COMPARATIVE ANALYSIS OF PUBLIC SENTIMENT TOWARDS SRI MULYANI AND PURBAYA AS FINANCE MINISTERS ON THE X PLATFORM USING THE INDOBERTWEET MODEL.

Abstract

The development of social media has positioned platform X (Twitter) as a primary source for expressing public opinion toward government figures and policies. This study aims to analyze public sentiment toward two Indonesian public figures, Sri Mulyani Indrawati and Purbaya Yudhi Sadewa, by utilizing the transformer-based IndoBERTweet model. The data were collected from January 1, 2025, to November 1, 2025. A total of 11,000 tweets related to Sri Mulyani were collected; however, only 2,500 tweets were used for data processing and model training, with a maximum limit of 1,000 tweets per month. Meanwhile, 650 tweets were obtained for Purbaya Yudhi Sadewa. This research employs a supervised learning approach with labeled data consisting of positive, negative, and neutral sentiment classes. Minimal preprocessing was applied, considering that IndoBERTweet is specifically designed to handle the characteristics of social media text. The model was trained for five epochs and evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the IndoBERTweet model can classify sentiment effectively, particularly on the Sri Mulyani dataset, which contains a larger volume of data and achieves an accuracy of over 82%. In contrast, the model’s performance on the Purbaya Yudhi Sadewa dataset shows a lower accuracy of 71%, influenced by the limited amount of data. This study confirms that the quantity and distribution of data significantly affect the performance of transformer-based sentiment analysis models. Based on the sentiment classification results, public sentiment toward Sri Mulyani Indrawati tends to be dominated by negative and neutral sentiments, while sentiment toward Purbaya Yudhi Sadewa shows a distribution dominated by neutral and positive sentiments.