Jurnal Komtika (Komputasi dan Informatika)

The development of information technology has driven the increasing use of digital services such as Google Maps, which functions not only as a navigation tool but also as a platform for users to provide reviews. These reviews serve as an important data source for sentiment analysis; however, they are often unstructured and contain noise. This study aims to conduct sentiment analysis using the Support Vector Machine (SVM) model with the application of resampling techniques to address data imbalance issues in user reviews of the Google Maps application. A total of 1,000 recent reviews were collected through a scraping process, followed by data cleaning (lowercasing, stopwords removal, stemming, and lemmatization) and data preprocessing. The SVM model combined with resampling techniques was then implemented and evaluated using accuracy, precision, and recall metrics. The results indicate that the SVM model achieved an accuracy of 81%, with a weighted average precision of 0.79, recall of 0.81, and F1-score of 0.76. These findings demonstrate that applying resampling techniques to SVM yields good performance in sentiment classification. The study is expected to contribute to the development of sentiment analysis methods using the SVM model with resampling in the context of Google Maps reviews.