Analisis Sentimen Berbasis Aspek pada Opini Publik Pascabencana Hidrometeorologi Menggunakan Model Pre-Trained IndoBERT
DOI:
https://doi.org/10.47065/bulletincsr.v6i5.1321Keywords:
Aspect-Based Sentiment Analysis; IndoBERT; Public Opinion; Hydrometeorological Disasters; AcehAbstract
Aceh Province is highly vulnerable to disasters, particularly hydrometeorological events like floods and landslides. Aceh Disaster Management Agency (BPBA) data shows 418 disasters occurred in 2023 with total losses reaching Rp430 billion; similarly, in 2024, most incidents were hydrometeorological disasters affecting tens of thousands of families. This high frequency triggered a social media reaction surge, producing data volumes unanalyzable via conventional sentiment methods. This study applies Aspect-Based Sentiment Analysis (ABSA) and the pre-trained IndoBERT model (indobenchmark/indobert-base-p1) to analyze public opinion across five disaster management aspects: logistics and assistance, evacuation and shelters, government coordination, infrastructure and reconstruction, and healthcare. A total of 1,000 opinions were gathered from X (Twitter) and YouTube (November 2025–April 2026), pre-processed via indoNLP, yielding 3,319 labeled opinion-aspect pairs. The model used sentence-pair classification, fine-tuned with CrossEntropy Loss (learning rate 2e-5, batch size 16, 3 epochs), evaluated via accuracy, precision, recall, and F1-score (target >85%). Testing shows IndoBERT achieved 91.26% accuracy and an 86.03% F1-score, exceeding targets. Sentiment distribution reveals healthcare triggered the most positive sentiment (89.82%), reflecting public appreciation for medical teams. Conversely, negative sentiment dominated evacuation and shelters (34.45%), indicating public dissatisfaction with evacuation procedures and shelter availability post-disaster in Aceh.
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