Sentiment Analysis of Public Opinion on Rupiah Redenomination on Twitter Using Naive Bayes Classification

Abstract

This study examines public opinion on the Rupiah redenomination policy through sentiment analysis of Twitter data. Redenomination refers to the simplification of currency denominations without changing their real value, a policy that often triggers varied public responses due to concerns such as inflation perception and money illusion. In the digital era, Twitter (currently X) serves as a major platform for real-time public expression, generating large volumes of unstructured textual data suitable for analysis. The objective of this research is to classify public sentiment toward the Rupiah redenomination policy into positive, negative, and neutral categories using the Naive Bayes Classifier, as well as to evaluate the model’s performance. The dataset consists of Indonesian-language tweets collected via the Twitter API using keywords related to redenomination. Data processing involves several stages, including data cleaning, manual labeling, text preprocessing (case folding, tokenization, stopword removal, and stemming), and feature extraction using Term Frequency–Inverse Document Frequency (TF–IDF). The classification results are evaluated using a confusion matrix. The Naive Bayes Classifier achieved an accuracy of approximately 74.84% and a precision of 80%, indicating that the model performs adequately in identifying sentiment patterns. The findings show that neutral sentiment dominates the discussion, suggesting that most users tend to provide informational or observational opinions rather than strong support or opposition. These results are expected to provide insights for policymakers, particularly Bank Indonesia and the government, regarding public acceptance of the redenomination policy, while also contributing to the development of sentiment analysis research on Indonesian social media data.