<![CDATA[This study utilizes Twitter data to understand user opinions and emotions towards an application. The Decision Tree method was chosen due to its ability to describe the relationship between input variables and the target. The TF-IDF method was used to weight words in the text, and the confusion matrix was used to evaluate the accuracy of the classification model. The research included the research process flow, data preprocessing, and data modeling. Word cloud visualization was used to display the frequency of words in the text. Data was collected from Twitter using Python and the Tweepy library. After preprocessing, the data was categorized into positive, negative, and neutral labels. The evaluation results using the decision tree algorithm showed an accuracy of 96%. The word cloud revealed that the word "aplikasi" (application) has the highest frequency, which shows the importance of the PLN Mobile application but also shows the need for further development. This study provides insights into user sentiment towards the PLN Mobile application and demonstrates the effectiveness of the Decision Tree method in sentiment analysis]]>
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