<![CDATA[Telkomsel, the largest operator in Indonesia with the most users, collects a significant amount of tweet data on Twitter, containing both positive and negative feedback about their internet service. Analyzing this data can provide valuable insights and accurate information about Telkomsel's internet services based on user tweets, retweets, and comments. The aim is to build a sentiment analysis model to extract relevant information from Telkomsel users' tweets on Twitter, serving as feedback for service evaluation and an educational tool for users. The sentiment analysis process involves data retrieval, preprocessing, training, testing, classification, and visualization using Python programming with the Flask framework. Analysis of customer satisfaction sentiment reveals that Telkomsel has a negative sentiment, with an accuracy of 81.7% for training data and 84% for testing data. The sentiment analysis model was built using the Naive Bayes Classification method.]]>
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