637–2643, 2018.
[5] E. Purnamasari, D. P. Rini, and Sukemi, “Prediction of the Student Graduation’s Level using C4.5 Decision Tree Algorithm,†in International Conference on Electrical Engineering and Computer Science (ICECOS) 2019, 2019, pp. 192–195, doi: 10.1109/icecos47637.2019.8984493.
[6] A. A. Supianto, A. Julisar Dwitama, and M. Hafis, “Decision Tree Usage for Student Graduation Classification: A Comparative Case Study in Faculty of Computer Science Brawijaya University,†in 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings, 2018, pp. 308–311, doi: 10.1109/SIET.2018.8693158.
[7] E. Sutoyo and A. Almaarif, “Educational Data Mining untuk Prediksi Kelulusan Mahasiswa Menggunakan Algoritme Naïve Bayes Classifier,†J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 1, pp. 95–101, 2020, doi: 10.29207/RESTI.V4I1.1502.
[8] I. A. Nikmatun and I. Waspada, “Implementasi Data Mining untuk Klasifikasi Masa Studi Mahasiswa Menggunakan Algoritma K-Nearest Neighbor,†J. SIMETRIS, vol. 10, no. 2, pp. 421–432, 2019.
[9] A. Maesya and T. Hendiyanti, “Forecasting Student Graduation with Classification and Regression Tree (CART) Algorithm,†IOP Conf. Ser. Mater. Sci. Eng., vol. 621, no. 1, pp. 1–6, 2019, doi: 10.1088/1757-899X/621/1/012005.
[10] R. Puspita, S. Putri, I. Waspada, D. Ilmu, K. Informatika, and F. Sains, “Penerapan Algoritma C4 . 5 pada Aplikasi Prediksi Kelulusan Mahasiswa Prodi Informatika,†Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 4, no. 1, pp. 1–7, 2018.
[11] D. Devina, A. A. Supianto, and W. Purnomo, “Aplikasi Data Mining Menggunakan Algoritme C4 . 5 untuk Memprediksi Ketepatan Lulus Mahasiswa Berdasarkan Faktor Demografi,†J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 6, pp. 6044–6051, 2019.
[12] M. Ridwan, H. Suyono, and M. Sarosa, “Penerapan Data Mining Untuk Evaluasi Kinerja Akademik Mahasiswa Menggunakan Algoritma Naive Bayes Classifier,†vol. 7, no. 1, pp. 59–64, 2013.
[13] S. Faisal, “Klasifikasi Data Minning Menggunakan Algoritma C4.5 Terhadap Kepuasan Pelanggan Sewa Kamera Cikarang,†J. Ilmu Komput. Teknol. Inf. ISSN, vol. 4, no. April, pp. 1–8, 2019.
[14] Rismayanti, “Implementasi Algoritma C4.5 Untuk Menentukan Penerima Beasiswa Di Stt Harapan Medan,†Media Infotama, vol. 12, no. 2, pp. 116–120, 2016.
[15] T. A. Kurniawan, “Pemodelan Use Case (Uml): Evaluasi Terhadap Beberapa Kesalahan Dalam Praktik,†J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 1, pp. 77–86, 2018, doi: 10.25126/jtiik.201851610.
[16] H. Hairani, K. E. Saputro, and S. Fadli, “K-means-SMOTE untuk menangani ketidakseimbangan kelas dalam klasifikasi penyakit diabetes dengan C4.5, SVM, dan naive Bayes,†Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 2, pp. 89–93, Apr. 2020, doi: https://doi.org/10.14710/jtsiskom.8.2.2020.89-93.
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Published
2020-05-30
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[1]
D. Kurniawan, A. Anggrawan, and H. Hairani, “Graduation Prediction System on Students Using C4.5 Algorithm”, MATRIK, vol. 19, no. 2, pp. 358–366, May 2020, doi: 10.30812/matrik.v19i2.685.
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