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All Journal English Review: Journal of English Education Jurnal Kependidikan Media IDEAS: Journal on English Language Teaching and Learning, Linguistics and Literature ESTETIK : Jurnal Bahasa Indonesia Bahtera Indonesia; Jurnal Penelitian Bahasa dan Sastra Indonesia UTAMAX : Journal of Ultimate Research and Trends in Education BERNAS: Jurnal Pengabdian Kepada Masyarakat Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian kepada Masyarakat DEIKTIS: Jurnal Pendidikan Bahasa dan Sastra Jurnal Pendidikan, Sains dan Teknologi Indonesian Research Journal on Education Jurnal Mataaalo : Masyarakat Peneliti Pendidikan Bahasa Indonesia AgroSainT: Jurnal Ilmiah INTERACTION: Jurnal Pendidikan Bahasa
Rita Tanduk
Universitas Kristen Indonesia Toraja
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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
COMPARISON OF NAIVE BAYES, DECISION TREE, AND RANDOM FOREST ALGORITHMS IN CLASSIFYING LEARNING STYLES OF UNIVERSITAS KRISTEN INDONESIA TORAJA STUDENTS Garonga, Melki; Rita Tanduk
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1020

Abstract

Learning style is an individual's habit or way of absorbing, processing, and managing information. This factor is very important in achieving learning goals. However, in reality, learning styles are often overlooked in the learning process, which can lead to suboptimal absorption of lessons and affect the quality of education. Various models have been developed by educational experts to identify students' learning styles, one of which is the VAK model (Visualization Auditory Kinesthetic) for grouping learning styles. This study compares algorithms in classifying learning styles using the VAK model. The results showed that the most dominant learning style was kinesthetic with a percentage of 46.9% or 478 students. The algorithm modeling showed that Naive Bayes had the highest accuracy with a value of 75%, while Random Forest had the lowest accuracy with a value of 59%. This suggests that Naive Bayes is more suitable for classifying students' learning styles. In conclusion, understanding students' learning styles is crucial for effective education. The VAK model is one way to identify learning styles, and Naive Bayes is a suitable algorithm for classifying students' learning styles. By considering learning styles, educators can tailor their teaching methods to better suit their students' needs and improve the quality of education.
Co-Authors ., Amsaliagusnawati ., Rina Agustina Allo, Herlina Payuk Amalia Pasongli Amsaliagusnawati, Amsaliagusnawati Anastasia Baan Anita Tika Amping Ardi Appulembang Ariani Paulus Berthin Simega Bonggala’bi, Bonggala’bi Bubun, Septika Priscillia Daud Rodi Palimbong Elvira Pasila Evi Layuk Dama Fitriani, Irna Garonga, Melki Gasong, Dina Herman Kandari Ilyas, Haryadi Ira, Veronika Labi, Bongga Lestari, Titin Damayanti Mangera, Elisabet Mangera, Monika Milka Milka Nelvan, Meiltianus Nirwanto Maruf Oktavianus, Randys Pawan, Fredy Perdy Karuru Rampo, Rosminati Regina Li’pang Resnita Dewi Rismayanti, Noni Salamba, Srikandi Sallolo Suluh Sallolo Suluh Serli Siampa’, Damaris Simega, Bethin Sukijan, Amoy Tandiallo, Erianto Theresyam Kabanga Yakub Tangdibiri’