Empowering Low-Resource Languages: Javanese Machine Translation

Authors

DOI:

https://doi.org/10.29207/resti.v9i5.6887

Keywords:

javanese translation, NMT, linguistic heritage, low-resource language

Abstract

This study addresses the critical need to preserve and revitalize the Javanese language, which despite its widespread popularity, faces challenges as a low-resource language in Indonesia. The decline in Javanese proficiency among younger generations poses a significant threat to the language's cultural significance and heritage. To address this issue, this study introduces an innovative approach to machine translation, focusing on the development of a robust Indonesian-Javanese translation system. Utilizing advanced neural machine translation (NMT) techniques, including Long Short-Term Memory (LSTM) networks, the proposed system aims to bridge the linguistic gap between Indonesian and Javanese. Special attention was given to the unique linguistic characteristics and challenges of Javanese, with the goal of achieving exceptional translation accuracy and fluency. Through extensive experimentation and evaluation, this study aims to demonstrate the effectiveness of the translation system in facilitating cross-cultural communication and language preservation efforts within the Javanese-speaking community. By emphasizing the significance of Javanese as a widely spoken yet under-resourced language, this study underscores the importance of innovative technological solutions in safeguarding linguistic diversity and cultural heritage. Through its contributions, the research seeks to address the pressing need for language preservation and revitalization, particularly in the context of low-resource languages like Javanese.

Downloads

Download data is not yet available.

References

B. Klimova, M. Pikhart, A. D. Benites, C. Lehr, and C. Sanchez-Stockhammer, ‘Neural machine translation in foreign language teaching and learning: a systematic review’, Education and Information Technologies, vol. 28, no. 1, pp. 663–682, 2023.

S. Ranathunga, E.-S. A. Lee, M. Prifti Skenduli, R. Shekhar, M. Alam, and R. Kaur, ‘Neural Machine Translation for Low-resource Languages: A Survey’, ACM Comput. Surv., vol. 55, no. 11, pp. 1–37, Nov. 2023, doi: 10.1145/3567592.

D. A. Sulistyo, D. D. Prasetya, F. A. Ahda, and A. P. Wibawa, ‘Pivoted Low Resource Multilingual Translation with NER Optimization’, ACM Trans. Asian Low-Resour. Lang. Inf. Process., vol. 24, no. 5, pp. 1–16, May 2025, doi: 10.1145/3727876.

D. A. Sulistyo, A. P. Wibawa, D. D. Prasetya, and F. A. Ahda, ‘An enhanced pivot-based neural machine translation for low-resource languages’, International Journal of Advances in Intelligent Informatics, vol. 11, no. 2, 2025, Accessed: June 30, 2025. [Online]. Available: http://www.ijain.org/index.php/IJAIN/article/view/2115

D. A. Sulistyo, A. P. Wibawa, D. D. Prasetya, and F. A. Ahda, ‘Indonesian cross-linguistic named entity recognition’, Research Methods in Applied Linguistics, vol. 4, no. 3, p. 100236, Dec. 2025, doi: 10.1016/j.rmal.2025.100236.

D. A. Sulistyo, A. P. Wibawa, D. D. Prasetya, and A. Nafalski, ‘Autogenerated Indonesian-Madurese Parallel Corpus Dataset Using Neural Machine Translation’, Available at SSRN 4644430, Accessed: Mar. 20, 2024. [Online]. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4644430

R. Bawden, B. Zhang, L. Yankovskaya, A. Tättar, and M. Post, ‘A Study in Improving BLEU Reference Coverage with Diverse Automatic Paraphrasing’, Oct. 08, 2020, arXiv: arXiv:2004.14989. Accessed: Mar. 07, 2024. [Online]. Available: http://arxiv.org/abs/2004.14989

M. Evtikhiev, E. Bogomolov, Y. Sokolov, and T. Bryksin, ‘Out of the bleu: how should we assess quality of the code generation models?’, Journal of Systems and Software, vol. 203, p. 111741, 2023.

W. A. Farias, D. M. Melo, L. M. dos Santos, Â. A. de Oliveira, R. L. Medeiros, and Y. K. Silva, ‘Web Scraping as a scientific tool for theoretical reference’, 2024, Accessed: Apr. 03, 2024. [Online]. Available: https://www.researchsquare.com/article/rs-3854342/latest

A. Abodayeh, R. Hejazi, W. Najjar, L. Shihadeh, and R. Latif, ‘Web Scraping for Data Analytics: A BeautifulSoup Implementation’, in 2023 Sixth International Conference of Women in Data Science at Prince Sultan University (WiDS PSU), IEEE, 2023, pp. 65–69. Accessed: Apr. 03, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10145369/

C. Biswas, R. Mallick, S. Paul, and D. Mukherjee, ‘Solution to web scraping’, in 2023 11th International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON), IEEE, 2023, pp. 1–5. Accessed: Apr. 03, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10092327/

A. N. Handayani, H. Herwanto, K. L. Chandrika, and K. Arai, ‘Recognition of Handwritten Javanese Script using Backpropagation with Zoning Feature Extraction’, Knowledge Engineering and Data Science, 2021, doi: 10.17977/um018v4i22021p117-127.

Y. Dai, ‘Exploring Syntactic Representations in Pre-trained Transformers to Improve Neural Machine Translation by a Fusion of Neural Network Architectures’, PhD Thesis, University of Leeds, 2023. Accessed: Apr. 03, 2024. [Online]. Available: https://etheses.whiterose.ac.uk/33962/

U. Farooq, M. S. M. Rahim, N. S. Khan, S. Rasheed, and A. Abid, ‘A crowdsourcing-based framework for the development and validation of machine readable parallel corpus for sign languages’, IEEE Access, vol. 9, pp. 91788–91806, 2021.

S. K. Mondal et al., ‘Enhancement of English-Bengali Machine Translation Leveraging Back-Translation’, Applied Sciences, vol. 14, no. 15, p. 6848, 2024.

G. Rafiq, M. Rafiq, and G. S. Choi, ‘Video description: A comprehensive survey of deep learning approaches’, Artif Intell Rev, vol. 56, no. 11, pp. 13293–13372, Nov. 2023, doi: 10.1007/s10462-023-10414-6.

S. Das, A. Tariq, T. Santos, S. S. Kantareddy, and I. Banerjee, ‘Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research’, in Machine Learning for Brain Disorders, vol. 197, O. Colliot, Ed., in Neuromethods, vol. 197. , New York, NY: Springer US, 2023, pp. 117–138. doi: 10.1007/978-1-0716-3195-9_4.

A. Baliyan, A. Batra, and S. P. Singh, ‘Multilingual Sentiment Analysis using RNN-LSTM and Neural Machine Translation’, in 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom), Mar. 2021, pp. 710–713.

S. Lee et al., ‘A survey on evaluation metrics for machine translation’, Mathematics, vol. 11, no. 4, p. 1006, 2023.

S. Yang, D. Chen, S. Li, and W. Wang, ‘Carbon price forecasting based on modified ensemble empirical mode decomposition and long short-term memory optimized by improved whale optimization algorithm’, Science of the Total Environment, vol. 716, p. 137117, 2020.

D. Bowman et al., ‘The Mathematical Foundations of Epistemic Network Analysis’, in Advances in Quantitative Ethnography, vol. 1312, A. R. Ruis and S. B. Lee, Eds, in Communications in Computer and Information Science, vol. 1312. , Cham: Springer International Publishing, 2021, pp. 91–105. doi: 10.1007/978-3-030-67788-6_7.

B. Li et al., ‘ODE Transformer: An Ordinary Differential Equation-Inspired Model for Neural Machine Translation’, Apr. 06, 2021, arXiv: arXiv:2104.02308. Accessed: Apr. 03, 2024. [Online]. Available: http://arxiv.org/abs/2104.02308

T. Liu, Q. Fang, W. Ding, H. Li, Z. Wu, and Z. Liu, ‘Mathematical word problem generation from commonsense knowledge graph and equations’, in Proceedings of the 2021 conference on empirical methods in natural language processing, 2021, pp. 4225–4240. Accessed: Apr. 03, 2024. [Online]. Available: https://aclanthology.org/2021.emnlp-main.348/

P. V. Abhiram et al., ‘Enhanced Auto Language Prediction with Dictionary Capsule -- A Novel Approach’, Mar. 09, 2024, arXiv: arXiv:2403.05982. Accessed: Apr. 04, 2024. [Online]. Available: http://arxiv.org/abs/2403.05982

P. Garg, J. Villasenor, and V. Foggo, ‘Fairness metrics: A comparative analysis’, in 2020 IEEE international conference on big data (Big Data), IEEE, 2020, pp. 3662–3666. Accessed: Apr. 03, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9378025/

A. Wang and K. Cho, ‘BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model’, Apr. 09, 2019, arXiv: arXiv:1902.04094. Accessed: Apr. 03, 2024. [Online]. Available: http://arxiv.org/abs/1902.04094

K. Kaur and S. Chauhan, ‘Original Research Article A comparative analysis of lexical-based automatic evaluation metrics for different Indic language pairs’, Journal of Autonomous Intelligence, vol. 7, no. 4, 2024, Accessed: Apr. 03, 2024. [Online]. Available: http://jai.front-sci.com/index.php/jai/article/view/1393

C. P. Chai, ‘Comparison of text preprocessing methods’, Natural Language Engineering, vol. 29, no. 3, pp. 509–553, 2023.

S. V. Mashtalir and O. V. Nikolenko, ‘Data preprocessing and tokenization techniques for technical Ukrainian texts’, Applied Aspects of Information Technology, vol. 3, no. 6, pp. 318–326, 2023.

I. Gupta, ‘Punctuation Restoration as Post-processing Step for Swedish Language Automatic Speech Recognition’. 2023. Accessed: Apr. 03, 2024. [Online]. Available: https://www.diva-portal.org/smash/record.jsf?pid=diva2:1807710

A. Kathuria, A. Gupta, and R. K. Singla, ‘A Review of Tools and Techniques for Preprocessing of Textual Data’, in Computational Methods and Data Engineering, vol. 1227, V. Singh, V. K. Asari, S. Kumar, and R. B. Patel, Eds, in Advances in Intelligent Systems and Computing, vol. 1227. , Singapore: Springer Singapore, 2021, pp. 407–422. doi: 10.1007/978-981-15-6876-3_31.

U. Naseem, I. Razzak, and P. W. Eklund, ‘A survey of pre-processing techniques to improve short-text quality: a case study on hate speech detection on twitter’, Multimedia Tools and Applications, vol. 80, pp. 35239–35266, 2021.

L. Huang, J. Qin, Y. Zhou, F. Zhu, L. Liu, and L. Shao, ‘Normalization techniques in training dnns: Methodology, analysis and application’, IEEE transactions on pattern analysis and machine intelligence, vol. 45, no. 8, pp. 10173–10196, 2023.

S. Supriyono, A. P. Wibawa, S. Suyono, and F. Kurniawan, ‘Analyzing Audience Sentiments in Digital Comedy: A Study of YouTube Comments Using LSTM Models’, Journal of Applied Data Sciences, vol. 5, no. 4, pp. 1877–1889, 2024.

C. Toraman, E. H. Yilmaz, F. Şahi̇nuç, and O. Ozcelik, ‘Impact of Tokenization on Language Models: An Analysis for Turkish’, ACM Trans. Asian Low-Resour. Lang. Inf. Process., vol. 22, no. 4, pp. 1–21, Apr. 2023, doi: 10.1145/3578707.

F. A. Ahda, A. P. Wibawa, D. D. Prasetya, and D. A. Sulistyo, ‘Comparison of Adam Optimization and RMS prop in Minangkabau-Indonesian Bidirectional Translation with Neural Machine Translation’, JOIV: International Journal on Informatics Visualization, vol. 8, no. 1, pp. 231–238, 2024.

T. L. M. Suryanto, A. P. Wibawa, H. Hariyono, and A. Nafalski, ‘Comparative performance of transformer models for cultural heritage in NLP tasks’, Advance Sustainable Science Engineering and Technology, vol. 7, no. 1, pp. 02501015–02501015, 2025.

A. F. Del Carpio and L. B. Angarita, ‘Trends in software engineering processes using deep learning: a systematic literature review’, in 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), IEEE, 2020, pp. 445–454. Accessed: Apr. 03, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9226311/

A. R. Lubis, Y. Y. Lase, D. A. Rahman, and D. Witarsyah, ‘Improving Spell Checker Performance for Bahasa Indonesia Using Text Preprocessing Techniques with Deep Learning Models.’, Ingénierie des Systèmes d’Information, vol. 28, no. 5, 2023, Accessed: Apr. 03, 2024. https://doi.org/10.18280/isi.280522

K. Dedes, A. B. P. Utama, A. P. Wibawa, A. Afandi, A. N. Handayani, and L. Hernandez, ‘Neural Machine Translation of Spanish-English Food Recipes Using LSTM’, undefined, 2022, Accessed: Sept. 12, 2022. [Online]. Available: https://www.semanticscholar.org/paper/Neural-Machine-Translation-of-Spanish-English-Food-Dedes-Utama/eafa6d3f83ec945b30ad4ea75925e2799a75eaf3

D. A. Sulistyo, A. P. Wibawa, D. D. Prasetya, and F. A. Ahda, ‘LSTM-Based Machine Translation for Madurese-Indonesian’, Journal of Applied Data Sciences, vol. 4, no. 3, pp. 189–199, 2023.

Downloads

Published

2025-10-26

Issue

Section

Computer Science Applications

How to Cite

Sulistyo, D. A., Aji Prasetya Wibawa, Wayan Firdaus Mahmudy, Fadhli Almu’iini Ahda, & Andrew Nafalski. (2025). Empowering Low-Resource Languages: Javanese Machine Translation. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 9(5), 1210-1221. https://doi.org/10.29207/resti.v9i5.6887