Public Sentiment Toward Rupiah Redenomination on Social Media X - Agung Febrian; Dody Herdiana; M. Agreindra Helmiawan (JASMINE: Journal of Intelligent Systems and Machine Learning, 2026)
This study examines public sentiment toward the proposed Indonesian Rupiah redenomination policy using data collected from Social Media X. The research applies a structured computational sentiment analysis pipeline, beginning with automatic sentiment labeling using a transformer-based language model, followed by classification using a Support Vector Machine with Term Frequency–Inverse Document Frequency feature representation. The dataset was collected over a one-day period from 7 November 2025 to 8 November 2025 to capture immediate public reactions to the policy discourse. Experimental results show that the classification model achieved an accuracy of 0.68 with balanced classification performance. Rather than aiming to optimize predictive accuracy, this study focuses on identifying general sentiment tendencies and patterns of public opinion regarding currency redenomination. The findings indicate that negative sentiment dominates the discourse, reflecting public concern and hesitation toward the policy, while a substantial proportion of neutral sentiment suggests ongoing evaluation and uncertainty among users. These results highlight the complexity of public responses to monetary policy communication and demonstrate the potential of social media analysis to provide an indicative overview of public sentiment in the digital public sphere. The study also acknowledges limitations related to automatic labeling and the inherent ambiguity of social media language, emphasizing that the findings should be interpreted as exploratory insights rather than definitive conclusions.