Classification of Service Sentiments on the by.U Application using the Support Vector Machine Algorithm - Zulkarnain, Zulkarnain; Novita, Rice; Angraini, Angraini; Zarnelly, Zarnelly (Sistemasi: Jurnal Sistem Informasi, 2025)
This study aims to classify user sentiment toward the by.U application service using the Support Vector Machine (SVM) algorithm. The background of this research is based on the importance of understanding user opinions on the quality of digital services as a basis for evaluation and service improvement. Review data was collected from the Google Play Store, totaling 9,091 data points, which were then processed through preprocessing stages such as cleaning, case folding, tokenization, stopword removal, and stemming. Sentiments were categorized into three groups: positive, negative, and neutral. The training and testing process involved dividing the data into training and testing sets with an 80:20 ratio, and evaluation was conducted using metrics such as accuracy, precision, recall, and F1-score. The evaluation results showed that the SVM algorithm achieved an accuracy of 83% in classifying sentiments. The model performed best on positive sentiment (precision 84%, recall 90%, F1-score 87%) and negative sentiment (precision 81%, recall 92%, F1-score 86%), while neutral sentiment still had weaknesses with an F1-score of only 64%. This indicates that neutral sentiment classification still requires model enhancement. This study demonstrates that SVM is an effective method for automatically analyzing user opinions on digital services. These classification results can serve as a reference for developers in evaluating and improving service quality based on user feedback.