Deteksi Hate Speech pada Unggahan Media Sosial dengan Naive Bayes Menggunakan Seleksi Fitur Chi-Square - Putu Steven Belva Chan; Ida Ayu Gde Suwiprabayanti Putra (Jurnal Nasional Teknologi Informasi dan Aplikasinya, 2024)
In the digital age, social media’s pervasive use has revolutionized global communication but also introduced challenges like hate speech. This study proposes a Multinomial Naive Bayes model optimized with Chi-square feature selection to detect hate speech efficiently from large-scale social media data. Leveraging machine learning, this approach aims to combat harmful content by identifying relevant text features crucial for distinguishing hate speech from non-hate speech. The study utilizes TF-IDF for feature extraction and Chi-square for feature selection, showing significant performance improvements in hate speech detection. The Chi-square feature selection model yielded average precision, recall, F1-score, and accuracy values of 92%, 92%, 91%, and 92% respectively. In contrast, the model without feature selection achieved values of 89%, 89%, 88%, and 89% for the same metrics. Results demonstrate enhanced accuracy, precision, recall, and F1-score across various hate speech categories.