Implementation of Neural Machine Translation in Translating from Indonesian to Sasak Language
DOI:
https://doi.org/10.30812/matrik.v23i2.3465Keywords:
Corpus Source Collection, Dataset, Neural Machine Translation, Sasak Regional LanguageAbstract
Language translation is part of Natural Language Processing, also known as Machine Translation, which helps the process of learning foreign and regional languages using translation technology in sentence form. In Lombok, there are still people who are not very fluent in Indonesian because Indonesian is generally only used at formal events. This research aimed to develop a translation model from Indonesian to Sasak. The method used was the Neural Machine Translation with the Recurrent Neural Network - Long Short Term Memory architecture and the Word2Vec Embedding with a sentence translation system. The dataset used was a parallel corpus from the Tatoeba Project and other open sources, divided into 80% training and 20% validation data. The result of this research was the application of Neural Machine Translation with the Recurrent Neural Network - Long Short Term Memory algorithm, which could produce a model with an accuracy of 99.6% in training data and 71.9% in test data. The highest ROUGE evaluation metric result obtained on the model was 88%. This research contributed to providing a translation model from Indonesian to Sasak for the local community to facilitate communication and preserve regional language culture.
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dalam Bahasa Indonesia menggunakan Microframework Flask,†Jurnal Sistem dan Informatika (JSI), pp. 94–103, 2022.
[3] N. Rezaputra and Y. Denny, “Alih Bentuk Kalimat Non-Formal Menjadi Kalimat Formal Menggunakan Pendekatan Machine
Translation,†vol. 8, no. 1, pp. 379–390, 2022.
[4] N. Rezaputra and Y. D. Prabowo, “Alih Bentuk Kalimat Non-Formal Menjadi Kalimat Formal Menggunakan Pendekatan Machine
Translation,†KALBISIANA Jurnal Sains, Bisnis dan . . . , vol. 8, no. 1, pp. 379–390, 2020.
[5] J. C. Hsu, M. Wu, C. Kim, B. Vora, Y. T. Lien, A. Jindal, K. Yoshida, S. Kawakatsu, J. Gore, J. Y. Jin, C. Lu, B. Chen,
and B. Wu, “Applications of Advanced Natural Language Processing for Clinical Pharmacology,†Clinical Pharmacology and
Therapeutics, vol. 0, no. 0, pp. 1–9, 2024, https://doi.org/10.1002/cpt.3161.
[6] A. S. Talita and A.Wiguna, “Implementasi Algoritma Long Short-Term Memory (LSTM) UntukMendeteksi Ujaran Kebencian
(Hate Speech) Pada Kasus Pilpres 2019,†MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 19,
no. 1, pp. 37–44, 2019, https://doi.org/10.30812/matrik.v19i1.495.
[7] Z. Abidin, “Statistical Machine Translation Pada Bahasa Lampung Dialek Api Ke Bahasa Indonesia,†vol. 4, pp. 519–528, 2020,
https://doi.org/10.30865/mib.v4i3.2116.
[8] Y. Fauziyah, R. Ilyas, and F. Kasyidi, “Mesin Penterjemah Bahasa Indonesia-Bahasa Sunda Menggunakan Recurrent Neural
Networks,†Jurnal Teknoinfo, vol. 16, no. 2, p. 313, 2022, https://doi.org/10.33365/jti.v16i2.1930.
[9] L. E. Haris and A. S. Pardiansyah, “Aplikasi Android Kamus Bahasa Indonesia-Sasak,†Jurnal Manajemen Informatika dan
Sistem Informasi, vol. 1, no. 1, p. 1, 2018, https://doi.org/10.36595/misi.v1i1.10.
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https://doi.org/10.1145/3406095.
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Indonesia,†Prosiding Seminar Nasional Metode Kuantitatif, no. 978, pp. 53–68, 2017.
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Arabic to Indonesia,†Matics, vol. 14, no. 1, pp. 13–17, 2022, https://doi.org/10.18860/mat.v14i1.13853.
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cpe.7581.
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And Long Short-Term Sentiment Analysis Using Word2vec And Long Short-Term Memory ( LSTM ) For Indonesian Hotel
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2021, https://doi.org/10.1016/j.procs.2021.01.061.
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downstream tasks,†pp. 134–141, 2022.
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Published
2024-03-28
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Section
Articles
How to Cite
[1]
H. Wardhana, I. M. Yadi Dharma, K. Marzuki, and I. Syarif Hidayatullah, “Implementation of Neural Machine Translation in Translating from Indonesian to Sasak Language”, MATRIK, vol. 23, no. 2, pp. 465–476, Mar. 2024, doi: 10.30812/matrik.v23i2.3465.
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