Optimizing Indonesian-Sundanese Bilingual Translation with Adam-Based Neural Machine Translation

Optimizing Indonesian-Sundanese Bilingual Translation with Adam-Based Neural Machine Translation

Adam; BLEU; Indonesian; LSTM; Sundanese

This research seeks to construct an automatic translation between Indonesian and Sundanese languages based on the Neural Machine Translation (NMT) method. The model used in this study is the Long Short-Term Memory (LSTM) type, which carries out an encoder-decoder structure model learned with Bible data. The text translation here was conducted in different epochs to optimize the process, followed bythe Adam optimization algorithm. Testing the Adam optimizer with different epoch settings yields a BLEU score for Indonesian to Sundanese translations of 0.991785, higher than the performance of the None optimizer. Experimental results demonstrate that Indonesian to Sundanese translation using Adam optimization with 1000 epochs consistently performed better in BLEU -Bilingual Evaluation Understudy -scoring thanSundanese to Indonesian translation. Limitations of the research were also put forth, particularly technical issues related to the collection of data and the Sundanese language’s complex grammatical features, that the model can onlypartially express, honorifics, and the problem of polysemy. Also, it mustbe mentioned that no special hyperparameter selection was performed, as parameters were chosen randomly. In future studies, transformer-based models can be investigated since these architectures will better dealwith complex language via their self-attention mechanism.

Anita Qotrun Nada1*, Aji Prasetya Wibawa2, Dhea Fanny Putri Syarifa3, Erliana Fajarwati4,Fadia Irsania Putri

https://jurnal.iaii.or.id/index.php/RESTI/article/view/6116/989

Faculty of Engineering, Universitas Negeri Malang, Malang, Indonesia

24-12-2024

FAJAR BAGUS W

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