<![CDATA[Public access to local government data in Indonesia, such as that in the Satu Data Indonesia portal for Banyumas Regency, is severely hampered by outdated search interfaces and the technical complexity of handling heterogeneous data formats like PDF, Excel, and CSV. This research directly addresses this accessibility gap by designing, developing, and evaluating an intelligent question-answering system. We introduce a novel application of a Retrieval- Augmented Generation (RAG) architecture tailored for Indonesian local government data. The core novelty lies in our methodology for handling heterogeneous data formats (PDF, Excel, CSV) by integrating a low-code orchestrator (n8n) with a high-performance vector database (pgvector), a practical solution for a common public sector challenge. The system utilizes the text-embedding-3-large model for semantic understanding and gpt-4.1 for generating grounded, factual answers. The system's effectiveness was rigorously validated, achieving a perfect 100% score across accuracy, precision, recall, and F1-score on defined test cases. Crucially, usability testing with end-users confirmed the system is perceived as significantly more efficient and user-friendly than manual data searching. The primary impact of this work is a validated, replicable blueprint for local governments to democratize public information. By transforming complex data retrieval into an intuitive conversation, this research offers a practical AI solution to enhance governmental transparency and citizen engagement.]]>
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