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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Evi Purnamasari
Universitas Sriwijaya
Computer Science & IT Electrical & Electronics Engineering Engineering

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The Combination of Naive Bayes and Particle Swarm Optimization Methods of Student’s Graduation Prediction Evi Purnamasari; Dian Palupi Rini; Sukemi Sukemi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 5, No 2 (2019): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (625.875 KB) | DOI: 10.26555/jiteki.v5i2.15272

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.
Seleksi Fitur menggunakan Algoritma Particle Swarm Optimization pada Klasifikasi Kelulusan Mahasiswa dengan Metode Naive Bayes Evi Purnamasari; Dian Palupi Rini; Sukemi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 3 (2020): Juni 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (420.114 KB) | DOI: 10.29207/resti.v4i3.1833

Abstract

The study of the classification of student graduation at a university aims to help the university understand the academic development of students and to be able to find solutions in improving the development of student graduation in a timely manner. The Naive Bayes method is a statistical classification method used to predict a student's graduation in this study. The classification accuracy can be improved by selecting the appropriate features. Particle Swarm Optimization is an evolutionary optimization method that can be used in feature selection to produce a better level of accuracy. The testing results of the alumni data using the Naive Bayes method that optimized with the Particle Swarm Optimization algorithm in selecting appropriate features, producing an accuracy value of 86%, 6% higher than the classification without feature selection using the Naive Bayes method.
Co-Authors Dian Palupi Rini Sukemi Sukemi