Sentiment Analysis of Tourist Reviews at Waterfront City Pangururan Using Naive Bayes and TF-IDF Algorithm

This research aims to analyze the sentiment of tourist reviews on Waterfront City Pangururan tourist destination using Naive Bayes algorithm and TF-IDF method. Data was collected from 311 Google Maps reviews, then went through preprocessing stages such as data cleaning, tokenization, stopwords removal, and stemming. A classification model was built using Naive Bayes, with evaluation results showing an accuracy of 90.48% and a predominance of positive sentiments in the reviews. These results indicate that this approach is effective in automatically classifying travelers' opinions. The findings can be a valuable input for managers and local governments in improving the quality of tourism services and attractions.