Comparative Analysis of IndoBERT and BiLSTM For Public Sentiment Classification Toward The Indonesian National Police on Youtube

Hardeva Satria Hazz (Institut Sains dan Bisnis Atma Luhur, Pangkalpinang, Indonesia)
Delpiah Wahyuningsih (Institut Sains dan Bisnis Atma Luhur, Pangkalpinang, Indonesia)

Journal of Deep Learning, Computer Vision and Digital Image Processing
Volume 4 Issue 2 June 2026

Purpose – This study aimed to compare the performance of IndoBERT and Bidirectional Long Short-Term Memory (BiLSTM) in classifying public sentiment toward the Indonesian National Police (INP) based on YouTube comments. This study sought to identify a robust sentiment classification model to support text-based public perception monitoring, particularly under a highly imbalanced sentiment distribution.Method – YouTube comments were collected using the YouTube Data API. A total of 8,268 raw comments were obtained, and 7,197 comments were retained as the final dataset after preprocessing, automatic pseudo-labeling, and confidence filtering using a 0.5 threshold. To address concerns regarding threshold selection, an additional sensitivity analysis was conducted using confidence thresholds of 0.65 and 0.75. The experiment applied a dual-track preprocessing pipeline, cost-sensitive learning through class-weighted loss, bootstrap confidence interval analysis, and BiLSTM preprocessing ablation.Findings – The results show that IndoBERT achieved stronger performance than BiLSTM. IndoBERT obtained an accuracy of 92.92% and a Macro-F1 Score of 0.8548, whereas BiLSTM achieved an accuracy of 76.11% and a Macro-F1 Score of 0.6124. Bootstrap analysis showed a Macro-F1 difference of 0.2424, with a 95% confidence interval of 0.1870 to 0.2959, indicating that IndoBERT’s advantage was statistically significant. Sensitivity analysis also confirmed that IndoBERT consistently outperformed BiLSTM across all the tested thresholds.Research Implications – The findings indicate that IndoBERT is more suitable for Indonesian sentiment classification in public perception monitoring than other models. However, because the dataset labels were generated using a BERT-based classifier, the evaluation may contain architectural circularity that favors the IndoBERT model. Future studies should use human-annotated gold-standard data and broader cross-platform validations.Originality – This study provides a comparative evaluation of transformer-based and recurrent models using sensitivity analysis, bootstrap testing, cost-sensitive learning, and pre-processing ablation under imbalanced sentiment conditions.