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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ILKOM Jurnal Ilmiah
Putri, Fadia Irsania
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Computer Science & IT Engineering

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Refining the Performance of Indonesian-Javanese Bilingual Neural Machine Translation Using Adam Optimizer Putri, Fadia Irsania; Wibawa, Aji Prasetya; Collante, Leonel Hernandez
ILKOM Jurnal Ilmiah Vol 16, No 3 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i3.2467.271-282

Abstract

This study focuses on creating a Neural Machine Translation (NMT) model for Indonesian and Javanese languages using Long Short-Term Memory (LSTM) architecture. The dataset was sourced from online platforms, containing pairs of parallel sentences in both languages. Training was performed with the Adam optimizer, and its effectiveness was compared to machine translation (MT) conducted without an optimizer. The Adam optimizer was utilized to enhance the convergence speed and stabilize the model by dynamically adjusting the learning rate. Model performance was assessed using BLEU (Bilingual Evaluation Understudy) scores to evaluate translation accuracy across different training epochs. The findings reveal that employing the Adam optimizer led to a significant enhancement in model performance. At epoch 2000, the model using the Adam optimizer achieved the highest BLEU score of 0.989957, reflecting very accurate translations, whereas the model without the optimizer showed lower results. Furthermore, translations from Indonesian to Javanese were found to be more precise than those from Javanese to Indonesian, largely due to the intricate structure and varying speech levels of the Javanese language. In summary, the implementation of the LSTM method with the Adam optimizer significantly improved the accuracy of bidirectional translations between Indonesian and Javanese. This research contributes notably to the advancement of local language translation technologies, supporting language preservation in the digital age and holding promise for applications in other regional languages.
Optimizing Indonesian-Sundanese Bilingual Translation with Adam-Based Neural Machine Translation Nada, Anita Qotrun; Wibawa, Aji Prasetya; Putri Syarifa, Dhea Fanny; Fajarwati, Erliana; Putri, Fadia Irsania
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.6116

Abstract

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 by the 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 than Sundanese 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 only partially express, honorifics, and the problem of polysemy. Also, it must be 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 deal with complex language via their self-attention mechanism.
Co-Authors Aji Prasetya Wibawa Collante, Leonel Hernandez Fajarwati, Erliana Nada, Anita Qotrun Putri Syarifa, Dhea Fanny