Analisis Sentimen Komentar Film Merah Putih One For All Metode Naïve Bayes

Abstract

Social media Twitter (X) has become a platform for expressing public opinion, including reactions to the animated film "Merah Putih: One For All." The film has garnered various criticisms regarding its graphic quality and the use of digital assets deemed unoriginal. Sentiment analysis of this public response is essential to provide an objective evaluation of society's reception of local animated works, identify specific aspects of audience concern, and offer valuable insights for the Indonesian animation industry to improve production quality. Automated sentiment classification using machine learning is an efficient solution for understanding patterns of public perception with large volumes of data, which is difficult to accomplish manually. This study classifies comments on the X platform into positive and negative sentiment categories using the Naïve Bayes method. Data collection was conducted by scraping posts from the account @tanyakanrl in August 2025, totaling 302 comments. The data underwent preprocessing stages, including cleaning, case folding, normalization, tokenizing, stopword removal, and stemming. Feature extraction utilized the TF-IDF (Term Frequency-Inverse Document Frequency) method to convert text data into numerical representation. Classification employed the Naïve Bayes algorithm with an 80:20 data split ratio for training and testing. Evaluation results indicate that the model achieved an Accuracy of 82%, Precision of 79%, Recall of 82%, and an F1-Score of 80%.