ALGORITMA NAÏVE BAYES UNTUK ANALISIS SENTIMENT REVIEW BLIBLI.COM DI GOOGLE PLAY STORE - Fadhilah, Siti Nur; Utomo, Fandy Setyo (Sistemasi: Jurnal Sistem Informasi, 2024)
Currently, there are approximately 354 million active mobile phones in Indonesia, placing the country fourth globally in terms of the highest number of mobile phone users. E-Commerce, as a form of online transaction, enables the digital exchange of goods and services to meet daily needs. This research aims to implement sentiment analysis using the Naive Bayes classification algorithm as a method to gather user opinions. Thus, the study not only provides insights into customer satisfaction with Blibli.com but also serves as a basis for potential improvements in services or feature development to enhance the online shopping experience. Overall, the Naive Bayes algorithm successfully achieved an accuracy of around 84%, demonstrating its proficiency in categorizing sentiment in reviews. When focusing on negative data, the Naive Bayes algorithm exhibited a precision of approximately 79%, recall of around 95%, and an f1-score of about 86%, indicating its success in identifying and classifying negative reviews with high precision and sensitivity. On the positive side, the Naive Bayes algorithm achieved a precision of about 91%, recall of around 83%, and an f1-score of about 87%.