<![CDATA[Thrifting is an increasingly popular second-hand shopping activity in Indonesia, especially among millennials and generation Z as a cost-saving shopping alternative. Thrifting activities have a clear positive impact on the Indonesian people in protecting the environment by reducing the purchase of new goods. However, thrifting is considered illegal and can harm the domestic textile industry. So sentiment analysis needs to be done to find out how people respond to thrifting activities. This study aims to calculate the number of positive and negative comments from Twitter users, and find out how accurately the Naïve Bayes algorithm is used in the classification. The data used is taken from Twitter social media as many as 900 tweets, then processed through several advanced stages such as pre-processing which consists of cleansing, tokenize, and filter stopwords. Then at the labeling stage the data is divided into training data and test data with a ratio of 60:40. After being classified using the Naïve Bayes algorithm, the results obtained tend to be positive with a total of 368 positive comments and 181 negative sentiments. After going through the evaluation stage, the accuracy value is 95.92%, the precision value is 95.76%, and the recall value is 97.41%. The evaluation results show that the Naïve Bayes algorithm is proven to have a high level of accuracy used in classification.]]>
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