Identification of Incung Characters (Kerinci) to Latin Characters Using Convolutional Neural Network

Putri, Tesa Ananda; Suratno, Tri; Khaira, Ulfa

doi:10.22146/ijccs.70939

Open access Jurnal SINTA 2

Identification of Incung Characters (Kerinci) to Latin Characters Using Convolutional Neural Network

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 16 No. 2 (2022) · pp. 205–216

DOI: 10.22146/ijccs.70939
ISSN: 2460-7258, 1978-1520
Published: 2022-04-30
Publisher: IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language: eng
License: creativecommons.org/licenses/by-sa/4.0

Abstract

Incung script is a legacy of the Kerinci tribe located in Kerinci Regency, Jambi Province. On October 17, 2014, the Incung script was designated by the Ministry of Education and Culture as an intangible heritage property owned by Jambi Province. But in reality, the Incung script is almost extinct in society. This study aims to identify the characters of the Incung (Kerinci) script with the output in the form of Latin characters from the Incung script. The classification method used is the Convolutional Neural Network (CNN) method. The dataset used as many as 1400 incung character images divided into 28 classes. In this study, an experiment was conducted to obtain the most optimal model. Showing the results using the CNN method during the training process that the accuracy of the training data reaches 99% and the accuracy of the testing data reaches 91% by using the optimal hyperparameters from the tests that have been done, namely batch size 32, epoch 100, and Adam's optimizer. It evaluates the CNN model using 80 images in words (a combination of several characters) with 4 test scenarios. It shows that the model can recognize image data from scanning printed books, digital writing test data, test data with images containing more than two characters, and check images with different font sizes

Keywords

Collaboration network of the authors

14 authors · 25 co-authorship links

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Universitas Jambi
Affiliation unknown

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Co-author	Organisation	Joint articles
Zainil Abidin	Universitas Jambi	2
Notasonda Soni Putra	Universitas Jambi	1
Benedika Ferdian Hutabarat	Universitas Jambi	1
Andre Ilham Saputra	Affiliation unknown	1
Indra Weni	Universitas Jambi	1
Mutia Fadhila Putri	Affiliation unknown	1
Shally Yanova	Affiliation unknown	1
Edi Saputra	Universitas Jambi	1
Daniel Arsa	Universitas Jambi	1
Norman Syarief	Universitas Jambi	1
Mutia Fadhila Putri	Affiliation unknown	1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en-US">Identification of Incung Characters (Kerinci) to Latin Characters Using  Convolutional Neural Network</dc:title>
	<dc:creator>Putri, Tesa Ananda</dc:creator>
	<dc:creator>Suratno, Tri</dc:creator>
	<dc:creator>Khaira, Ulfa</dc:creator>
	<dc:subject xml:lang="en-US">Information System</dc:subject>
	<dc:subject xml:lang="en-US">Incung Script; Convolutional Neural Network; Deep Learning; Image; Pattern Recognition</dc:subject>
	<dc:description xml:lang="en-US">Incung script is a legacy of the Kerinci tribe located in Kerinci Regency, Jambi Province. On October 17, 2014, the Incung script was designated by the Ministry of Education and Culture as an intangible heritage property owned by Jambi Province. But in reality, the Incung script is almost extinct in society. This study aims to identify the characters of the Incung (Kerinci) script with the output in the form of Latin characters from the Incung script. The classification method used is the Convolutional Neural Network (CNN) method. The dataset used as many as 1400 incung character images divided into 28 classes. In this study, an experiment was conducted to obtain the most optimal model. Showing the results using the CNN method during the training process that the accuracy of the training data reaches 99% and the accuracy of the testing data reaches 91% by using the optimal hyperparameters from the tests that have been done, namely batch size 32, epoch 100, and Adam's optimizer. It evaluates the CNN model using 80 images in words (a combination of several characters) with 4 test scenarios. It shows that the model can recognize image data from scanning printed books, digital writing test data, test data with images containing more than two characters, and check images with different font sizes</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
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	<dc:date>2022-04-30</dc:date>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/70939</dc:identifier>
	<dc:identifier>10.22146/ijccs.70939</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 16, No 2 (2022): April; 205-216</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/70939/33701</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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