The Combination of Naive Bayes and Particle Swarm Optimization Methods of Student’s Graduation Prediction

Evi Purnamasari, Dian Palupi Rini, Sukemi Sukemi

Abstract


This research conducted classification testing on the study case of student graduation prediction in a university. It aims to assist the university in maintaining academic development and in finding solutions for improving timely graduation. This study combined two methods, i.e., Naive Bayes and Particle Swarm Optimization, to produce a better level of accuracy. The Naive Bayes method is a statistical classification method used to predict a student's graduation in this study. That will be further enhanced using the Particle Swarm Optimization method to produce a better level of accuracy. There are 10 (ten) samples in this study randomly selected from the alumni data of UIGM students in 2011-2014. From the test results, this research resulted in an accuracy value of 90% from the Naive Bayes algorithm testing, after testing the Naive Bayes with Particle Swarm Optimization, which produced an accuracy value of 100%. The conclusion obtained from the results is the Naive Bayes method has a higher accuracy value if combined with Particle Swarm Optimization. Thus the university can more easily predict whether or not the students graduate on time for the upcoming graduation period. The results of this test prove that to predict student graduation using the Naive Bayes method with Particle Swarm Optimization is appropriate.

Keywords


Prediction; Naive Bayes; Particle Swarm Optimization; Graduation; Students

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References


A. Aryasanti. Sistem Komparasi Naive Bayes dan Decision Trees untuk Menentukan Klasifikasi Kegagalan Studi Mahasiswa,†Jurnal TICOM, Vol. 6, No. 3, 2018, pp. 103-111. Online

A. Romadhona, S. Suprapedi, and H. Himawan, “Prediksi Kelulusan Mahasiswa Tepat Waktu Berdasarkan Usia, Jenis Kelamin dan Indeks Prestasi Menggunakan Algoritma Decision Tree,†Jurnal Teknologi Informasi Cyberku, Vol. 13, No. 1, pp. 69–83, 2017. Google Scholar

I. P. Astuti, “Prediksi Ketepatan Waktu Kelulusan Dengan Algoritma Data Mining C4.5,†Fountain of Informatics Journal, vol. 2, no. 2, pp. 41-45, 2017. DOI: 10.21111/fij.v2i2.1067

Salmu, S., & Solichin, A. “Prediksi Tingkat Kelulusan Mahasiswa Tepat Waktu Menggunakan Naïve Bayes: Studi Kasus UIN Syarif Hidayatullah Jakarta,†Prosiding Seminar Nasional Multidisiplin Ilmu Universitas Budi Luhur, pp. ICT-701-709, April 2017. Research Gate

S. Jayaprakash, E. Balamurugan, and V. Chandar, "Predicting Students’ Academic Performance Using Naïve Bayes Algorithm," 8th Annual International Applied Research Conference, 2015. Google Scholar

M. W. Amelia, A. S. M. Lumenta, and A. Jacobus, “Prediksi Masa Studi Mahasiswa dengan Menggunakan Algoritma Naïve Bayes,†Jurnal Teknik Informatika, vol. 11, no. 1, pp. 2017. DOI: 10.35793/jti.11.1.2017.17652

U. Pujianto, E. N. Azizah and A. S. Damayanti, "Naive Bayes using to predict students' academic performance at faculty of literature," 2017 5th International Conference on Electrical, Electronics and Information Engineering (ICEEIE), Malang, 2017, pp. 163-169. DOI: 10.1109/ICEEIE.2017.8328782

E. Sugiharti, “Predictive Evaluation of Performance of Computer Science Students of Unnes Using Data Mining Based on Naive Bayes Classifier (NBC),†Journal of Theoretical and Applied Information Technology, vol. 95, no. 4, pp. 902–911, 2017. Google Scholar

P. V. P. Sundar and D. A. V. S. Kumar, “A Hybrid Classification Method for Disengagement Detection in Online Learning,†International Journal of Education and Information Studies, vol. 5, no. 1, pp. 67–74, 2015. Google Scholar

S. A. Pattekari, and A. Parveen, “Prediction System for Heart Disease Using Naive Bayes,†International Journal of Advanced Computer and Mathematical Sciences, vol. 3, no. 3, 290–294. 2012. Google Scholar

N. K. Ghanad and S. Ahmadi, “Combination of PSO Algorithm and Naive Bayesian Classification for Parkinson Disease Diagnosis,†Advances in Computer Science: An International Journal, vol. 4, no. 4, 119–125, 2015. Google Scholar

W. F. Mahmudy, “Improved Particle Swarm Optimization untuk Menyelesaikan Permasalahan Part Type Selection dan Machine Loading pada Flexible Manufacturing System (FMS),†Konferensi Nasional Sistem Informasi, 2015. Google Scholar

A. Kesumawati and D. T. Utari, “Predicting patterns of student graduation rates using Naïve bayes classifier and support vector machine,†AIP Conference Proceedings, vol. 2021, 2018. DOI: 10.1063/1.5062769

S. Kaur and M. Goyal, “Fast and robust Hybrid Particle Swarm Optimization Tabu Search Association Rule Mining (HPSO-ARM) algorithm for Web Data Association Rule Mining (WDARM),†International Journal of Advance Research in Computer Science and Management Studies, vol. 2, no. 9, 448–451, 2014. Google Scholar

H. Abadlia, N. Smairi, and K. Ghedira, “A hybrid Immigrants schema for particle swarm optimization algorithm,†Procedia Computer Science, vol. 126, pp. 105–115, .2018. DOI: 10.1016/j.procs.2018.07.214




DOI: http://dx.doi.org/10.26555/jiteki.v5i2.15272

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Jurnal Ilmiah Teknik Elektro Komputer dan Informatika
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