Optimizing Indonesian-Sundanese Bilingual Translation with Adam-Based Neural Machine Translation
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 d...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2024-12-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6116 |
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| author | Anita Qotrun Nada Aji Prasetya Wibawa Dhea Fanny Putri Syarifa Erliana Fajarwati Fadia Irsania Putri |
| author_facet | Anita Qotrun Nada Aji Prasetya Wibawa Dhea Fanny Putri Syarifa Erliana Fajarwati Fadia Irsania Putri |
| author_sort | Anita Qotrun Nada |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bb8bb60f35e944479014f9ef3b9d02c2 |
| institution | Kabale University |
| issn | 2580-0760 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-bb8bb60f35e944479014f9ef3b9d02c22025-01-13T03:30:32ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602024-12-018669070010.29207/resti.v8i6.61166116Optimizing Indonesian-Sundanese Bilingual Translation with Adam-Based Neural Machine TranslationAnita Qotrun Nada0Aji Prasetya Wibawa1Dhea Fanny Putri Syarifa2Erliana Fajarwati3Fadia Irsania Putri4Universitas Negeri MalangUniversitas Negeri MalangUniversitas Negeri MalangUniversitas Negeri MalangUniversitas Negeri MalangThis 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.https://jurnal.iaii.or.id/index.php/RESTI/article/view/6116adambleuindonesianlstmsundanese |
| spellingShingle | Anita Qotrun Nada Aji Prasetya Wibawa Dhea Fanny Putri Syarifa Erliana Fajarwati Fadia Irsania Putri Optimizing Indonesian-Sundanese Bilingual Translation with Adam-Based Neural Machine Translation Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) adam bleu indonesian lstm sundanese |
| title | Optimizing Indonesian-Sundanese Bilingual Translation with Adam-Based Neural Machine Translation |
| title_full | Optimizing Indonesian-Sundanese Bilingual Translation with Adam-Based Neural Machine Translation |
| title_fullStr | Optimizing Indonesian-Sundanese Bilingual Translation with Adam-Based Neural Machine Translation |
| title_full_unstemmed | Optimizing Indonesian-Sundanese Bilingual Translation with Adam-Based Neural Machine Translation |
| title_short | Optimizing Indonesian-Sundanese Bilingual Translation with Adam-Based Neural Machine Translation |
| title_sort | optimizing indonesian sundanese bilingual translation with adam based neural machine translation |
| topic | adam bleu indonesian lstm sundanese |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6116 |
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