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

Teguh Ikhlas Ramadhan; Nur Ghaniaviyanto Ramadhan; Agus Supriatman

doi:10.47065/bits.v4i3.2614

Teguh Ikhlas Ramadhan * Universitas Perjuangan, Tasikmalaya, Indonesia
Nur Ghaniaviyanto Ramadhan Institut Teknologi Telkom Purwokerto, Purwokerto, Indonesia
Agus Supriatman Universitas Perjuangan, Tasikmalaya, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/bits.v4i3.2614

Keywords: Neural Machine Translation; Recurrent Neural Network; Encoder Decoder Long Short-Term Memory (LTSM)

Abstract

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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