Klasifikasi Metode Naïve Bayes pada Ulasan Pengguna Aplikasi Dazzcam untuk Pengeditan Foto Vintage di App Store - Salsa Dwi Agistina; Irwansyah; Agus Budiyantara; Faldy Irwiensyah (DIGINTEL-AI : DIGital INnovation and inTELligence – AI, 2026)
The rapid growth of mobile applications has increased the importance of user-generated reviews as a source of information for evaluating application quality and user satisfaction. Dazzcam, a photo editing application known for its vintage-style filters, has gained significant popularity among iOS users. This study aims to classify user reviews from the App Store into positive and negative sentiment categories using the Naïve Bayes algorithm and to evaluate the performance of the model. A total of 911 reviews were collected and divided into training and testing datasets with a ratio of 80:20. The research methodology includes data preprocessing, feature extraction using TF-IDF, and classification using Naïve Bayes, followed by evaluation with a confusion matrix. The results show that 712 reviews were classified as positive and 199 as negative, with an accuracy of 79.78%, precision of 79.89%, recall of 79.78%, and F1-score of 79.53%. These findings indicate that the Naïve Bayes algorithm demonstrates good performance and can be effectively utilized for sentiment analysis of application reviews.