This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Implementation of Deep Learning Based on Convolution Neural Network for Batik Pattern Recognition
Corresponding Author(s) : Edi Sugiarto
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 10, No. 1, February 2025
Abstract
Batik as a cultural heritage is one of the heritages that needs to be preserved so that it continues to be recognized from generation to generation. Efforts to preserve batik can be made by using technology that can recognize batik motifs. Pattern recognition is a branch of science related to the identification, classification, and interpretation of patterns. Deep learning is one of the technologies that can be used very well for pattern recognition, especially for syllable and image recognition. Convolutional neural network (CNN) is one of the most popular deep learning methods and the most established algorithm for deep learning models. The main advantage of CNN over the preceding methods is its ability to automatically detect features, making the feature extraction and classification process highly organized. This study aims to apply CNN for batik pattern recognition. The batik patterns used were geometric patterns, divided into 7 batik classes. Experiments were conducted on 3100 data, consisting of 3000 for training set and 100 for testing set. At the preprocessing stage, the batik image was resized to 28x28, and the color was changed to grayscale. Training was carried out on 100, 200, and 300 epochs. The classification results prove that CNN can recognize batik patterns well with an accuracy rate of 95%.
Keywords
Download Citation
Endnote/Zotero/Mendeley (RIS)BibTeX
- . Edi Sugiarto. “Optimization of Discrete Wavelet Transform Based on Gray Level Co-occurrence Matrix (GLCM) and Principal Component Analysis (PCA) to Improve Features Extraction on Batik Pattern Recognition”. International Journal of Emerging Technology and Advanced Engineering (IJETAE) 2023, Vol 13, Issue 5, ISSN: 2250-2459. https://doi.org/10.46338/ijetae0523_12
- . Fikri Budiman. “Non-linear Multiclass SVM Classification Optimization using Large Datasets of Geometric Motif Image”. (IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 12, No. 9, 2021. https://dx.doi.org/10.14569/IJACSA.2021.0120932
- . Ida Nurhaida. “Automatic Indonesian Batik Pattern Recognition Using SIFT Approach”. Procedia Computer Science 59 (2015) 567-576.
- . Ignatia Dhian E.K.R, “Klasifikasi Batik Menggunakan KNN Berbasis Wavelet”, Seminar Nasional Teknologi Informasi dan Komunikasi 2016 (SENTIKA 2016), ISSN : 2089-9815, Yogyakarta.
- . Xiao, Y.; Tian, Z.; Yu, J.; Zhang, Y.; Liu, S.; Du, S.; Lan, X. “A review of object detection based on deep learning”. Multimed. Tools Appl. 2020, 79, 23729–23791. https://doi.org/10.1007/s11042-020-08976-6
- . LeCun, Y., Bengio, Y., & Hinton, G. “Deep learning Nature”, 2015, 521(7553), 436-444.
- . Alzubaidi, L., Zhang, J., Humaidi, A.J. et al. “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions”. J Big Data 8, 53 (2021). https://doi.org/10.1186/s40537-021-00444-8
- . Chandrahas Mishra. “Deep Machine Learning and Neural Networks: An Overview”. IAES International Journal of Artificial Intelligence (IJ-AI), Vol. 6, No. 2, June 2017, pp. 66-73, ISSN: 2252-8938. http://doi.org/10.11591/ijai.v6.i2.pp66-73
- . Mohammad Mustafa Taye. “Theoretical Understanding of Convolutional Neural Network:Concepts, Architectures, Applications, Future Directions”. Computation 2023, 11, 52. https://doi.org/10.3390/computation11030052h
- . Yasaka K, Akai H, Abe O, Kiryu S “Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study”. 2018, Radiology 286:887–896. https://doi.org/10.1148/radiol.2017170706
- . Talo M, Yildirim O, Baloglu UB, Aydin G, Acharya UR. “Convolutional neural networks for multi‑class brain disease detection using MRI images”. Comput Med Imaging Gr. 2019;78:101673. https://doi.org/10.1016/j.compmedimag.2019.101673
- . Bezdan, T.; Džakula, N.B. “Convolutional Neural Network Layers and Architectures”. In International Scientific Conference on Information Technology and Data Related Research; Singidunum University: Belgrade, Serbia, 2019; pp. 445–451. http://dx.doi.org/10.15308/Sinteza-2019-445-451
- . Laith Alzubaidi. “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions”. Journal of Big Data 2021, 8:53. https://doi.org/10.1186/s40537-021-00444-8
- . Thaha MM, Kumar KPM, Murugan B, Dhanasekeran S, Vijayakarthick P, Selvi AS. “Brain tumor segmentation using convolutional neural networks in MRI images”. J Med Syst. 2019;43(9):294. https://doi.org/10.1007/s10916-019-1416-0
- . Yamashita, R., Nishio, M., Do, R.K.G. et al. “Convolutional neural networks: an overview and application in radiology”. Insights Imaging 9, 611–629 (2018). https://doi.org/10.1007/s13244-018-0639-9
- . Rikiya Yamashita. “Convolutional neural networks: an overview and application in radiology”, Insights into Imaging (2018) 9:611–629. https://doi.org/10.1007/s13244-018-0639-9
- . Zhiming Xie. “A Face Recognition Method Based on CNN”, Journal of Physics: Conference Series 2019, Vol : 1395. doi.org/10.1088/1742-6596/1395/1/012006
- . Yao G, Lei T, Zhong J. “A review of convolutional‑neural‑network‑based action recognition”. Pattern Recogn Lett. 2019;118:14–22. https://doi.org/10.1016/j.patrec.2018.05.018
- . Alicia. ”Filosofi Motif Batik Sebagai Identitas Bangsa Indonesia”. Jurnal FOLIO Volume 1 No. 1 Februari 2020.
- . Wahyudi setiawan. “Perbandingan Arsitektur Convolutional Neural Network Untuk Klasifikasi Fundus”. Jurnal SIMATEC, Vol. 7, No. 2, Juni 2019, ISSN 2088-2130. https://doi.org/10.21107/simantec.v7i2.6551
- . Hirahara D, Takaya E, Takahara T, Ueda T. “Efects of data count and image scaling on deep learning training”. PeerJ Comput Sci. 2020;6:312. https://doi.org/10.7717/peerj-cs.312
- . Jiao L, Zhang F, Liu F, Yang S, Li L, Feng Z, Qu R. “A survey of deep learning‑based object detection”. IEEE Access. 2019;7:128837–68. https://doi.org/10.48550/arXiv.1907.09408
- . I Wayan Suartika E. P. “Klasifikasi Citra Menggunakan ConvolutionalNeural Network (Cnn) pada Caltech 101”, JURNAL TEKNIK ITS Vol. 5, No. 1, (2016) ISSN: 2337-3539.
- . Shorten C, Khoshgoftaar TM, Furht B. “Deep learning applications for COVID‑19”. J Big Data. 2021;8(1):1–54. https://doi.org/10.1186/s40537-020-00392-9
- . Zhou DX. “Theory of deep convolutional neural networks: downsampling”. Neural Netw. 2020;124:319–27. https://doi.org/10.1016/j.neunet.2020.01.018
References
. Edi Sugiarto. “Optimization of Discrete Wavelet Transform Based on Gray Level Co-occurrence Matrix (GLCM) and Principal Component Analysis (PCA) to Improve Features Extraction on Batik Pattern Recognition”. International Journal of Emerging Technology and Advanced Engineering (IJETAE) 2023, Vol 13, Issue 5, ISSN: 2250-2459. https://doi.org/10.46338/ijetae0523_12
. Fikri Budiman. “Non-linear Multiclass SVM Classification Optimization using Large Datasets of Geometric Motif Image”. (IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 12, No. 9, 2021. https://dx.doi.org/10.14569/IJACSA.2021.0120932
. Ida Nurhaida. “Automatic Indonesian Batik Pattern Recognition Using SIFT Approach”. Procedia Computer Science 59 (2015) 567-576.
. Ignatia Dhian E.K.R, “Klasifikasi Batik Menggunakan KNN Berbasis Wavelet”, Seminar Nasional Teknologi Informasi dan Komunikasi 2016 (SENTIKA 2016), ISSN : 2089-9815, Yogyakarta.
. Xiao, Y.; Tian, Z.; Yu, J.; Zhang, Y.; Liu, S.; Du, S.; Lan, X. “A review of object detection based on deep learning”. Multimed. Tools Appl. 2020, 79, 23729–23791. https://doi.org/10.1007/s11042-020-08976-6
. LeCun, Y., Bengio, Y., & Hinton, G. “Deep learning Nature”, 2015, 521(7553), 436-444.
. Alzubaidi, L., Zhang, J., Humaidi, A.J. et al. “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions”. J Big Data 8, 53 (2021). https://doi.org/10.1186/s40537-021-00444-8
. Chandrahas Mishra. “Deep Machine Learning and Neural Networks: An Overview”. IAES International Journal of Artificial Intelligence (IJ-AI), Vol. 6, No. 2, June 2017, pp. 66-73, ISSN: 2252-8938. http://doi.org/10.11591/ijai.v6.i2.pp66-73
. Mohammad Mustafa Taye. “Theoretical Understanding of Convolutional Neural Network:Concepts, Architectures, Applications, Future Directions”. Computation 2023, 11, 52. https://doi.org/10.3390/computation11030052h
. Yasaka K, Akai H, Abe O, Kiryu S “Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study”. 2018, Radiology 286:887–896. https://doi.org/10.1148/radiol.2017170706
. Talo M, Yildirim O, Baloglu UB, Aydin G, Acharya UR. “Convolutional neural networks for multi‑class brain disease detection using MRI images”. Comput Med Imaging Gr. 2019;78:101673. https://doi.org/10.1016/j.compmedimag.2019.101673
. Bezdan, T.; Džakula, N.B. “Convolutional Neural Network Layers and Architectures”. In International Scientific Conference on Information Technology and Data Related Research; Singidunum University: Belgrade, Serbia, 2019; pp. 445–451. http://dx.doi.org/10.15308/Sinteza-2019-445-451
. Laith Alzubaidi. “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions”. Journal of Big Data 2021, 8:53. https://doi.org/10.1186/s40537-021-00444-8
. Thaha MM, Kumar KPM, Murugan B, Dhanasekeran S, Vijayakarthick P, Selvi AS. “Brain tumor segmentation using convolutional neural networks in MRI images”. J Med Syst. 2019;43(9):294. https://doi.org/10.1007/s10916-019-1416-0
. Yamashita, R., Nishio, M., Do, R.K.G. et al. “Convolutional neural networks: an overview and application in radiology”. Insights Imaging 9, 611–629 (2018). https://doi.org/10.1007/s13244-018-0639-9
. Rikiya Yamashita. “Convolutional neural networks: an overview and application in radiology”, Insights into Imaging (2018) 9:611–629. https://doi.org/10.1007/s13244-018-0639-9
. Zhiming Xie. “A Face Recognition Method Based on CNN”, Journal of Physics: Conference Series 2019, Vol : 1395. doi.org/10.1088/1742-6596/1395/1/012006
. Yao G, Lei T, Zhong J. “A review of convolutional‑neural‑network‑based action recognition”. Pattern Recogn Lett. 2019;118:14–22. https://doi.org/10.1016/j.patrec.2018.05.018
. Alicia. ”Filosofi Motif Batik Sebagai Identitas Bangsa Indonesia”. Jurnal FOLIO Volume 1 No. 1 Februari 2020.
. Wahyudi setiawan. “Perbandingan Arsitektur Convolutional Neural Network Untuk Klasifikasi Fundus”. Jurnal SIMATEC, Vol. 7, No. 2, Juni 2019, ISSN 2088-2130. https://doi.org/10.21107/simantec.v7i2.6551
. Hirahara D, Takaya E, Takahara T, Ueda T. “Efects of data count and image scaling on deep learning training”. PeerJ Comput Sci. 2020;6:312. https://doi.org/10.7717/peerj-cs.312
. Jiao L, Zhang F, Liu F, Yang S, Li L, Feng Z, Qu R. “A survey of deep learning‑based object detection”. IEEE Access. 2019;7:128837–68. https://doi.org/10.48550/arXiv.1907.09408
. I Wayan Suartika E. P. “Klasifikasi Citra Menggunakan ConvolutionalNeural Network (Cnn) pada Caltech 101”, JURNAL TEKNIK ITS Vol. 5, No. 1, (2016) ISSN: 2337-3539.
. Shorten C, Khoshgoftaar TM, Furht B. “Deep learning applications for COVID‑19”. J Big Data. 2021;8(1):1–54. https://doi.org/10.1186/s40537-020-00392-9
. Zhou DX. “Theory of deep convolutional neural networks: downsampling”. Neural Netw. 2020;124:319–27. https://doi.org/10.1016/j.neunet.2020.01.018