Tourism-Dermayon: A Basic Model for Development of Basa Dermayu Language Model

Denny Firmansyah Suwardi, Mohammad Yani (), Muhamad Mustamiin, Robieth Sohiburoyyan and Vera Wati
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Denny Firmansyah Suwardi: Politeknik Negeri Indramayu, Departemen Teknik Informatika
Mohammad Yani: Politeknik Negeri Indramayu, Departemen Teknik Informatika
Muhamad Mustamiin: Politeknik Negeri Indramayu, Departemen Teknik Informatika
Robieth Sohiburoyyan: Politeknik Negeri Indramayu, Departemen Teknik Informatika
Vera Wati: Politeknik Negeri Indramayu, Departemen Teknik Informatika

A chapter in Proceedings of the International Conference on Applied Science and Technology on Social Science 2025 (iCAST-SS 2025), 2025, pp 577-585 from Springer

Abstract: Abstract Indramayu Language or Basa Dermayu is a Javanese language spoken by the people of Indramayu. The dialect differences between Basa Dermayu and Standard Javanese pose a challenge in developing a large language model (LLM) for Basa Dermayu, which is currently not available. This study aims to develop an LLM for Basa Dermayu to support tourism and cultural needs in Indramayu by implementing it in a chatbot application. The model was built using a pre-trained LLM that was fine-tuned with a dataset concerning tourism and culture in Indramayu. The selection of tourism and cultural topics for the dataset was based on prior research related to the promotion of tourism and culture in Indramayu. The dataset was created using artificial intelligence (AI) tools and then manually annotated by native Basa Dermayu speakers. Experimental results indicate that, although not yet optimal, achieving an average ROUGE score of 26.4%, the results are reasonably favorable when compared with the baseline model prior to fine-tuning. The contribution of this study lies in the development of a pre-trained LLM for Basa Dermayu, which can serve as a baseline for future development of Basa Dermayu LLMs.

Keywords: Fine-tuning; Low-resource language; Synthetic data generation; Tourism Chatbot; Tourism Indramayu (search for similar items in EconPapers)
Date: 2025
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DOI: 10.2991/978-94-6463-938-4_65

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