<![CDATA[Kabupaten Bogor is among the highest disaster-risk regencies in West Java, having recorded 1,826 disaster events throughout 2025, while official mitigation guidelines remain confined to static PDF documents that are difficult to consult during emergencies. To address this gap, this study implements a disaster mitigation chatbot built on Graph Retrieval-Augmented Generation (GraphRAG), pairing Neo4j Desktop as a local graph database with Gemma3:4b (served through Ollama) as the language model, orchestrated via LangChain and exposed through a Flask web interface. The knowledge graph was built from 11 source documents provided by BPBD Kabupaten Bogor—Disaster Preparedness Pocket Books together with supplementary disaster-information documents—spanning 8 disaster types, yielding 483 nodes and 754 relationships. Each entity node carries three properties (name, description, and disaster phase) to enable more precise retrieval. System performance was assessed with the RAGAS v0.4.3 framework over a 99-question dataset over three trials, yielding average Faithfulness, Answer Relevancy, and Context Precision scores of 0.8764, 0.6773, and 0.8216, respectively. These results indicate promising contextual grounding and retrieval performance, while the lower Answer Relevancy score highlights the need for further improvement in aligning generated answers with user questions. The findings demonstrate the technical feasibility of a locally hosted GraphRAG prototype for retrieving disaster-mitigation information from BPBD Kabupaten Bogor documents. However, the evaluation does not establish operational readiness for emergency use, as comparison with conventional RAG, expert assessment, and validation with intended users remain necessary.]]>
