HYBRID NAIVE BAYES DAN IMPROVED PARTICLE SWARM OPTIMIZATION UNTUK PREDIKSI KELULUSAN MAHASISWA UIGM

PURNAMASARI, EVI and Rini, Dian Palupi and Sukemi, Sukemi (2020) HYBRID NAIVE BAYES DAN IMPROVED PARTICLE SWARM OPTIMIZATION UNTUK PREDIKSI KELULUSAN MAHASISWA UIGM. Master thesis, Sriwijaya University.

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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.

Item Type:	Thesis (Master)
Uncontrolled Keywords:	klasifikasi, kelulusan Mahasiswa, fitur, seleksi, naive bayes, PSO
Subjects:	T Technology > TH Building construction
Divisions:	09-Faculty of Computer Science > 55101-Informatics (S2)
Depositing User:	Users 6443 not found.
Date Deposited:	05 Aug 2020 07:07
Last Modified:	05 Aug 2020 07:07
URI:	http://repository.unsri.ac.id/id/eprint/32182

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