The Fight Against Fiction: Leveraging AI for Fake News Detection
Article Sidebar
Context-Specific Nature of News,
Fake News Detection
Main Article Content
Misinem Misinem
Dinny Komalasari
Nurul Adha Oktarini Saputri
Abstract
This study aims to evaluate the performance of three machine learning algorithms namely Logistic Regression, Naïve Bayes, and Random Forest in classifying fake news. The research methods include data collection from various news sources, text preprocessing to improve data quality, and context-based feature engineering that considers temporal, linguistic, and named entity aspects. Furthermore, the model is developed using a machine learning approach that integrates ensemble techniques to improve prediction accuracy. Evaluation was conducted using accuracy, precision, accuracy, and F1 score metrics. The experimental results showed that Random Forest performed best with an accuracy of 93.00%, superior to Naïve Bayes (89.96%) and Logistic Regression (91.00%). This analysis confirms that algorithm selection should be tailored to the specific needs of the project, with Random Forest being a more reliable choice for scenarios that require high accuracy and robustness to data complexity. The findings are expected to contribute to the development of fake news detection systems that are more effective and adaptive to the dynamics of information in the digital world.
Article Details
Copyright (c) 2025 Misinem Misinem, Dinny Komalasari, Nurul Adha Oktarini Saputri
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
Abiodun, E. O., Alabdulatif, A., Abiodun, O. I., Alawida, M., Alabdulatif, A., & Alkhawaldeh, R. S. (2021). A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities. Neural Computing and Applications, 33(22), 15091–15118. https://doi.org/10.1007/s00521-021-06406-8
Abouhawwash, M. (2024). Innovations in Cyber Defense with Deep Reinforcement Learning: A Concise and Contemporary Review. Artificial Intelligence in Cybersecurity, 1, 44–51. https://doi.org/10.61356/j.aics.2024.1298
Alghamdi, J., Luo, S., & Lin, Y. (2024). A comprehensive survey on machine learning approaches for fake news detection. Multimedia Tools and Applications, 83(17), 51009–51067. https://doi.org/10.1007/s11042-023-17470-8
Bachmann, P., Eisenegger, M., & Ingenhoff, D. (2022). Defining and Measuring News Media Quality: Comparing the Content Perspective and the Audience Perspective. International Journal of Press/Politics, 27(1), 9–37. https://doi.org/10.1177/1940161221999666
Broda, E., & Strömbäck, J. (2024). Misinformation, disinformation, and fake news: lessons from an interdisciplinary, systematic literature review. Annals of the International Communication Association, 48(2), 139–166. https://doi.org/10.1080/23808985.2024.2323736
Choi, S., Shin, H., & Kang, S. S. (2021). Predicting Audience-Rated News Quality: Using Survey, Text Mining, and Neural Network Methods. Digital Journalism, 9(1), 84–105. https://doi.org/10.1080/21670811.2020.1842777
Gupta, M., Dennehy, D., Parra, C. M., Mäntymäki, M., & Dwivedi, Y. K. (2023). Fake news believability: The effects of political beliefs and espoused cultural values. Information and Management, 60(2), 1–12. https://doi.org/10.1016/j.im.2022.103745
Hamdikatama, B. (2025). BEYOND ALGORITHMS : AN INTEGRATED APPROACH TO FAKE NEWS. JITK (JURNAL ILMU PENGETAHUAN DAN TEKNOLOGI KOMPUTER), 10(3), 609–622. https://doi.org/10.33480/jitk.v10i3.6061.BEYOND
Kwon, S., & Jang, B. (2025). A Comprehensive Survey of Fake Text Detection on Misinformation and LM-Generated Texts. IEEE Access, 13(January), 25301–25324. https://doi.org/10.1109/ACCESS.2025.3538805
Lin, C. H., & Nuha, U. (2023). Sentiment analysis of Indonesian datasets based on a hybrid deep-learning strategy. Journal of Big Data, 10(1). https://doi.org/10.1186/s40537-023-00782-9
Mridha, M. F., Keya, A. J., Hamid, M. A., Monowar, M. M., & Rahman, M. S. (2021). A Comprehensive Review on Fake News Detection with Deep Learning. IEEE Access, 9, 156151–156170. https://doi.org/10.1109/ACCESS.2021.3129329
Priya Varshini, A. G., Anitha Kumari, K., & Varadarajan, V. (2021). Estimating software development efforts using a random forest-based stacked ensemble approach. Electronics (Switzerland), 10(10), 1–19. https://doi.org/10.3390/electronics10101195
Ruchansky, N., Seo, S., & Liu, Y. (2017). CSI: A hybrid deep model for fake news detection. International Conference on Information and Knowledge Management, Proceedings, Part F131841, 797–806. https://doi.org/10.1145/3132847.3132877
Shen, Y., Liu, Q., Guo, N., Yuan, J., & Yang, Y. (2023). Fake News Detection on Social Networks: A Survey. Applied Sciences (Switzerland), 13(21), 1–19. https://doi.org/10.3390/app132111877
Wang, Y., McKee, M., Torbica, A., & Stuckler, D. (2019). Systematic Literature Review on the Spread of Health-related Misinformation on Social Media. Social Science and Medicine, 240(August), 112552. https://doi.org/10.1016/j.socscimed.2019.112552