Public Sentiment Analysis on Demonstration Actions Using IndoBERT Based on Transfer Learning

Abstract

Sentiment analysis based on language modeling plays a crucial role in mapping public perception of socio-political dynamics in Indonesia. This study aims to evaluate public sentiment toward the House of Representatives of the Republic of Indonesia (DPR RI) in response to the August 2025 demonstrations using the IndoBERT model based on transfer learning. The dataset comprises 1,815 Indonesian-language opinion texts classified into positive and negative sentiments. Due to a substantial class imbalance dominated by negative opinions, a hybrid sampling strategy combining oversampling and undersampling was employed to obtain a balanced dataset of 650 samples per class. The research methodology included text preprocessing, an 80:20 training–testing split, and fine-tuning the IndoBERT-base-p1 model. Experimental results indicate that the proposed model achieves robust and balanced performance, with an overall accuracy of 85%. Precision and F1-score for both sentiment classes reached 0.85, while recall values were 0.86 for negative sentiment and 0.85 for positive sentiment, demonstrating the model’s ability to identify both classes effectively without bias toward the majority class. Despite the dominance of negative sentiment in the original dataset, the application of data balancing techniques successfully mitigated class imbalance effects, enabling fair and proportional sentiment classification. These findings confirm that the IndoBERT-based transfer learning approach is effective in capturing public sentiment related to mass demonstrations and can provide valuable, data-driven insights for policymakers in understanding societal concerns in the digital era.