<![CDATA[This study investigates public sentiment toward cryptocurrency regulation policies in Indonesia by employing a logistic regression approach on social media data. A total of 300 Indonesian-language tweets were collected from platform X between January 2022 and April 2025 through a web scraping method using targeted keywords related to cryptocurrency payment regulations. Data preprocessing included text cleaning, case folding, stemming with the Sastrawi library, stopword removal, and tokenization, followed by feature extraction using TF-IDF. Sentiment labels were manually assigned in collaboration with legal experts to ensure classification accuracy. The logistic regression model achieved strong predictive performance, with 91.67% accuracy on the test set and stable results across K-Fold Cross Validation, yielding an average accuracy of 92–93%. The sentiment analysis revealed that the majority of public opinion expressed positive sentiment (85%), while negative sentiment represented only 15%. Positive sentiment was primarily associated with terms such as “protect,” “regulate,” “benefit,” and “legality,” highlighting public support for regulatory measures that enhance investor protection and provide legal certainty. Conversely, negative sentiment featured terms including “forbidden,” “restrict,” and “obstruct,” which reflected concerns regarding regulatory barriers and religious considerations surrounding cryptocurrency usage. The findings demonstrate that Indonesian society generally perceives cryptocurrency regulation as a constructive initiative toward building a secure and trustworthy digital asset ecosystem. Furthermore, the empirical evidence contributes to the growing literature on public perception of financial technology regulations in developing countries. For policymakers, the results emphasize the importance of transparent communication and balanced regulatory frameworks to maintain public trust while addressing potential risks. Overall, this research provides valuable insights into how sentiment analysis can inform the design of more effective regulatory strategies in the evolving landscape of digital finance.]]>
