<![CDATA[The development of digital technology has driven the wider adoption of e-ticketing systems on the Tiket.com application, which is increasingly used by the public. However, the increasing number of users doesn't always correlate directly with satisfaction levels, as evidenced by the diverse user reviews on the Google Play Store. This research is urgent for analyzing user sentiment as a basis for improving service quality. The purpose of this research is to classify reviews into positive and negative sentiments using the Support Vector Machine (SVM) algorithm and to analyze dominant word patterns thru the Apriori algorithm. The research data consists of 5,000 Indonesian-language reviews collected between 2023 and 2025, which were then processed thru preprocessing, TF-IDF weighting, SVM classification, association analysis with Apriori, and result visualization using Streamlit. The research results show that SVM produces a high level of accuracy in sentiment classification, while association analysis reveals dominant words that reflect user satisfaction and complaints. The integration of these two methods provides a more comprehensive understanding of user opinions and is expected to serve as a basis for developing service improvement strategies and as a reference for further research in the field of machine learning-based sentiment analysis.]]>
Copyrights © 2026
