A Web-Based for Demak Batik Classification Using VGG16 Convolutional Neural Network
DOI:
https://doi.org/10.26877/asset.v6i4.771Keywords:
Batik Demak, Deep learning, Convolutional Network, Classification, VGG16, Cultural PreservationAbstract
The diversity of Demak batik motifs presents challenges in classification and identification. This research aims to develop a Demak batik motif classification system using deep learning and VGG16 convolutional network. A dataset of Demak batik images is collected and processed to train the model. The VGG16 architecture is modified by fine-tuning to optimize the classification performance. Results show that the modified VGG16 model achieved a classification accuracy of 98.72% on the test dataset, demonstrating its potential application in preserving and digitizing Demak batik cultural heritage.
References
S. Ariessaputra, V. H. Vidiasari, S. Mariyanto, A. Sasongko, B. Darmawan, and S. Nababan, “Classification of Lombok Songket and Sasambo Batik Motifs Using the Convolution Neural Network (CNN) Algorithm,” 2024. [Online]. Available: www.joiv.org/index.php/joiv
A. H. Rangkuti, A. Harjoko, and A. Putra, “A Novel Reliable Approach for Image Batik Classification That Invariant with Scale and Rotation Using MU2ECS-LBP Algorithm,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 863–870. doi: 10.1016/j.procs.2021.01.075.
B. D. Mardiana, W. B. Utomo, U. N. Oktaviana, G. W. Wicaksono, and A. E. Minarno, “Herbal Leaves Classification Based on Leaf Image Using CNN Architecture Model VGG16,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 7, no. 1, pp. 20–26, Feb. 2023, doi: 10.29207/resti.v7i1.4550.
D. A. Anggoro, A. A. T. Marzuki, and W. Supriyanti, “Classification of Solo Batik patterns using deep learning convolutional neural networks algorithm,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 22, no. 1, pp. 232–240, Feb. 2024, doi: 10.12928/TELKOMNIKA.v22i1.24598.
S. Mallick and S. P. Mishra, “Skin Cancer Detection using CNN (VGG16) inculcated with CLAH and Gaussian Filter,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 9s, pp. 157–163, Aug. 2023, doi: 10.17762/ijritcc.v11i9s.7407.
L. R. Bague, R. Jr. L. Jorda, B. N. Fortaleza, A. DM. Evanculla, M. A. V. Paez, and J. S. Velasco, “Recognition of Baybayin (Ancient Philippine Character) Handwritten Letters Using VGG16 Deep Convolutional Neural Network Model,” International Journal of Emerging Trends in Engineering Research, vol. 8, no. 9, pp. 5233–5237, Sep. 2020, doi: 10.30534/ijeter/2020/55892020.
K. Azmi, S. Defit, and U. Putra Indonesia YPTK Padang Jl Raya Lubuk Begalung-Padang-Sumatera Barat, “Implementasi Convolutional Neural Network (CNN) Untuk Klasifikasi Batik Tanah Liat Sumatera Barat,” vol. 16, no. 1, pp. 1–13, 2023.
E. Sentosa et al., “Implementasi Image Classification Pada Batik Motif Bali Dengan Data Augmentation dan Convolutional Neural Network,” vol. 6, no. 1, pp. 1–13, 2022.
H. Hatmawati, S. Hidayati, A. Aghnaita, and D. Oktavia, “Implementation Of Eco Print Activities In Stimulating Children’s Fine Motor Development Based On Local Wisdom,” European Alliance for Innovation n.o., Dec. 2023. doi: 10.4108/eai.26-11-2022.2339539.
R. K. Sethi and K. Kumar Mohanty, “Optical Odia Character Classification using CNN and Transfer Learning: A Deep Learning Approach,” International Research Journal of Engineering and Technology, 2020, [Online]. Available: www.irjet.net
C. Uswatun Khasanah, A. Kusuma Pertiwi, F. Witamajaya, P. Akbara Surakarta, and J. Sumbing Raya, “Implementasi Data Augmentation Random Erasing dan GridMask pada CNN untuk Klasifikasi Batik Implementation of Random Erasing and GridMask Data Augmentations on CNN for Batik Classification,” vol. 13, no. 1, 2023, doi: 10.30700/jst.v13i1.1274.
Moh. A. Hasan, Y. Riyanto, and D. Riana, “Grape leaf image disease classification using CNN-VGG16 model,” Jurnal Teknologi dan Sistem Komputer, vol. 9, no. 4, pp. 218–223, Oct. 2021, doi: 10.14710/jtsiskom.2021.14013.
Y. Azhar, Moch. C. Mustaqim, and A. E. Minarno, “Ensemble convolutional neural network for robust batik classification,” IOP Conf Ser Mater Sci Eng, vol. 1077, no. 1, p. 012053, Feb. 2021, doi: 10.1088/1757-899x/1077/1/012053.
E. A. Nabila, C. A. Sari, E. H. Rachmawanto, and M. Doheir, “A Good Performance of Convolutional Neural Network Based on AlexNet in Domestic Indonesian Car Types Classification,” Advance Sustainable Science Engineering and Technology, vol. 5, no. 3, p. 0230302, Oct. 2023, doi: 10.26877/asset.v5i3.16854.
A. Sunyoto et al., “The Performance Evaluation of Transfer Learning VGG16 Algorithm on Various Chest X-ray Imaging Datasets for COVID-19 Classification.” [Online]. Available: www.ijacsa.thesai.org
A. Susanto, C. A. Sari, E. H. Rachmawanto, I. U. W. Mulyono, and N. Mohd Yaacob, “A Comparative Study of Javanese Script Classification with GoogleNet, DenseNet, ResNet, VGG16 and VGG19,” Scientific Journal of Informatics, vol. 11, no. 1, pp. 31–40, Jan. 2024, doi: 10.15294/sji.v11i1.47305.
M. Omran and E. N. Alshemmary, “An Iris Recognition System Using Deep convolutional Neural Network,” in Journal of Physics: Conference Series, Institute of Physics Publishing, May 2020. doi: 10.1088/1742-6596/1530/1/012159.
F. Idlahcen, M. M. Himmi, and A. Mahmoudi, “CNN-based Approach for Cervical Cancer Classification in Whole-Slide Histopathology Images,” May 2020, [Online]. Available: http://arxiv.org/abs/2005.13924
Y. Sun, B. Xue, M. Zhang, G. G. Yen, and J. Lv, “Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification,” IEEE Trans Cybern, vol. 50, no. 9, pp. 3840–3854, Sep. 2020, doi: 10.1109/TCYB.2020.2983860.
L. Z. Yong, S. Khairunniza-Bejo, M. Jahari, and F. MelissaMuharam, “Automatic detection of an early stage of basal stem rot disease infection using VGG16 and mask R-CNN,” in IOP Conference Series: Earth and Environmental Science, Institute of Physics, 2023. doi: 10.1088/1755-1315/1133/1/012076.
R. Mohan, K. Ganapathy, and A. Rama, “Brain tumour classification of magnetic resonance images using a novel CNN-based medical image analysis and detection network in comparison to VGG16,” Journal of Population Therapeutics and Clinical Pharmacology, vol. 28, no. 2, pp. e113–e125, 2021, doi: 10.47750/jptcp.2022.873.
D. Gede, T. Meranggi, N. Yudistira, and Y. A. Sari, “Batik Classification Using Convolutional Neural Network with Data Improvements,” 2022. [Online]. Available: www.joiv.org/index.php/joiv
H. Prasetyo and B. A. Putra Akardihas, “Batik image retrieval using convolutional neural network,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 17, no. 6, pp. 3010–3018, Dec. 2019, doi: 10.12928/TELKOMNIKA.v17i6.12701.
Z. Widyantoko, T. P. Widowati, Isnaini, and P. Trapsiladi, “Expert role in image classification using cnn for hard to identify object: Distinguishing batik and its imitation,” IAES International Journal of Artificial Intelligence, vol. 10, no. 1, pp. 93–100, 2021, doi: 10.11591/ijai.v10.i1.pp93-100.
M. A. Rasyidi and T. Bariyah, “Batik pattern recognition using convolutional neural network,” Bulletin of Electrical Engineering and Informatics, vol. 9, no. 4, pp. 1430–1437, Aug. 2020, doi: 10.11591/eei.v9i4.2385.
N. D. Girsang, “Literature Study of Convolutional Neural Network Algorithm for Batik Classification,” Briliance Research of Artificial Intelligence, vol. 1, no. 1, pp. 1–7, Feb. 2021, doi: 10.47709/briliance.v1i1.1069.
S. Winiarti, I. Faisal, U. Ahmad Dahlan Yogyakarta Kampus, and U. Ringroad Selatan, “Particle Swarm Optimization Algorithm for Hyperparameter Convolutional Neural Network and Transfer Learning VGG16 Model Murinto,” Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE), vol. 5, no. 1, pp. 474–480, 2024, doi: 10.30596/jcositte.v5i1.16680.
