Integration of the Naive Bayes Algorithm in Website-Based Detection of Hoaxes Related to Nutritious Food Health Information - Reza Bakara, Khairul; Didi Febrian; Yulita Molliq Rangkuti; Zulfahmi Indra; Kana Saputra S, (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2025)
There is an intelligent solution for automatic detection due to the increasing number of health-related hoaxes, especially those concerning nutritious food. The aim of this study is to integrate the Multinomial Naive Bayes algorithm into a hoax detection system that focuses on health information about nutritious food found on the internet. A quantitative method was employed, using the Multinomial Naive Bayes algorithm and the Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction technique. The dataset used consists of 1,000 Indonesian-language news articles collected from five platforms: TurnBackHoax for hoaxes, and Detik, Kompas, Tempo, and the Ministry of Health for valid news. The data was divided into 800 training samples and 200 testing samples. The results of this study show a Precision of 0.9717, Recall of 0.9712, and F1-Score of 0.9712, as indicated by the Weighted Average, which accounts for the number of instances in each class. The overall model accuracy is 0.97125, based on the proportion of correctly classified data. These findings demonstrate that the system is capable of identifying distinctive linguistic patterns that differentiate between valid and invalid information. This indicates that probabilistic statistical techniques such as Naive Bayes are highly suitable for use in text-based fake information detection, particularly in the domain of health-related nutritious food information.