Pemodelan Analisis Sentimen Ulasan Pengguna Aplikasi Info Bmkg Menggunakan Pendekatan Multinomial Naïve Bayes - Moh. Syaogi; Nur Ariesanto Ramdhan; Otong Saeful Bachri; Bambang Irawan (Fakultas Teknik Universitas Muhammadiyah Parepare, 2026)
Info BMKG is one of several digital platforms that have been pushed by the fast evolution of IT to replace traditional methods of providing public services. Reviews on the Play Store can be used to determine user perceptions and levels of satisfaction with the application. Manual analysis is laborious and inefficient due to the high number of evaluations. Consequently, the purpose of this research is to use the Naive Bayes algorithm to categorize evaluations of the Info BMKG app as either positive or negative in order to do sentiment analysis. Using a web scraping approach, a total of 5,000 user evaluations were obtained for the study data. Next, the data underwent text preprocessing, word weighting using the TF-IDF technique, and sentiment classification with the Multinomial Naive Bayes algorithm. There was an 80:20 split between the dataset's training and testing sets. The experimental findings show that the Naive Bayes algorithm achieves an accuracy of 87.83% on the testing data when it comes to classifying user review emotions.