Comparison of C4.5 Algorithm and Support Vector Machine in Predicting the Student Graduation Timeliness

Authors

  • Agus Mailana Department of Informatics, STAI Al-Hidayah Bogor, Indonesia
  • Andi Agung Putra Department of Informatics, Universitas Budi Luhur Jakarta, Indonesia
  • Sarifudlin Hidayat Department of Informatics, Universitas Budi Luhur Jakarta, Indonesia
  • Arief Wibowo Master of Computer Science, Universitas Budi Luhur, Indonesia

DOI:

https://doi.org/10.15575/join.v6i1.608

Keywords:

mahasiswa, algoritma c4.5, support vector machine, prediksi kelulusan, stai

Abstract

In higher educational institutions, graduation rates are one of the many aspects to assess the quality of the learning process. Al-Hidayah Islamic University in Bogor is one of the established private Islamic universities to create skilled human resources with moral values required by many companies nowadays. Having another institution in Bogor as a competitor with the same direction and objective is a challenge for Al-Hidayah Islamic University. Thus a solution is required to face the competition. One solution is to predict the student graduation timeliness of the students using data mining method with classification function. The implemented methodology in the data mining is Discovery Knowledge of Database (KDD), starting from selecting, preprocessing, transformation, data mining, and evaluation/ interpretation. There were two Algorithm models used in this paper, namely C4.5 and Support Vector Machine (SVM). The classification procedure consists of predictor variables and one of the target variables. Predictor variables are gender, Grade Point Average, marital status, and job status. Rapid Miner software was used to process the data. The final results of both Algorithms show an 81% precision rate and 80% accuracy level for the C4.5 Algorithm, while SVM has an 88% precision rate and 85% accuracy level.

References

Suhardjono, G. Wiajaya, and H. Abdul, “Prediksi Waktu Kelulusan Mahasiswa Menggunakan Svm Berbasis Pso,†Bianglala Inform., vol. 7, no. 2, pp. 97–101, 2019.

R. P. S. Putri and I. Waspada, “Penerapan Algoritma C4.5 pada Aplikasi Prediksi Kelulusan Mahasiswa Prodi Informatika,†Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 4, no. 1, p. 1, 2018.

A. Pratama, R. C. Wihandika, and D. E. Ratnawati, “Implementasi Algoritma Support Vector Machine (SVM) untuk Prediksi Ketepatan Waktu Kelulusan Mahasiswa,†J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. March, pp. 1704–1708, 2018.

E. Supriyadi and D. I. Sensuse, “Optimasi Algoritma Support Vector Machine Dengan Particle Swarm Optimization Dalam Mendeteksi Ketepatan Waktu Kelulusan Mahasiswa : Studi Kasus Poltek Lp3i Jakarta ‘Kampus Depok,’†vol. 1, no. 1, pp. 163–174, 2015.

I. P. Astuti, “Prediksi Ketepatan Waktu Kelulusan Dengan Algoritma Data Mining C4.5,†Fountain Informatics J., vol. 2, no. 2, pp. 41–45, 2017.

A. Rohman and M. Rochcham, “Komparasi Metode Klasifikasi Data Mining Untuk Prediksi Kelulusan Mahasiswa,†Neo Tek., vol. 5, no. 1, 2019.

C. N. Dengen, Kusrini, and E. T. Luthfi, “Implementasi Decision Tree Untuk Prediksi Kelulusan Mahasiswa Tepat Waktu,†J. Ilm. SISFOTENIKA, vol. 10, no. 1, pp. 1–11, 2020.

A. Fahrudin, L. Listiyoko, P. Surya, and A. Maksum, “Prediksi Peringkat Kelulusan Mahasiswa Untuk Menentukan Strategi Pemasaran Kampus Menggunakan Pohon Keputusan,†no. November, 2017.

I. Iskandar et al., “Prediksi Kelulusan Mahasiswa Menggunakan Algoritma Decision Tree C4 . 5 Dengan Teknik Pruning,†J. Ilmu Komput. dan Sist. Inf., vol. 6, no. 1, pp. 64–68, 2018.

A. Andie, “Penerapan Decision Tree Untuk Menganalisis Kemungkinan Pengunduran Diri Calon Mahasiswa Baru,†Technologia, vol. 7, no. 1, pp. 8–14, 2016.

Y. B. Samponu and K. Kusrini, “Optimasi Algoritma Naive Bayes Menggunakan Metode Cross Validation Untuk Meningkatkan Akurasi Prediksi Tingkat Kelulusan Tepat Waktu,†J. ELTIKOM, vol. 1, no. 2, pp. 56–63, 2018.

D. T. Larose and C. D. Larose, Discovering Knowledge in Data: An Introduction to Data Mining, 2nd ed. Canada: Wiley-Interscience, 2014.

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Published

2021-06-17

Issue

Vol. 6 No. 1 (2021)

Section

Article

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