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  2. JUITA Vol. 11 No. 1, May 2023
  3. Fitriani

Image Classification On Garutan Batik Using Convolutional Neural Network with Data Augmentation

Leni Fitriani, Dewi Tresnawati, Muhammad Bagja Sukriyansah

Abstract


In Indonesia, Batik is one of the cultural assets in the field of textiles with various styles. There are many types of batik in Indonesia, one of which is Batik Garutan. Batik Garutan has different motifs that show the characteristics of Batik Garutan itself. Therefore, to distinguish the features of Batik Garutan from another batik, a system is needed to classify the types of batik patterns. Classification of batik patterns can be done using image classification. In image classification, there are methods to increase the size and quality of the limited training dataset by performing data augmentation. This study aims to obtain an image classification model by applying data augmentation. The image classification process is carried out using the Deep Learning method with the Convolutional Neural Network algorithm, which is expected to be helpful as a reference for research and can be applied to software development related to image classification. This study generated models from several experiments with different epoch parameters and dataset proportions. A system obtained the investigation with the best performance with a data proportion of 9:1, resulting in an accuracy value of 91 percent.

Keywords


Batik Garutan; CNN; Data Augmentation; Image Classification

References


[1] S. Suhartini, N. A. Mahbubah, and M. Basjir, “MARKETING STRATEGY DESIGN BASED ON INFORMATION TECHNOLOGY IN BATIK SMALL AND MEDIUM-SIZED ENTERPRISES IN INDONESIA,” Eastern-European J. Enterp. Technol., vol. 6, no. 13–114, pp. 39–48, 2021, doi: 10.15587/1729-4061.2021.244137.

[2] Y. Y. Sunarya and B. Anas, “The priangan batik in the constellation of modern aesthetics,” J. Vis. Art Des., vol. 6, no. 1, pp. 9–16, Jan. 2014, doi: 10.5614/itbj.vad.2014.6.1.2.

[3] J. Qin, W. Pan, X. Xiang, Y. Tan, and G. Hou, “A biological image classification method based on improved CNN,” Ecol. Inform., vol. 58, p. 101093, 2020, doi: 10.1016/J.ECOINF.2020.101093.

[4] G. Zhou, W. Zhang, A. Chen, M. He, and X. Ma, “Rapid detection of rice disease based on FCM-KM and faster R-CNN fusion,” IEEE access, vol. 7, pp. 143190–143206, 2019, doi: 10.1109/ACCESS.2019.2943454.

[5] X. Lei, H. Pan, and X. Huang, “A dilated CNN model for image classification,” IEEE Access, vol. 7, pp. 124087–124095, 2019, doi: 10.1109/ACCESS.2019.2927169.

[6] S. Arya and R. Singh, “A Comparative Study of CNN and AlexNet for Detection of Disease in Potato and Mango leaf,” 2019 Int. Conf. Issues Challenges Intell. Comput. Tech., vol. 1, pp. 1–6, 2019, doi: 10.1109/ICICT46931.2019.8977648.

[7] S. Baranwal, S. Khandelwal, and A. Arora, “Deep learning convolutional neural network for apple leaves disease detection,” Proc. Int. Conf. Sustain. Comput. Sci. Technol. Manag. (SUSCOM), Amity Univ. Rajasthan, Jaipur-India., 2019, doi: 10.2139/ssrn.3351641.

[8] A. Ramcharan, K. Baranowski, P. McCloskey, J. Ahmed, B., Legg, and D. P. Hughes, “Deep learning for image-based cassava disease detection,” Front. Plant Sci., vol. 8, p. 1852, 2017, doi: 10.3389/fpls.2017.01852.

[9] J. Chaki and N. Dey, A beginner’s guide to image shape feature extraction techniques. CRC Press Taylor & Francis Group, 2019.

[10] L. Perez and J. Wang, “The effectiveness of data augmentation in image classification using deep learning,” arXiv Prepr. arXiv1712.04621, 2017, doi: 10.48550/arXiv.1712.04621.

[11] M. A. Khan, T. Akram, M. Sharif, K. Javed, M. Raza, and T. Saba, “An automated system for cucumber leaf diseased spot detection and classification using improved saliency method and deep features selection,” Multimed. Tools Appl., vol. 79, no. 25–26, pp. 18627–18656, Jul. 2020, doi: 10.1007/S11042-020-08726-8.

[12] M. Kashif et al., “Detection of viruses in sweetpotato from Honduras and Guatemala augmented by deep-sequencing of small-RNAs,” Plant Dis., vol. 96, no. 10, pp. 1430–1437, 2012, doi: 10.1094/PDIS-03-12-0268-RE.

[13] C. Uswatun Khasanah, E. Utami, and S. Raharjo, “Implementation of Data Augmentation Using Convolutional Neural Network for Batik Classification,” in In 2020 8th International Conference on Cyber and IT Service Management (CITSM), Oct. 2020, pp. 1–5, doi: 10.1109/CITSM50537.2020.9268890.

[14] M. A. Rasyidi and T. Bariyah, “Batik pattern recognition using convolutional neural network,” Bull. Electr. Eng. Informatics, vol. 9, no. 4, pp. 1430–1437, Aug. 2020, doi: 10.11591/eei.v9i4.2385.

[15] D. G. T. Meranggi, N. Yudistira, and Y. A. Sari, “Batik Classification Using Convolutional Neural Network with Data Improvements,” JOIV Int. J. Informatics Vis., vol. 6, no. 1, pp. 6–11, Mar. 2022, doi: 10.30630/joiv.6.1.716.

[16] I. M. A. Agastya and A. Setyanto, “Classification of Indonesian batik using deep learning techniques and data augmentation,” in In 2018 3rd international conference on information technology, information system and electrical engineering (ICITISEE), Nov. 2018, pp. 27–31, doi: 10.1109/ICITISEE.2018.8720990.

[17] M. A. Rasyidi, R. Handayani, and F. Aziz, “Identification of batik making method from images using convolutional neural network with limited amount of data,” Bull. Electr. Eng. Informatics, vol. 10, no. 3, pp. 1300–1307, Jun. 2021, doi: 10.11591/eei.v10i3.3035.

[18] B. Pasaribu, Y. Latief, and S. Riantini L, “Development of Risk-Based Standardized Work Breakdown Structure (WBS) to Improve Quality Planning of Drainage Construction Work Development of Risk-Based Standardized Work Breakdown Structure (WBS) to,” IOP Conf. Ser. Earth Environ. Sci., vol. 258, no. 1, p. 12054, 2019, doi: 10.1088/1755-1315/258/1/012054.

[19] E. Siami-Irdemoosa, S. R. Dindarloo, and M. Sharifzadeh, “Work breakdown structure ({WBS}) development for underground construction,” Autom. Constr., vol. 58, pp. 85–94, 2015, doi: 10.1016/j.autcon.2015.07.016.

[20] R. C. Tausworthe, “The work breakdown structure in software project management,” J. Syst. Softw., vol. 1, pp. 181–186, 1979, doi: 10.1016/0164-1212(79)90018-9.

[21] L. Fitriani, P. Hakim, and R. M. Al Haq, “‪E-Commerce For Village Information System Using Agile Methodology,” J. Online Inform., vol. 7, no. 1, pp. 89–96, 2022, doi: 10.15575/join.v7i1.825.

[22] L. Fitriani, S. Dianti, D. Kurniadi, A. Mulyani, and R. Setiawan, “Mapping-Based Using Geographic Information Systems for Smart Transportation,” in in 2021 International Conference on ICT for Smart Society (ICISS), 2021, pp. 1–5, doi: 10.1109/ICISS53185.2021.9533202.

[23] D. I. Mulyana and V. V. Pramansah, “Gender Classification for Anime Character Face Image Using Random Forest Classifier Method and GLCM Feature Extraction,” JUITA J. Inform., vol. 10, no. 2, pp. 243–250, 2022, doi: 10.30595/juita.v10i2.13833.


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DOI: 10.30595/juita.v11i1.16166

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ISSN: 2579-8901