Batik Classification using Deep Convolutional Network Transfer Learning

Gultom, Yohanes; Arymurthy, Aniati Murni; Masikome, Rian Josua

doi:10.21609/jiki.v11i2.507

Jurnal SINTA 2

Batik Classification using Deep Convolutional Network Transfer Learning

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 11 No. 2 (2018) · pp. 59–66

DOI: 10.21609/jiki.v11i2.507
ISSN: 2502-9274, 2088-7051
Published: 2018-06-29
Publisher: Faculty of Computer Science - Universitas Indonesia
Language: eng

Abstract

Batik fabric is one of the most profound cultural heritage in Indonesia. Hence, continuous research on understanding it is necessary to preserve it. Despite of being one of the most common research task, Batik’s pattern automatic classification still requires some improvement especially in regards to invariance dilemma. Convolutional neural network (ConvNet) is one of deep learning architecture which able to learn data representation by combining local receptive inputs, weight sharing and convolutions in order to solve invariance dilemma in image classification. Using dataset of 2,092 Batik patches (5 classes), the experiments show that the proposed model, which used deep ConvNet VGG16 as feature extractor (transfer learning), achieves slightly better average of 89 ± 7% accuracy than SIFT and SURF-based that achieve 88 ± 10% and 88 ± 8% respectively. Despite of that, SIFT reaches around 5% better accuracy in rotated and scaled dataset.

Keywords

Collaboration network of the authors

19 authors · 34 co-authorship links · 4 across different organisations

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University of Indonesia
RWTH Aachen University
University of Auckland
Affiliation unknown

Different organisations
Same organisation
Affiliation unknown
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Co-author	Organisation	Joint articles
Edward Citrahadi	Affiliation unknown	1
Tieta Antaresti	University of Indonesia	1
Vina Ayumi	University of Indonesia	1
L. M. Rasdi Rere	University of Indonesia	1
Mohamad Ivan Fanany	University of Indonesia	1
Muhamad Soleh	University of Indonesia	1
Sesa Wiguna	University of Auckland	1
Yuni Arti	University of Indonesia	1
Hans Timothy Wijaya	University of Indonesia	1
Juan Thomas Wijaya	University of Indonesia	1
Yohanes Fridolin Hestrio	University of Indonesia	1
Bayu Satria Persada	University of Indonesia	1
Frederic Morado Saragih	University of Indonesia	1
Muhammad Yusuf Kardawi	University of Indonesia	1
Wisnu Aryo Jatmiko	University of Indonesia	1
Herwanto Herwanto	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://jiki.cs.ui.ac.id/index.php/jiki/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">Batik Classification using Deep Convolutional Network Transfer Learning</dc:title>
	<dc:creator>Gultom, Yohanes</dc:creator>
	<dc:creator>Arymurthy, Aniati Murni</dc:creator>
	<dc:creator>Masikome, Rian Josua</dc:creator>
	<dc:subject xml:lang="en-US">Batik</dc:subject>
	<dc:subject xml:lang="en-US">classification</dc:subject>
	<dc:subject xml:lang="en-US">deep learning</dc:subject>
	<dc:subject xml:lang="en-US">transfer learning</dc:subject>
	<dc:description xml:lang="en-US">Batik fabric is one of the most profound cultural heritage in Indonesia. Hence, continuous research on understanding it is necessary to preserve it. Despite of being one of the most common research task, Batik’s pattern automatic classification still requires some improvement especially in regards to invariance dilemma. Convolutional neural network (ConvNet) is one of deep learning architecture which able to learn data representation by combining local receptive inputs, weight sharing and convolutions in order to solve invariance dilemma in image classification. Using dataset of 2,092 Batik patches (5 classes), the experiments show that the proposed model, which used deep ConvNet VGG16 as feature extractor (transfer learning), achieves slightly better average of 89 ± 7% accuracy than SIFT and SURF-based that achieve 88 ± 10% and 88 ± 8% respectively. Despite of that, SIFT reaches around 5% better accuracy in rotated and scaled dataset.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2018-06-29</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/507</dc:identifier>
	<dc:identifier>10.21609/jiki.v11i2.507</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 11 No. 2 (2018): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 59-66</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v11i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/507/395</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2018 Jurnal Ilmu Komputer dan Informasi</dc:rights>
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