Penerapan Metode Naïve Bayes Classifier Untuk Klasifikasi Sentimen Pada Judul Berita - Astuti, Yani Parti; Wibowo, Alrico Rizki; Kartikadarma, Etika; Subhiyakto, Egia Rosi; Sri Winarsih, Nurul Anisa; Rohman, Muhammad Syaifur (Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan, 2024)
News has a major role as a source of information to convey reports on opinions, events, and the latest findings in various aspects of life. News headlines, as an important component, can be a determinant of news content. The sentiment contained in news headlines can be classified using sentiment analysts, as is the case in the online media platform Kompas.TV. News headlines are retrieved using an automated program that utilises the HTML body with the help of NodeJs as the technology for program creation. This research is focused on the application of Naïve Bayes Classifier method to classify sentiment on Kompas.TV news headlines in Semarang City. The results showed an accuracy rate of 91.04%, with a ratio of training data and test data of 90:10. The conclusion of this study is that the Naïve Bayes Classifier method is effective in identifying news headlines with negative sentiment on Kompas.TV, with a precision of 89% and recall of 94%. This finding makes a positive contribution to the understanding of sentiment analysis on news headlines in online media, especially in the context of Kompas.TV news in Semarang City.