Implementasi Clean Architecture pada Chatbot Cuaca Hiper-Lokal Berbasis Hybrid NLP di Wilayah Bali - Md Wira Putra Dananjaya (TEMATIK, 2026)
Accurate meteorological information is crucial for the tourism and agriculture sectors in Bali Province. However, global weather Application Programming Interfaces (APIs) often have limitations in recognizing hyper-local areas and lack natural language interaction capabilities. This study aims to develop the Bali Weather Bot, an intelligent Telegram-based assistant using Clean Architecture and Hybrid Natural Language Processing (NLP). The methodology combines the Gemini 1.5 Flash Large Language Model (LLM) for entity extraction with a Context-Aware Dictionary Mapping fallback mechanism to standardize local abbreviations and Indonesian temporal metaphors. Evaluation was conducted using a dataset of 250 test query scenarios evaluated through a confusion matrix and latency benchmarking. System evaluation demonstrates that this hybrid architecture successfully extracts spatial and temporal parameters with an overall accuracy of 82.80%. Quantitative evaluation shows a Precision of 92.00%, Recall of 89.22%, and an F1-score of 90.58%, while maintaining a 0% system crash rate. In conclusion, the hybrid approach effectively mitigates AI hallucination and enhances hyper-local data retrieval reliability in conversational interfaces.