Analisis Sentimen Restoran Ulasan Pelanggan Dengan Menggunakan Algoritme Naive Bayes Untuk Meningkatkan Kualitas Pelayanan Restoran “Warung Pedes Gemes”

In the digital era, customer reviews play an important role in shaping a restaurant’s reputation and assessing the quality of its services. Feedback provided by customers through reviews can be utilized as a valuable source of information for service improvement. This study aims to analyze customer sentiment toward Warung Pedes Gemes restaurant using a sentiment analysis approach based on the Naive Bayes algorithm. Review data were collected through questionnaires and processed through several stages, including data preprocessing, weighting using Term Frequency–Inverse Document Frequency (TF-IDF), sentiment classification, and model evaluation using a confusion matrix. The results indicate that the majority of customer reviews were classified as positive, with 543 positive reviews and 22 negative reviews identified. The performance evaluation of the Naive Bayes model shows an accuracy, precision, recall, and F1-score of 100%. These findings demonstrate the effectiveness of the Naive Bayes algorithm in accurately classifying customer sentiment. Therefore, this study contributes to the application of data-driven sentiment analysis as a supporting tool for improving service quality in the culinary industry.