<![CDATA[In this digital era, technology pervades every aspect of daily life, revolutionizing industries and interactions. Within this landscape, online-based transportation services have emerged as transformative solutions, notably exemplified by Gojek in Indonesia. This study delves into sentiment analysis of Gojek reviews using Multinomial Naive Bayes and Bag-of-Words extraction, aiming to gauge user perceptions and responses. Leveraging a dataset of 9.996 App reviews, the research undertakes comprehensive preprocessing, including case folding, filtering, tokenization, stopword removal, and stemming, followed by sentiment labeling. By employing Bag-of-Words feature extraction, textual data is converted into numerical vectors, enabling the application of the Multinomial Naive Bayes classification model. Evaluation metrics, derived from a confusion matrix, reveal an accuracy rate of 86.29%, with precision, recall, and F1-Score values of 86.94%, 86.41%, and 86.26% respectively. This study underscores the efficacy of the adapted Multinomial Naive Bayes model with Bag-of-Words feature extraction in discerning user sentiments towards Gojek, offering valuable insights for enhancing service applications in the digital realm. ]]>
Copyrights © 2025
