Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Vol 4 No 3 (2020): Juni 2020

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)



Article Info

Publish Date
20 Jun 2020

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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Journal Info

Abbrev

RESTI

Publisher

Subject

Computer Science & IT Engineering

Description

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat ...