<![CDATA[The rapid development of the world of technology and communication cannot be separated from web service providers who always provide various information. One example is text data taken from Twitter. Twitter is a social media and micro blogging service that allows its users to post realtime messages. This message is popularly known as a tweet, by using it, users will find it easy to follow trends, stories, information and news from all corners of the world. Problems regarding the construction of the Jakarta-Bandung fast train (KCJB) which required large funds, not to mention the transfer of the route from Jakarta-Surabaya to Jakarta-Bandung which resulted in a large increase in funds. So that it raises a problem such as differences of opinion and statements of approval and disapproval of the construction of the Jakarta-Bandung fast train. The method used is Naive Bayes because it has a high probability or opportunity value for classifying data, for weighting using TF-IDF calculations, and testing data using a confusion matrix. The solution to the problem is sentiment analysis, grouping and at the same time helping data from predicting the tweet data. . The purpose of this study is to analyze the sentiments of Twitter users towards the construction of the Jakarta-Bandung fast train using the Naive Bayes method by calculating the value of the KCJB tweet data which has a total of 2390 data which after going through the cleaning process becomes 2007 data. After processing with 673 negative sentiment results, 668 positive sentiment results, and 665 neutral sentiment results, 71% accuracy, 73% precision, recall]]>
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