Integration of Artificial Intelligence in the Preservation of Regional Arts and Languages: A Digital Humanities Approach in Indonesia

Malathy Batumalay; Haryono; Masrin

doi:10.55681/armada.v3i9.1756

Authors

Malathy Batumalay Faculty of IT, INTI International University, 71800 Nilai, Negeri Sembilan, Malaysia
Haryono University of Bumigora, Mataram, Indonesia
Masrin Postgraduate Faculty of Languages, University of Indraprasta PGRI, Jakarta, Indonesia

DOI:

https://doi.org/10.55681/armada.v3i9.1756

Keywords:

Artificial intelligence, digital humanities, cultural preservation

Abstract

The development of artificial intelligence (AI) technology has opened up new opportunities for the preservation of cultural heritage, including endangered regional arts and languages. This study aims to analyze how the application of AI in the context of digital humanities can support efforts to document, revitalize, and disseminate local culture in Indonesia. The method used is descriptive-qualitative with a literature study approach and policy analysis of digital culture preservation projects that have been running, such as Google AI for Indigenous Languages and the Digital Culture program of the Ministry of Education, Culture, Research, and Technology. The results show that the integration of AI through Natural Language Processing (NLP), machine translation, speech recognition, and digital archiving is able to accelerate the process of documentation and learning of regional languages and encourage the participation of the creative community in the preservation of traditional arts. However, the main challenges lie in the limitations of linguistic data, algorithmic bias, and technological gaps between regions. This study concludes that the successful implementation of AI-based digital humanities depends on multidisciplinary collaboration between governments, academics, cultural communities, and technology developers.

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References

Baevski, A., Zhou, Y., Mohamed, A., & Auli, M. (2020). wav2vec 2.0: A framework for self-supervised learning of speech representations. In NeurIPS 2020 Proceedings. https://doi.org/10.5555/3495724.3496768

Bromham, L., Dinnage, R., Skirgård, H., Ritchie, A., Cardillo, M., Meakins, F., Greenhill, S., & Hua, X. (2022). Global predictors of language endangerment and the future of linguistic diversity. Nature Ecology & Evolution, 6(2), 163–173. https://doi.org/10.1038/s41559-021-01604-y

Casillo, M. (2025). Artificial intelligence in archaeological site conservation. Journal of Computer Applications in Archaeology. https://doi.org/10.5334/jcaa.207

Chang-E dataset authors. (2024). Chang-E: A high-quality motion capture dataset of Dunhuang dance. ACM. https://doi.org/10.1145/3709000

Foka, A. (2025). Tracing the bias loop: AI, cultural heritage and bias-mitigating in practice. AI & Society. https://doi.org/10.1007/s00146-025-02349-z

Foka, A., & Griffin, G. (2024). AI, cultural heritage, and bias: Some key queries that arise from the use of GenAI. Heritage, 7(11), 6125–6136. https://doi.org/10.3390/heritage7110287

Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020). Explainable AI: A review of machine learning interpretability methods. Entropy, 23(1), 18. https://doi.org/10.3390/e23010018

Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020). Explainable AI: A review of machine learning interpretability methods. Entropy. https://doi.org/10.3390/e23010018

Mohd Herrow, M. F., & Azraai, N. Z. (2021). Digital preservation of intangible cultural heritage of Joget dance movement using motion capture technology. International Journal of Heritage, Art and Multimedia, 4(15), 01–13. https://doi.org/10.35631/IJHAM.415001

Münster, S., Lehmann, C., Lazariv, T., Maiwald, F., Karsten, S., et al. (2024). Artificial intelligence for digital heritage innovation: Setting up a R&D agenda for Europe. Heritage, 7(2), 794–816. https://doi.org/10.3390/heritage7020038

Pagnin, L. (2023). Protecting street art from outdoor environmental threats: Review and perspectives. Coatings, 13(12), 2044. https://doi.org/10.3390/coatings13122044

Prem, E. (2024). Principles of digital humanism: A critical post-humanist view. Journal of Responsible Technology, 17(C), 100075. https://doi.org/10.1016/j.jrt.2024.100075

Romero, M., Gómez-Canaval, S., & Torre, I. G. (2024). Automatic speech recognition advancements for indigenous languages of the Americas. Applied Sciences, 14(15), 6497. https://doi.org/10.3390/app14156497

Vadineanu, S. (2024). Convolutional neural networks and their activations: An exploratory case study on mounded settlements. Journal of Computer Applications in Archaeology, 163. https://doi.org/10.5334/jcaa.163

Westenberger, P. (2025). Artificial intelligence for cultural heritage research: Copyright, policy and practice. European Journal of Cultural Management and Policy. https://doi.org/10.3389/ejcmp.2025.14009