KLASIFIKASI BERITA HOAX POLITIK PADA MEDIA SOSIAL X MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN RANDOM FOREST - Astuti, Rika; Tiarma Sipahutar, Angel (Universitas Siber Indonesia, 2025)
Social media has become a major source of information as well as a fertile ground for the spread of hoax news, particularly political news. This study examines two classification algorithms, Naive Bayes and Random Forest, to detect hoax news. The data used consists of political news labelled as hoax or non-hoax. The results show that the Random Forest algorithm has better accuracy and performance compared to Naive Bayes in terms of precision, recall, and F1-score. In conclusion, Random Forest is more effective for classifying political hoax news on social media. This research provides important insights into the application of machine learning algorithms in text classification and emphasizes the importance of selecting the right algorithm for optimal results.