Mesin Penterjemah Bahasa Indonesia-Bahasa Sunda Menggunakan Recurrent Neural Networks

Fauziyah, Yustiana; Ilyas, Ridwan; Kasyidi, Fatan

doi:10.33365/jti.v16i2.1930

Cited by 8 publications

(10 citation statements)

References 9 publications

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“…Another research is the implementation of the long short-term memory (LSTM) algorithm to detect hate speech in the 2019 presidential election case [6], while the research we propose is entitled Implementation of Neural Machine Translation in Translation from Indonesian to Sasak Language using NMT with the RNN-LSTM and Word2vec algorithms. Implementing language translation from Indonesian to regional languages is usually carried out using a statistics-based approach [2,7,8]. The research results show that the implementation of translation using a statistical approach is still word for word, not sentence, which creates new problems in determining grammar and word order [9].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Neural Machine Translation in Translating from Indonesian to Sasak Language

Wardhana,

Yadi Dharma,

Marzuki

et al. 2024

matrik

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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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Section: Introductionmentioning

confidence: 99%

Implementation of Neural Machine Translation in Translating from Indonesian to Sasak Language

Wardhana,

Yadi Dharma,

Marzuki

et al. 2024

matrik

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“…Papua has many ethnicities and subethnicities with many different languages which are starting to enter the stage of language extinction [3]. One effort to preserve regional languages is to build national to regional language machine translations so that users understand regional language speakers [4]. Machine translation is software that is capable of changing one language into another different language [5].…”

Section: Introductionmentioning

confidence: 99%

“…Previous research on machine translation from Indonesian to regional languages includes Indonesian to Lampung [6], Indonesian-Dayak language [7], Indonesian-Sambas language [8], Indonesian-Sundanese language [9], Ketapang [10] and Kawi [11]. Meanwhile, GRU is the state of the art of internal RNN which has been used to build an Indonesian-Sundanese language machine translation [4].…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network-Gated Recurrent Unit for Indonesia-Sentani Papua Machine Translation

Achmad,

Tokoro,

Haurissa

et al. 2023

J. Inf. Syst. Informatics

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The Papuan Sentani language is spoken in the city of Jayapura, Papua. The law states the need to preserve regional languages. One of them is by building an Indonesian-Sentani Papua translation machine. The problem is how to build a translation machine and what model to choose in doing so. The model chosen is Recurrent Neural Network – Gated Recurrent Units (RNN-GRU) which has been widely used to build regional languages in Indonesia. The method used is an experiment starting from creating a parallel corpus, followed by corpus training using the RNN-GRU model, and the final step is conducting an evaluation using Bilingual Evaluation Understudy (BLEU) to find out the score. The parallel corpus used contains 281 sentences, each sentence has an average length of 8 words. The training time required is 3 hours without using a GPU. The result of this research was that a fairly good BLEU score was obtained, namely 35.3, which means that the RNN-GRU model and parallel corpus produced sufficient translation quality and could still be improved.

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“…al. [6] using the Recurrent Neural Network based to do the machine translation from Indonesian to Sundanese language. The result that the study achieves a great result with GRU and attention-based model.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Neural Machine Translation for English-Sundanese Language using Long Short Term Memory (LSTM)

Ramadhan

Supriatman

2022

bits

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In this modern era, machine translation has been used all over the world for solving humankind’s problems such as it deals with language. Machine translation is almost used by people who want to translate their native language into their foreign language. The international language being used is the English language. Machine translation is the task to translate a source language to another language. The input of it is a word or a sentence from the source language and it will be translated into another language. The input of it is a word or a sentence from the source language and it will be translated into another language. There are many purposes for using machine translation such as learning another language, communicating, finding a certain or better word to use, and even writing something in a book or another article. Several methods have been conducted to do the machine translation task such as the statistical approach and the neural approach In terms of Sundanese machine translation, there are several methods or several approaches that other researchers have conducted. However the study about Sundanese machine translation, none of the research conducted the English into Sundanese language. In this study using the encoder and decoder LSTM architecture achieve a good result regarding building a model for machine translation task. The performance of this model has achieved 0.99 accuracies in both training and testing as well as less than 0.1 loss value to both training and testing data. This model also achieves more than 0.8 average BLEU score for both training and testing data.

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Mesin Penterjemah Bahasa Indonesia-Bahasa Sunda Menggunakan Recurrent Neural Networks

Cited by 8 publications

References 9 publications

Implementation of Neural Machine Translation in Translating from Indonesian to Sasak Language

Implementation of Neural Machine Translation in Translating from Indonesian to Sasak Language

Recurrent Neural Network-Gated Recurrent Unit for Indonesia-Sentani Papua Machine Translation

Implementation of Neural Machine Translation for English-Sundanese Language using Long Short Term Memory (LSTM)

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