<![CDATA[Twitter has become an essential platform for traders and stock investors worldwide, including major countries like America. Traders rely on Twitter to gather information, similar to how they use Bloomberg terminals. While Twitter provides valuable insights, it also contains negative elements such as false information. The sentiment surrounding stocks on Twitter has been growing, and this study aims to analyze the sentiment of Telkom Indonesia's stock price based on tweets. The research involved several stages. First, data was collected from Twitter and labeled manually into positive, neutral, and negative sentiments. The data then underwent pre-processing, including cleaning and dividing it into training and testing datasets using K-Fold Cross Validation. The data was further weighted using the TF-IDF method, and a training process was conducted to develop a model. The final stage involved testing the accuracy of the model. The study successfully implemented the Multinomial Naïve Bayes (MNB) method, achieving an accuracy of 89.0%. The tweet classification results revealed that out of 1000 tweets, 76.5% were classified as positive, 14.3% as negative, and 9.2% as neutral.]]>
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