Klasifikasi Emosi Publik di Instagram Terhadap Uji Coba Jalan Satu Arah di Palembang dengan Naive Bayes - Ariantoro, Tri Rizqi; Setiadi, Arif; Cecilia, Cecilia; Fadhliazi, Fadhliazi; Sri Handini, Nurul (Algoritme Jurnal Mahasiswa Teknik Informatika, 2025)
Traffic congestion in Palembang City remains a major issue despite various traffic engineering policies, including the implementation of a one-way system trial. This study aims to classify public emotions toward the policy based on comments from Instagram users. The research method includes data collection through web scraping, translation of local dialects into Indonesian using Generative AI, manual emotion labeling, text preprocessing, feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF), and classification with the Multinomial Naive Bayes algorithm optimized by hyperparameter tuning. The evaluation results show an accuracy of 69.66%, precision of 63.09%, recall of 69.66%, and F1-score of 62.86%. The “anticipation” emotion emerged as the most dominant class, while “love” and “annoyance” were underrepresented. These findings indicate that the Naive Bayes-based text mining approach is effective for analyzing public emotional responses to government policies.