Sentiment Analysis of Mie Gacoan Pemuda Cirebon Restaurant Reviews Using Support Vector Machine - Darusman, Aditya (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2026)
The growth of digital platforms has increased the use of sentiment analysis to understand public perceptions of business services. Customer reviews on Google Maps provide valuable insights but are unstructured and linguistically diverse, requiring robust analytical methods. This study conducts sentiment analysis on reviews of Mie Gacoan Pemuda Cirebon using a Support Vector Machine (SVM) classifier. The research focuses on designing an effective text preprocessing pipeline, identifying sentiment distribution, and evaluating SVM performance. The methodology includes web scraping, manual labeling, text preprocessing, TF-IDF feature extraction, dataset splitting, model training, and evaluation using accuracy, precision, recall, and F1-score. The results show that the majority of reviews are positive, and the SVM model achieves strong performance with an accuracy of 0.82. These findings provide an objective overview of customer perceptions and demonstrate the effectiveness of SVM for Indonesian-language sentiment classification. The model can support businesses in improving service quality based on customer feedback.