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Arsi, Primandani
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Comparative Performance of Retrieval Augmented Generation Tourism Chatbots: Kinerja Komparatif Retrieval Augmented Generation pada Chatbot Pariwisata Farizi, Amar Al; Arsi, Primandani; Subarkah, Pungkas
Indonesian Journal of Innovation Studies Vol. 27 No. 1 (2026): January
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v27i1.1836

Abstract

General Background: The rapid adoption of artificial intelligence in smart tourism has increased the use of contextual chatbots to deliver destination information efficiently. Specific Background: However, tourism chatbots based on Large Language Models frequently encounter information hallucination, reducing reliability when handling dynamic and local tourism data. Knowledge Gap: Existing studies mainly focus on rule-based or single-model chatbot implementations and provide limited comparative evaluation of Retrieval Augmented Generation configurations combining embedding models and Large Language Models. Aims: This study aims to comparatively evaluate multiple Retrieval Augmented Generation configurations to identify the most suitable combination for contextual tourism chatbots and to analyze differences between large multilingual and small monolingual embedding models using a local tourism dataset. Results: Experimental evaluation using data from 49 tourist destinations in Banyumas Regency shows that the Multilingual-E5-Large embedding model consistently achieves perfect Precision, Recall, and F1-Score across all tested Large Language Models. The combination of Multilingual-E5-Large and GPT-4.1-Mini demonstrates the most balanced performance, achieving a BERTScore F1 of 0.7515 with an average response time of 1.555 seconds. Novelty: This research provides a systematic comparative assessment of embedding capacity and Large Language Model selection within a unified Retrieval Augmented Generation framework for tourism chatbots. Implications: The findings offer practical guidance for selecting model configurations that ensure accurate retrieval, high-quality responses, and efficient system performance in contextual tourism information services. Highlights • Multilingual embedding models deliver consistently higher retrieval accuracy across all tested configurations• GPT-4.1-Mini produces the most balanced generative quality and response latency• Embedding model selection plays a more decisive role than language model variation Keywords Retrieval Augmented Generation; Tourism Chatbot; Large Language Model; Embedding Model; Comparative Evaluation
Co-Authors Adam Prayogo Kuncoro Aditya Permana, Reza Agung Prasetyo Astrida, Deuis Nur Asyari, Muhammad Hasyim Azzahra, Delia Oktaviana Bimawan, Zaynuri Ilham Busyro, Muhammad Dedi Purwanto, Dedi Dhanar Intan Surya Saputra Farizi, Amar Al Firmanda, Reza Arief Hendra Marcos Isnaini, Khairunnisak Nur Junianto, Haris Khofiyah, Salma Ngarifatul Kholifah Dwi Prasetyo Kartika, Nur Kisma, Atmaja Jalu Narendra Kusuma, Bagus Adhi Lestari, Vika Febri Nandang Hermanto Nasar Ghanim, Nadif Nur Ikhsan, Ali Prasetyo Kartika, Nur Kholifah Dwi Prayoga, Iphang Riandini, Dini Rofiqoh, Dayana Salsabiela, Ayuni Sandhi Bhakti, Dwi Saputri, Inka Sari, Rida Purnama Septiana Putri, Refida Subarkah, Pungkas Sugiarti Sugiarti Suhaman, Jali Tarwoto, Tarwoto Tri Astuti V, Jay Waluyo, Retno