<![CDATA[Natural disasters have a significant impact on public health, giving rise to various post-disaster illnesses. This study presents an automated information‑extraction framework based on Named Entity Recognition (NER), leveraging the IndoBERT model to identify disaster types, health impacts, and affected locations from online news reports. Data were gathered via web scraping from multiple reputable news portals and subsequently processed through tokenization, stop‑word removal, and lemmatization. Extracted entities were visualized via bar charts and word clouds to reveal disease patterns associated with each disaster type. Results indicate that floods have a significant public health impact, with skin diseases being the most prevalent, followed by diarrhea, fever, influenza, and Acute Respiratory Infections (ARIs). Volcanic eruptions are linked to health conditions such as ARI, hypertension, diarrhea, and influenza, whereas earthquakes show strong correlations with diarrhea, ARI, skin diseases, and fever. Droughts and landslides are closely associated with diarrheal outbreaks due to compromised sanitation resulting from limited access to clean water. Although less frequently reported, tsunamis also exhibit a notable association with cases of diarrhea. The proposed method achieves 90 % accuracy and an 88 % F1‑score. These findings confirm the effectiveness of our NER-based approach in detecting causal relationships between disasters and health outcomes, providing valuable insights for policymakers and healthcare professionals in designing targeted post-disaster mitigation and response strategies.]]>
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