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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika
steven willian
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Computer Science & IT

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Design of Batak Toba Script Recognition System Using Convolutional Neural Network Algorithm Steven Willian; Rochadiani, Theresia Herlina; Thamrin Sofian
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12617

Abstract

Indonesia is one of the countries with diversity and abundant cultural wealth, one of which is the Batak Toba script as one of the wealth originating from the Batak tribe. However, the existence of the Batak Toba script is decreasing along with the rapid development of the times, due to the lack of interest of the younger generation and public awareness in preserving the Batak Toba script. From these problems, the author conducted research to create a model of introducing the Batak Toba script, as an effort to preserve the Batak Toba script which is one of Indonesia's cultural wealth. The purpose of this research is to create a Batak Toba script recognition model using a digital handwriting dataset, and has an output in the form of visual text and with audio pronunciation of each script. The method used in this research is the Convolutional Neural Network algorithm combined with RMSprop optimizer. Convolutional Neural Network is an algorithm that is one of the deep learning methods that has good performance on image data. The results of this study incised a recognition model with a relatively high level of accuracy, which is equal to 99,54% which was tested on the Batak Toba script dataset in the form of digital handwriting. Through this research, the model using the Convolutional Neural Network algorithm used in this research is able to produce good results for recognizing the Batak Toba script in the form of handwriting.
Co-Authors Rochadiani, Theresia Herlina Thamrin Sofian