Analisis Sentimen Terhadap Kinerja Kepolisian Indonesia Menggunakan Metode Multinomial Naive Bayes, Long Short-Term Memory, dan Lexicon-Based - Nurpandi, Finsa; Sulaeman, Fietri Setiawati; Hermawan, Aditya (Teknik Informatika Universitas Suryakancana Cianjur, 2024)
According to the Indonesian Survey Institute, the public trust index in the Indonesian Police decreased to 53% as of October 2022. The level of public satisfaction and trust in police can be measured by examining how the police handle each case that occurs. This research focuses on analyzing and measuring the public’s sentiment toward the police’s performance among Twitter users in Indonesia. After scraping (collecting) data from Twitter, out of the 14,000 collected tweets, only 12,222 tweets were for analysis after the preprocessing stage. Then, the data was labeled into two categories, negative sentiment and positive sentiment using InSet Lexicon. The results showed that 53% (6,478) tweets have a negative sentiment, while 47% (5,744) tweets have a positive sentiment. The topics frequently appear and discussed related to such as the shooting case of Brigadier J, the Kanjuruhan tragedy, the security of the G20 Bali Summit, securing Christmas 2022 and securing New Year 2023. Subsequently, classification was performed on the test data using the Multinomial Naïve Bayes and Long Short-Term Memory (LSTM) methods. In the testing phase, a confusion matrix used to calculate accuracy, recall, precision, and f1-score. The results showed that the Multinomial Naïve Bayes method achieved an accuracy score of 78%, precision of 78.5%, recall of 77%, and an f1-score of 77%, with an average score of 77.6%. Similarly, the LSTM method resulted in an equal accuracy, precision, recall, and f1-score of 87%.