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All Journal Journal of the Indonesian Mathematical Society Journal of Mathematical and Fundamental Sciences Journal of Engineering and Technological Sciences Indonesia Symposium on Computing KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Limits: Journal of Mathematics and Its Applications Jurnal Matematika UNAND Journal of Research in Mathematics Trends and Technology Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun MATHunesa: Jurnal Ilmiah Matematika Jurnal Siger Matematika Jurnal Matematika Integratif BACA: Jurnal Dokumentasi dan Informasi Integra: Journal of Integrated Mathematics and Computer Science Limits: Journal of Mathematics and Its Applications
Aang Nuryaman
Universitas Lampung
Astronomy Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Journal : Integra: Journal of Integrated Mathematics and Computer Science

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Comparison of Naïve Bayes and Random Forest Models in Predicting Undergraduate Study Duration Classification at the University of Lampung Hestina P., Shelvira; Widiarti; Nuryaman, Aang; Usman, Mustofa
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 3 (2024): November
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241317

Abstract

This study aims to compare the performance of the Naïve Bayes and Random Forest classification algorithms in predicting the study duration of undergraduate students in the Mathematics Study Program at the University of Lampung. The dataset consists of 537 graduation records from 2020–2024. The research steps include data preprocessing, data partitioning (train-test split and k-fold cross validation), model building, and evaluation using a confusion matrix. The results show that the Random Forest algorithm achieved the highest accuracy of 94.44%, outperforming Naïve Bayes which reached a maximum accuracy of 92.59%. These findings suggest that Random Forest is more effective for classifying student study durations. These findings suggest that Random Forest is more effective for classifying student study durations.
Co-Authors . Wamiliana Agus Yodi Gunawan Ahidiyah, Siti AHMAD FAISOL Ahmad Faisol Akmal Akmal Alam, Muhammad Fikri Anne Parlina Ardiyanti, Lily Asmiati Asmiati Asmiati Asmiati Asmiati Asmiati Atika Faradilla Bagus Hayatul Jihad Bagus Hayatul Jihad Citra Puspa Tria Dalfa Habibah Nurul Aini Danuari Fitrio, Beni Dewi Rakhmatia Nur Dian Kurniasari Dian Kurniasari Dina Eka Nurvazly Eri Setiawan Eri Setiawan Fachri Faisal Faradilla, Atika Fathudin Fathudin Fatimatuzzahra Fatimatuzzahra Fitriani Fitriani Fitriani, Debi Nur Hestina P., Shelvira Ismawati Ismawati Khotimah, Purnomo Husnul Kuntjoro Adji Sidarto Kurniawati, Virda La Zakaria La Zakaria Mardani, Maya Mega Putri Muhammad Ihsan Abyan Muhammad Ihsan Abyan Mulia Astuti Mustofa Usman Mustofa Usman Nabilla Yolanda Paramitha Nisa, Rizki Khoirun Notiragayu Notiragayu Nur Hamzah Nur, Dewi Rakhmatia Ranti Vidia Mahyunis Raratia ulsa Alfian Rudi Ruswandi Rudi Ruswandi Saidi, Subian Saiful Rohman Sihombing, Andre Soleh Fajar Junjunan Soleh Fajar Junjunan Sutrisno, Agus Swita, Baki Utami, Bernadhita Herindri Samodera Warsono Warsono Warsono Warsono Widarti, Widiarti Widiarti Widiarti Widiarti Wiranto, Ahmad Rizki Yogi Wibisono Budhi Zakaria, La