Sentiment Analysis Of Mie Gacoan Reviews In Blitar City On The Grab Application Using The Support Vector Machine Method - Bayu Samudra; Sri Lestanti; Rizky Dwi Romadhona (JOSAR (Journal of Students Academic Research), 2025)
This study aims to analyze customer review sentiments for Mie Gacoan restaurant in Blitar City through the Grab application using the Support Vector Machine (SVM) algorithm. Customer reviews are categorized into three sentiment classes: positive, negative, and neutral. And the total amount of data used is 991 data. The research process includes manual data collection, text preprocessing, weighting using the TF-IDF method, and classification with the SVM algorithm. Model performance is evaluated using a confusion matrix with precision, recall, and F1-score metrics. And for the testing of the algorithm method using Grid Search and Cross Validation. The results show that the linear kernel achieves the best performance with an F1-score of 0.4649. Positive sentiment dominates the reviews, while negative and neutral sentiments are less prevalent. This study demonstrates that SVM is effective for classifying sentiments in customer reviews and can assist restaurant managers in identifying areas for service improvement.