MESIN PENTERJEMAH BAHASA INDONESIA-BAHASA SUNDA MENGGUNAKAN RECURRENT NEURAL NETWORKS

Abstract

Translator is a process where one language is changed into another language. Translator in the last research was carried out using a Phrase-based Statistical Machine Translation (PSMT) approach. This research builds an Indonesian to Sundanese translator. The stages used start from pre-processing using text preprocessing and word embedding Word2Vec and the approach used is Neural Machine Translation (NMT) with Encoder-Decoder architecture in which there is a Recurrent Neural Network (RNN). Tests in the study resulted in the optimal value by the GRU of 99.17%. Models using Attention got 99.94%. The use of optimization model got optimal results by Adam 99.35% and BLEU Score results with optimal bleu 92.63% and brievity penalty 0.929. The results of the machine translator produce training predictions from Indonesian to Sundanese if the input sentences are in accordance with the corpus and the translation results are not suitable when the input sentences are different from the corpus.