APPLICATION OF CANNY OPERATOR IN BATIK MOTIF IMAGE CLASSIFICATION WITH CONVOLUTIONAL NEURAL NETWORK APPROACH

Iwan Jaya Bakti; Nirwana Hendrastuty

doi:10.52436/1.jutif.2024.5.3.1894

Iwan Jaya Bakti Informatics, Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia, Indonesia
Nirwana Hendrastuty Informatics, Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.3.1894

Keywords: Batik Motif, Canny Operator, Classification, Convolutional Neural Network, DenseNet121

Abstract

Batik, as Indonesia's cultural heritage, has high artistic value and has a variety of unique motifs.. The main focus of this research is to solve the problem of the complexity and diversity of motifs found in Indonesian batik culture. The Canny operator is used as a first step to extract the edges of batik motifs, with the aim of improving the quality of feature extraction before entering the classification stage using CNN, specifically by using the DenseNet121 model. The dataset of this study was obtained through the Kaggle platform, published by Dionisius Darryl Hermansyah. The platform consists of 983 images (.jpg) with 20 different Indonesian batik motifs. Pre-processing includes the use of Canny for edge detection and data augmentation to increase the diversity of the dataset. Next, variations in the number of epochs and batch size were used to train the model. The results show that in the first test, the use of the Canny operation gives a higher confidence level in the model. In the model with Canny, there is a 1.6% increase in accuracy (33.57% with Canny and 31.97% without Canny). In addition, there are differences in the level of confidence in some batik classes. For example, the "batik mega mendung" class shows an increase in confidence of 66.57% with Canny (88.53% with Canny and 21.96% without Canny), while the "batik sekar" class shows a decrease in confidence of 12.09% with Canny.

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References

S. Saifulloh, H. A. Mumtahana, and M. W. Astuti, “Pelatihan Design Motif Batik Berbasis Kearifan Lokal Di Kelompok Batik Bogo Desa Kediren Kecamatan Lembeyan Kabupaten Magetan,” Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat, vol. 2, no. 1, pp. 13–21, Jan. 2022, doi: 10.25008/altifani.v2i1.193.

G. P. Kurniawan, H. Almash, and A. Shofwana, “Analisis Makna Filosofis Motif Batik Ponorogo Sebagai Upaya Penanaman Pendidikan Karakter,” Attractive : Innovative Education Journal, vol. 3, no. 2, 2021, [Online]. Available: https://www.attractivejournal.com/index.php/aj/

S. S. Yadav and S. M. Jadhav, “Deep convolutional neural network based medical image classification for disease diagnosis,” J Big Data, vol. 6, no. 1, Dec. 2019, doi: 10.1186/s40537-019-0276-2.

M. Shafaey, M. ElBery, M. Salem, H. Moushier, E.-S. A. El-Dahshan, and M. Tolba, “HYPERSPECTRAL IMAGE ANALYSIS USING A CUSTOM SPECTRAL CONVOLUTIONAL NEURAL NETWORK,” International Journal of Intelligent Computing and Information Sciences, vol. 22, no. 4, pp. 146–158, Dec. 2022, doi: 10.21608/ijicis.2022.147175.1198.

Y. Liu et al., “Richer Convolutional Features for Edge Detection,” IEEE Trans Pattern Anal Mach Intell, vol. 41, no. 8, pp. 1939–1946, Aug. 2019, doi: 10.1109/TPAMI.2018.2878849.

S. T. H. Kieu, A. Bade, and M. H. A. Hijazi, “Modified canny edge detection technique for identifying endpoints,” in Journal of Physics: Conference Series, Institute of Physics, 2022. doi: 10.1088/1742-6596/2314/1/012023.

M. He, R. Zhang, S. Liu, Y. Tan, and Y. Zeng, “Ultrasonic Image Diagnosis of Liver and Spleen Injury Based on a Double-Channel Convolutional Neural Network,” Wirel Commun Mob Comput, vol. 2021, 2021, doi: 10.1155/2021/2827011.

D. Gede, T. Meranggi, N. Yudistira, and Y. A. Sari, “INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION journal homepage : www.joiv.org/index.php/joiv INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION Batik Classification Using Convolutional Neural Network with Data Improvements.” [Online]. Available: www.joiv.org/index.php/joiv

S. Widiyanto, D. Sundani, Y. Karyanti, and D. T. Wardani, “Edge Detection Based on Quantum Canny Enhancement for Medical Imaging,” in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, 2019. doi: 10.1088/1757-899X/536/1/012118.

E. A. Sekehravani, E. Babulak, and M. Masoodi, “Implementing canny edge detection algorithm for noisy image,” Bulletin of Electrical Engineering and Informatics, vol. 9, no. 4, pp. 1404–1410, Aug. 2020, doi: 10.11591/eei.v9i4.1837.

Amity University. Dubai Campus, Amity University. Dubai Campus. IEEE Student Branch, Institute of Electrical and Electronics Engineers. UAE Section, and Institute of Electrical and Electronics Engineers, Proceedings of 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE’ 2019) : 11--12 December 2019, venue: Amity University Dubai, UAE.

J. N. Uy and J. F. Villaverde, “A Durian Variety Identifier Using Canny Edge and CNN,” in 2021 7th International Conference on Control Science and Systems Engineering, ICCSSE 2021, Institute of Electrical and Electronics Engineers Inc., Jul. 2021, pp. 293–297. doi: 10.1109/ICCSSE52761.2021.9545195.

A. Sasikumar, M. Sathyanarayanan, A. N. Sriayapppan, R. Santhosh, and R. Reshma, “A CNN-based Canny Edge Detection Approach for Car Scratch Detection,” in 6th International Conference on Inventive Computation Technologies, ICICT 2023 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 404–409. doi: 10.1109/ICICT57646.2023.10134444.

H. T. Gorji and N. Kaabouch, “A deep learning approach for diagnosis of mild cognitive impairment based on mri images,” Brain Sci, vol. 9, no. 9, Sep. 2019, doi: 10.3390/brainsci9090217.

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.

A. Baloch, T. D Memon, F. Memon, B. Lal, V. Viyas, and T. Jan, “Hardware Synthesize and Performance Analysis of Intelligent Transportation Using Canny Edge Detection Algorithm,” International Journal of Engineering and Manufacturing, vol. 11, no. 4, pp. 22–32, Aug. 2021, doi: 10.5815/ijem.2021.04.03.

C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J Big Data, vol. 6, no. 1, Dec. 2019, doi: 10.1186/s40537-019-0197-0.

Y. D. Zhang, S. C. Satapathy, X. Zhang, and S. H. Wang, “COVID-19 Diagnosis via DenseNet and Optimization of Transfer Learning Setting,” Cognit Comput, 2021, doi: 10.1007/s12559-020-09776-8.

A. Imam Rauyani, M. Hamka Ibrahim, and S. Pramono, “ROI based Indonesian Paper Currency Recognition Using Canny Edge Detection.”

S. H. Noh, “Performance comparison of CNN models using gradient flow analysis,” Informatics, vol. 8, no. 3, Sep. 2021, doi: 10.3390/informatics8030053.

H. Alshazly, C. Linse, M. Abdalla, E. Barth, and T. Martinetz, “COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans,” PeerJ Comput Sci, vol. 7, pp. 1–40, 2021, doi: 10.7717/peerj-cs.655.

L. Lv, M. Peng, X. Wang, and Y. Wu, “Multi-scale information fusion network with label smoothing strategy for corneal ulcer classification in slit lamp images,” Front Neurosci, vol. 16, Nov. 2022, doi: 10.3389/fnins.2022.993234.

K. Sanjar, A. Rehman, A. Paul, and K. Jeonghong, “Weight dropout for preventing neural networks from overfitting,” in 2020 8th International Conference on Orange Technology, ICOT 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020. doi: 10.1109/ICOT51877.2020.9468799.

N. ALHAWAS and Z. TÜFEKCİ, “The Effectiveness of Transfer Learning and Fine-Tuning Approach for Automated Mango Variety Classification,” European Journal of Science and Technology, Mar. 2022, doi: 10.31590/ejosat.1082217.

A. A. Kasim, J. Jurusan, I. Komputer, and D. Elektronika, “Klasifikasi Citra Batik Menggunakan Jaringan Syaraf Tiruan Berdasarkan Gray Level Co-Occurrence Matrices (GLCM) Agus Harjoko.”

A. P. A. Masa and H. Hamdani, “Klasifikasi Motif Citra Batik Menggunakan Convolutional Neural Network Berdasarkan K-means Clustering,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 5, no. 4, p. 1292, Oct. 2021, doi: 10.30865/mib.v5i4.3246.