<![CDATA[TIX ID is an online cinema ticket purchasing application that plays on a smartphone platform. Where users can buy cinema tickets anywhere and anytime without having to wait in line. The concept of purchasing cinema tickets is integrated with a third party, namely DANA as a digital money concept that is integrated with several large applications such as Tokopedia and Shopee. This study aims to Conduct text preprocessing (NLP) on TIX ID application user review data so that the data is ready to be used in the sentiment analysis process, Extract text features using the Term Frequency–Inverse Document Frequency (TF-IDF) method to represent reviews in numeric form, Apply the Naive Bayes and Support Vector Machine (SVM) algorithms in classifying user review sentiments into positive and negative categories, Evaluate the performance of the Naive Bayes and Support Vector Machine (SVM) models using accuracy, precision, recall, and F1-score metrics, Provides an overview of user sentiment towards the TIX ID application as a consideration for developers in improving the quality and service of the application.]]>
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