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Dwi Sripamuji, Asti
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Computer Science & IT Control & Systems Engineering Engineering

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Automatic Vocal Completion for Indonesian Language Based on Recurrent Neural Network Prasetiadi, Agi; Dwi Sripamuji, Asti; Riski Amalia, Risa; Saputra, Julian; Ramadhanti, Imada
IT Journal Research and Development Vol. 9 No. 1 (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2024.14171

Abstract

Most Indonesian social media users under the age of 25 use various words, which are now often referred to as slang, including abbreviations in communicating. Not only causes, but this variation also poses challenges for the natural language processing of Indonesian. The previous researchers tried to improve the Recurrent Neural Network to correct errors at the character level with an accuracy of 83.76%. This study aims to normalize abbreviated words in Indonesian into complete words using a Recurrent Neural Network in the form of Bidirected Long Short-Term Memory and Gated Recurrent Unit. The dataset is built with several weight confgurations from 3-Gram to 6-Gram consisting of words without vowels and complete words with vowels. Our model is the frst model in the world that tries to fnd incomplete Indonesian words, which eventually become fully lettered sentences with an accuracy of 97.44%.
Co-Authors Agi Prasetiadi Julian Saputra Ramadhanti, Imada Riski Amalia, Risa