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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi Perfecting a Video Game with Game Metrics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Pengabdian UntukMu NegeRI RABIT: Jurnal Teknologi dan Sistem Informasi Univrab CIRCUIT: Jurnal Ilmiah Pendidikan Teknik Elektro Seminar Nasional Teknologi Informasi Komunikasi dan Industri JITK (Jurnal Ilmu Pengetahuan dan Komputer) ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Computer Science and Information Technologies Journal of Education Informatic Technology and Science Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) IJISTECH Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) SATIN - Sains dan Teknologi Informasi
Edi Ismanto
Universitas Muhammadiyah Riau
Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

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A comparative study of machine learning algorithms for virtual learning environment performance prediction Edi Ismanto; Hadhrami Ab. Ghani; Nurul Izrin Binti Md Saleh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1677-1686

Abstract

Virtual learning environment is becoming an increasingly popular studyoption for students from diverse cultural and socioeconomic backgroundsaround the world. Although this learning environment is quite adaptable,improving student performance is difficult due to the online-only learningmethod. Therefore, it is essential to investigate students' participation andperformance in virtual learning in order to improve their performance. Usinga publicly available Open University learning analytics dataset, this studyexamines a variety of machine learning-based prediction algorithms todetermine the best method for predicting students' academic success, henceproviding additional alternatives for enhancing their academic achievement.Support vector machine, random forest, Nave Bayes, logical regression, anddecision trees are employed for the purpose of prediction using machinelearning methods. It is noticed that the random forest and logistic regressionapproach predict student performance with the highest average accuracyvalues compared to the alternatives. In a number of instances, the supportvector machine has been seen to outperform the other methods.
Co-Authors Agus Satria Ambiyar, Ambiyar Diah Eka Ratna Eka Pandu Cynthia Eka Pandu Cynthia Eka Pandu Cynthia Erik Suanda Handika Gunawan, Rahmad Hadhrami Ab Ghani Hadhrami Ab. Ghani Herlandy, Pratama Benny Januar Al Amien Khairul Anshari Melly Novalia Melly Novalia Noverta Effendi Noverta Effendi Nurul Izrin Binti Md Saleh Nurul Izrin Md Saleh Pratama Benny Herlandi Rahmad Al Rian Rahmad Alrian Rahmad Alrian Rahmad Gunawan Gunawan Renita Rahmadani Resmi Darni Siti Niah Sri Fitria Retnawati Vitriani Vitriani Vitrian Vitriani Vitriani Wandi Syahfutra Yeeri Badrun