Please use this identifier to cite or link to this item: http://repository.kalbis.ac.id/handle/123456789/216
Title: Alih Bentuk Kalimat Non-Formal menjadi Kalimat Formal Menggunakan Pendekatan Machine Translation
Authors: Rezaputra, Nathanael
Prabowo, Yulius Denny
Keywords: Natural Language Programming
LSTM
over form of formal sentences
BLEU
Issue Date: 27-Jun-2020
Publisher: Institut Teknologi dan Bisnis Kalbis
Abstract: The purpose of this study is the development of a model by applying the Short Term Memory algorithm to transform informal sentences into formal sentences and determine the supporting and inhibiting factors of the accuracy value generated in the machine learning process. The method used is action research. Software development uses an incremental model through analysis, design, implementation, and testing. The agreed results are part of the results of the study which tests the standard words into formal sentences and determines the factors of the accuracy value generated in the study. The BLEU evaluation determines whether or not sentences are predicted by measuring the closeness of the formal sentences from the dataset. This research resulted in a web-based application. The conclusion obtained is a training model that is built to produce value and loss with a value of 0.7011 and 3.7401. Based on these results, training models are not good enough. This causes several factors that lead to training overfitting models.
URI: http://repository.kalbis.ac.id/handle/123456789/216
Appears in Collections:IF 2020

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