<![CDATA[Fake news has become a serious problem in today's digital era. The existence of fake news can have various negative impacts, including the spread of misinformation, social unrest, and economic losses. This study compares the performance of Naïve Bayes and Random Forest classification methods in detecting fake news. Both methods were evaluated on a news dataset comprising 44,898 samples. It uses public data from the Kaggle repository. The news samples are represented by four features: title, news content, subject, and news date. This data is then subjected to cleaning, stemming, tokenization, and feature extraction. The results indicate that the Random Forest method outperforms the Naïve Bayes method. The Random Forest method has an accuracy of 99%, while the Naïve Bayes method has an accuracy of 96%. In general, this research demonstrates that the Random Forest method can be a viable alternative for detecting fake news.]]>
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