<![CDATA[The rapid advancement of artificial intelligence has created significant opportunities to enhance tourism services, a vital sector of Indonesia’s economy, particularly in Raja Ampat as a leading ecotourism destination. (R1-1 Background) A persistent challenge in this region is effective communication for homestay management, where limited human resources and linguistic diversity constrain service quality. (R1-1 Methodology) This study evaluates a multilingual voice chatbot integrating Natural Language Processing, Large Language Models, and a Retrieval-Augmented Generation architecture, supporting Indonesian, English, and a virtual local language. System performance is quantitatively assessed using Speech-to-Text accuracy measured by Word Error Rate, intent classification metrics, semantic retrieval effectiveness, and end-to-end evaluation. The proposed pipeline includes speech data collection, text normalization, multilingual embedding, vector storage, semantic retrieval, and response generation. (R1-2 Results) Results show that STT quality strongly determines downstream performance. Indonesian achieves the lowest WER (0.14) and the highest intent F1-score (0.89), while the virtual language records the highest WER (0.25) and the lowest intent F1-score (0.65). The semantic retriever attains a Mean Average Precision of 0.55, indicating moderate document ranking quality. The integrated end-to-end system achieves an F1-score of 0.857 with a user satisfaction score of 4.4. (A-1 Contribution) Compared with existing tourism chatbots, the proposed system uniquely combines multilingual voice interaction with RAG-based grounding to improve response reliability in low-resource settings. (A-2 Conclusion and applicability) These findings demonstrate practical effectiveness for homestay services and highlight scalability to other multilingual tourism regions in Indonesia and beyond.]]>
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