Sentiment Classification of Google Maps Reviews for Tepian Pandan Restaurant Using Support Vector Machine - I Made Borneo Setyawan; Heny Pratiwi; Kusno Harianto (Politeknik Pertanian Negeri Samarinda, 2026)
The rapid development of information technology has driven the increasing use of online review platforms as a means of sharing consumer experiences. Customer reviews now serve not only as a medium for expressing opinions but also as a valuable source of data in measuring the level of public satisfaction with a business, particularly in the culinary field. One of the most widely used platforms is Google Maps, which allows customers to provide ratings and comments regarding food quality, service, price, and the atmosphere of the place. The information presented in text form can be further analyzed to obtain a general overview of consumer perceptions. This study aims to analyze public satisfaction sentiment towards Tepian Pandan Restaurant based on reviews found on Google Maps by applying the Support Vector Machine (SVM). The method used refers to the text approach. mining which includes several stages, namely collecting review data, text preprocessing (such as case folding, tokenizing, and data cleaning), feature extraction using the Term Frequency – Inverse method Document Frequency (TF-IDF), and sentiment classification using the SVM model. The processed reviews were then grouped into two main categories: positive sentiment and negative sentiment. To assess model performance, this study used evaluation metrics such as accuracy, precision, recall, and F1-score. The test results showed that the Support Vector Machine (SVM) model was able to classify review sentiment with good and consistent performance. Therefore, this approach is considered effective in identifying customer satisfaction levels based on online review data. The findings of this study are expected to inform restaurant management's efforts to improve service and product quality based on customer feedback.