Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal International Journal Engineering and Applied Technology (IJEAT)
Faridatun Ni’mah
Universitas Nusa Putra
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
COMPARISON C4.5 AND NAÏVE BAYES METHODS BASED ON PARTICLE SWARM OPTIMIZATION IN LEVELS OF DROP OUT STUDENTS dudih gustian; Faridatun Ni’mah; Agus Darmawan
INTERNATIONAL JOURNAL ENGINEERING AND APPLIED TECHNOLOGY (IJEAT) Vol. 2 No. 2 (2019): International Journal of Engineering and Applied Technology (IJEAT)
Publisher : Nusa Putra University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/ijeat.v2i2.19

Abstract

The high percentage of drop-out students causes a campus management problem, this is because the percentage of students graduating on time is one of the elements of accreditation assessment set by the national accreditation board of higher education. One reason why the drop out rate is still high is because the Management System has not run well, such as lecturer professionalism, campus facilities, academics and administration, student affairs, outside influence and student personality. This study aims to analyze several indicators that can cause student drop outs by comparing the C4.5 method based on particle swarm optimization and Naïve Bayes based on PSO. This study contributes to campus management in anticipating the occurrence of drop outs through indicators that occur and can predict student drop out rates through the classification process. The highest level of accuracy produced from C4.5 + PSO is around 99.32% with AUC from Naïve Bayes is 0.974 categorized as excellent classification.
Co-Authors Dudih Gustian Yofita Sandra, S.Pd., M.Pd., Zico Farlin, Dr. Budiwirman, M.Pd.,