Classification of Sentiment in Reddit Forum Comments using Fine-Tuned IndoBERT (Case Study: Free Nutritious Meal Program) - Bram Aji Saka Putra; Suprianto (International Journal on Human-Computing Studies, 2026)
This study analyzes public opinion on the Free Nutritious Meals Program (MBG) policy on the Reddit platform using the IndoBERT model. The study uses a dataset of 6,295 Reddit comments collected from 2024 to 2025. The preprocessing stage implemented a comprehensive suite of textual refinement procedures, encompassing normalization of colloquial language, emoji conversion, and translation into Indonesian to establish linguistic consistency across the corpus. Manual annotation was performed with meticulous care on a balanced dataset of 3,000 comments, evenly allocated among positive, negative, and neutral classes. An 80:20 train–test split was applied to enhance the reliability of model training and evaluation. A fine‑tuned IndoBERT model exhibited outstanding performance, attaining 99.00% for accuracy, F1‑score, and precision. Applied to the full dataset, the model predicted neutral sentiment as predominant (59.44%), followed by positive (34.11%) and negative (6.45%) sentiments, a distribution that suggests a measured and reflective public discourse on the topic. Model reliability was further supported by a mean confidence score of 0.8502 and a processing throughput of 137.93 samples per second, indicating strong potential for deployment as an effective tool for near real‑time sentiment monitoring in public policy contexts.