Feature Selection using Particle Swarm Optimization Algorithm in Student Graduation Classification with Naive Bayes Method

Seleksi Fitur menggunakan Algoritma Particle Swarm Optimization pada Klasifikasi Kelulusan Mahasiswa dengan Metode Naive Bayes

  • Evi Purnamasari Universitas Sriwijaya
  • Dian Palupi Rini Universitas Sriwijaya
  • Sukemi Universitas Sriwijaya
Keywords: Klasifikasi, kelulusan Mahasiswa, fitur, Seleksi, naive bayes, PSO

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.

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Published
2020-06-20
How to Cite
Evi Purnamasari, Rini, D. P., & Sukemi. (2020). Feature Selection using Particle Swarm Optimization Algorithm in Student Graduation Classification with Naive Bayes Method. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(3), 469 - 475. https://doi.org/10.29207/resti.v4i3.1833
Section
Artikel Teknologi Informasi