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Implementation of the Multinomial Naïve Bayes Algorithm in a Web-Based System for Detecting Online News Hoaxes During the 2024 Elections Juliawati Haribae; Irene R.H.T Tangkawarow; Gladly C. Rorimpandey
Journal of Vocational, Informatics and Computer Education Vol 4, No 3 (2026): September 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i3.608

Abstract

Purpose – This study addresses the escalation of disinformation during the 2024 Indonesian General Election by developing an automated hoax detection system. The primary focus is to evaluate the integration of data balancing methods to minimize detection failures in hoax narratives, which often appear less frequently than factual news in real-world scenarios. Methods – The dataset consists of 1,529 unique news documents, comprising 1,020 factual articles from Kompas.com and 509 hoax articles from TurnBackHoax.id. The modelling workflow involves text preprocessing, feature extraction via Term Frequency-Inverse Document Frequency (TF-IDF), and the application of the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. The model was developed using the Multinomial Naive Bayes algorithm and integrated into a web-based platform using the Flask framework. Findings – Results from 5-fold cross-validation demonstrated a stable average accuracy of 92.64% (±1.27%). On an independent test set, the model achieved 89.54% accuracy with a hoax class recall of 0.93. This proves that SMOTE significantly enhances sensitivity in identifying disinformation, reducing the risk of false information bypassing the system. Research implications – The study is limited to textual data from curated sources and cannot yet capture multimedia disinformation or high levels of sarcasm. Future research should explore Transformer-based models for deeper semantic context understanding. Originality – This research provides a functional public verification platform with a response time of under one second. Its novelty lies in a domain-specific detection system that balances algorithmic efficiency with precision on imbalanced real-world election data.
Co-Authors Alfiansyah Hasibuan Alfrina Mewengkang Archangela G. Repi Ayu Triana Situmorang Batmetan, Johan R Bonenehu, Kurniawan Brandon Natanael Gerungan Chrysilia Rimbing Cindy Pamela C Munaiseche Civita Loho Clay Justin Wowor Efraim R. S. Moningkey Febiola Emilliany Christiani Makarawung Friliandra Valentcia Sumendap Friska Regina Maralantang Gitarosalina Sibarani Glenn D. P. Maramis Grasella Pandey Gupuh, Andi Hamsah Hamsah Hasibuan, Alfiansyah Hendro Maxwell Sumual Henri Rompas HS, Fatimah Hulu, Lena Enjelin Imanuel Wowor Intan, Sondakh Agnes Irene R.H.T Tangkawarow Jessica Daniel Joni Juliawati Haribae Kainde, Quido C Kenap, Audy A. Krina Crisila T. Mawuntu Kristofel Santa Kristofel Santa Kristofel Santa Lipan, Kezia Lumataw, Alfred Tenda Lumenta, Harfey Mamonto, Nurmila Mantik, Felitia Theona Geofani Maramis, Glenn D. P. Marcelliano Riccardy Anantho Omega Kalitouw Mario Trinto Risky Rettob Martina Lorensa Maswonggo, Vandi Vanda Medi Hermanto Tinambunan Melati Roring Merlin Aprilia Liow Meyn Choudy Riovan Kaotel Moh Ramdhan Arif Kaluku Moningkey, Efraim R.S Muhammad Zulkifli Mumpel, Stevyoman Oktavianus Ngovangari, Rivchi Hanni Oliver Simon Hardianto Raharusun Olivia Kembuan parabelem tinno dolf rompas Ponggohong, Alfa Riegel Imanuel Quido Conferti Kainde Quido Conferti Kainde Ramdan Adjis Rapar, Pedro Vincensius Rawung, Lanamey Praisy Indiana Readel Lantang Beryl Mangkey Refandi Andika Runtu Rombon, Natalia Arsel Rompas, Billy Setia Handayani Sinaga Silalahi, Rike Sondy C. Kumajas Sondy Campvid Kumajas Sondy Campvid Kumajas Stevyoman Oktavianus Mumpel Sulaeman, Raffy Avian Supit, Charenia Syalomita Cinta Tameo, Vito Ernesto Tandiapa, Saron Tandiapa, Solagratia Saron Tesalonika Gracia Palilingan Tiwi, Heri Susan Turnip, Maydupi Astri Vandi V.Maswonggo Vincensius Rapar, Pedro Vira Chlaudia Makahinda Vivi Peggie Rantung