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Wiadji , Sevia Berliana
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Computer Science & IT

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Identification of Batik in Central Java using Transfer Learning Method Adithama, Stephanie Pamela; Dwiandiyanta, B. Yudi; Wiadji , Sevia Berliana
Jurnal Buana Informatika Vol. 14 No. 02 (2023): Jurnal Buana Informatika, Volume 14, Nomor 2, Oktober 2023
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v14i02.6977

Abstract

Batik was recognized as a human heritage for oral and nonmaterial culture by UNESCO due to its symbolic and philosophical ties to the lives of Indonesians. However, the younger generation is gradually losing its legacy because of technological and sociological changes that have influenced Indonesian batik. Consequently, batik knowledge is disappearing. A convolutional neural network and transfer learning techniques were utilized in deep learning to construct a model recognising batik motifs. The study utilized a dataset of one thousand images, five classes of batik designs (Banji, Kawung, Slope, Parang, and Slobog), and pre-trained architectural models VGG16 and VGG19 on Keras. The best model utilizes the VGG16 architecture, and the number of epochs is 50, with the result of testing accuracy of 0.9200.
Identification of Batik in Central Java using Transfer Learning Method Adithama, Stephanie Pamela; Dwiandiyanta, B. Yudi; Wiadji , Sevia Berliana
Jurnal Buana Informatika Vol. 14 No. 02 (2023): Jurnal Buana Informatika, Volume 14, Nomor 2, Oktober 2023
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v14i02.6977

Abstract

Batik was recognized as a human heritage for oral and nonmaterial culture by UNESCO due to its symbolic and philosophical ties to the lives of Indonesians. However, the younger generation is gradually losing its legacy because of technological and sociological changes that have influenced Indonesian batik. Consequently, batik knowledge is disappearing. A convolutional neural network and transfer learning techniques were utilized in deep learning to construct a model recognising batik motifs. The study utilized a dataset of one thousand images, five classes of batik designs (Banji, Kawung, Slope, Parang, and Slobog), and pre-trained architectural models VGG16 and VGG19 on Keras. The best model utilizes the VGG16 architecture, and the number of epochs is 50, with the result of testing accuracy of 0.9200.
Co-Authors Adithama, Stephanie Pamela B. Yudi Dwiandiyanta