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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research
Fajarwati, Erliana
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Computer Science & IT Electrical & Electronics Engineering Engineering

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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.
LSTM Model Using Adam’s Optimizer for Indonesian – Bugis Bidirectional Translation System Fajarwati, Erliana; Wibawa, Aji Prasetya; Hernandez, Leonel
International Journal of Artificial Intelligence Research Vol 9, No 1 (2025): June
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1272

Abstract

The purpose of this research is to develop a machine translation of Bugis to Indonesian and vice versa in order to preserve the Bugis language. This research utilizes a recent dataset consisting of 30,000 Bugis-Indonesian sentence pairs from the online Bible. This research conducts scraping to compile the corpus which is then followed by manual and automatic pre-processing. The method chosen is Neural Machine Translation (NMT) while for training and testing models Long Short-Term Memory (LSTM) is used. The performance of the model is evaluated by Bilingual Evaluation Understudy (BLEU) score to measure the translation accuracy at various epochs. In addition, this study also compared the use of Adam's optimizer with non-optimizer. The results showed that the use of Adam's optimizer significantly improved the performance of the model where at epoch 2000 the model achieved the highest BLEU score of 0.996261 indicating highly accurate translation quality. In contrast, the model without the optimizer showed lower performance. Other results also found that the translation from Bugis to Indonesian was more accurate than from Indonesian to Bugis. This is due to the more balanced word count difference in the Bugis to Indonesian translation, which makes it easier for the model to match words. In conclusion, the use of NMT with Adam optimizer effectively improves the accuracy of two-way translation from Bugis-Indonesian.
Co-Authors Aji Prasetya Wibawa Leonel Hernandez, Leonel Nada, Anita Qotrun Putri Syarifa, Dhea Fanny Putri, Fadia Irsania