Identification of Batik Motif Based Deep Learning-Convolutional Neural Network Approach
Abstract
Batik, a rich Indonesian cultural heritage, boasts a diverse array of motifs, each reflecting the unique philosophy of different regions. However, this diversity can make it challenging to distinguish between various batik patterns. This study aims to identify batik motifs using the Convolutional Neural Network (CNN) method. This research dataset comprises 521 digital batik images, encompassing five distinct motifs: Betawi, Cendrawasih, Kawung, Megamendung, and Parang. The data underwent a rigorous processing pipeline, including pre-processing, image segmentation, and feature extraction using Gray Level Co-occurrence Matrix (GLCM). Subsequently, a CNN model was employed for classification. The experimental results yielded an impressive average accuracy of 99.2% in identifying batik motifs. This outcome underscores the efficacy of deep learning, particularly CNNs, in recognizing and categorizing intricate batik patterns. This study may expect to serve a foundational step towards the development of advanced, automated batik recognition systems.
##Keywords:##
Batik, Convolutional Neural Network, Motif Identification, Digital Image, Deep Learning
Published
Nov 30, 2024
How to Cite
OKTARINO, Ade; TASRI, Yanti Desnita; EFENDI, Akmar.
Identification of Batik Motif Based Deep Learning-Convolutional Neural Network Approach.
Journal of Ocean, Mechanical and Aerospace -science and engineering-, [S.l.], v. 68, n. 3, p. 139-147, nov. 2024.
ISSN 2527-6085.
Available at: <https://isomase.org/Journals/index.php/jomase/article/view/381>. Date accessed: 19 mar. 2025.
doi: http://dx.doi.org/10.36842/jomase.v68i3.381.
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[5] Hakim, L., Kristanto, S.P., Yusuf, D. & Afia, F.N. (2022). Pengenalan Motif Batik Banyuwangi Berdasarkan Fitur Grey Level Co-Occurrence Matrix. Jurnal Teknoinfo, 16(1), 1-7.
[6] Fajar, R.M., Mulyono, H. & Adi, F.P. (2021). Identifikasi Nilai Karakter Motif Batik Ngawi Berbasis Budaya Lokal sebagai Muatan Pendidikan Seni Rupa di Sekolah Dasar. Jurnal basicedu, 5(2), 571-580.
[7] Wijaya, Y.A. (2021). Analisa Klasifikasi menggunakan Algoritma Decision Tree pada Data Log Firewall Jurnal Sistem Informasi dan Manajemen. Jurnal Sistem Informasi dan Manajemen (JURSIMA), 9(3), 256-264.
[8] Hartono, S., Perwitasari, A. & Sujaini, H. (2020). Komparasi Algoritma Nonparametrik untuk Klasifikasi Citra Wajah Berdasarkan Suku di Indonesia. JEPIN (Jurnal Edukasi dan Penelitian Informatika), 6(3), 337-343.
[9] Tasri, Y. (2022). Identification of Signature Images with Edge Detection Canny. Journal Of Ocean, Mechanical And Aerospace -Science And Engineering-, 66(3), 89-93. doi:10.36842/jomase.v66i3.316.
[10] Ryansyah, R. (2021). Identifikasi tingkatan warna pada kopi roasting menggunakan metode HSV berbasis mobile. Jurnal Terapan Informatika Nusantara, 1(10), 520–526.
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[13] Diana, R., Warni, H. & Sutabri, T. (2023). penggunaan teknologi machine learning untuk pelayanan monitoring kegiatan belajar mengajar pada smk bina sriwijaya palembang. Jurnal Teknik Informatika (JUTEKIN), 11(1).
[14] Wijanarko, E.W.S. & Adhisa, R.R. (2023). Media pembelajaran object detection perangkat jaringan komputer menggunakan machine learning berbasis desktop. Edumatic: Jurnal Pendidikan Informatika, 7(2), 207-216.
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