FINE-GRAINED SENTIMENT ANALYSIS ON BIG DATA FROM MULTI-PLATFORM IN INDONESIA

Sentiment analysis on multi-platform big data in Indonesia presents a complex challenge, particularly in optimizing sentiment classification with higher granularity levels. This study aims to develop and optimize a sentiment classification model for analyzing public opinion on ChatGPT using a Fine-Grained Sentiment Analysis approach based on Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT). The method is applied to big data collected from various social media platforms to improve accuracy and precision in identifying a broader spectrum of sentiments, including highly positive, positive, neutral, negative, and highly negative categories. A comparative analysis was conducted on different base models, including BERT, RoBERTa, and IndoBERT, to determine the most effective model. Experimental results show that the optimized IndoBERT model achieves an accuracy of 96% and outperforms other models in terms of precision and F1-score across all sentiment categories. Additionally, this study evaluates the model's computational efficiency and adaptability to diverse data. Thus, the developed model can serve as a more effective solution for gaining deeper insights into public opinion across various digital platforms in Indonesia.