Classification of Lombok Songket and Sasambo Batik Motifs Using the Convolution Neural Network (CNN) Algorithm

Suthami Ariessaputra - University of Mataram, Mataram, 83125, Indonesia
Viviana Vidiasari - University of Mataram, Mataram, 83125, Indonesia
Sudi Sasongko - University of Mataram, Mataram, 83125, Indonesia
Budi Darmawan - University of Mataram, Mataram, 83125, Indonesia
Sabar Nababan - University of Mataram, Mataram, 83125, Indonesia

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Sasambo batik is a traditional batik from the West Nusa Tenggara province. Sasambo itself is an abbreviation of three tribes, namely the Sasak (sa) in the Lombok Islands, the Samawa (sam), and the Mbojo (bo) tribes in Sumbawa Island. Classification of batik motifs can use image processing technology, one of which is the Convolution Neural Network (CNN) algorithm. Before entering the classification process, the batik image first undergoes image resizing. After that, proceed with the operation of the convolution, pooling, and fully connected layers. The sample image of Lombok songket motifs and Sasambo batik consists of 20 songket fabric data with the same motif and color and 14 songket data with the same motif but different colors. In addition, there are 10 data points on songket fabrics with other motifs and colors. In addition, there are 5 data points on Sasambo batik fabrics with the same motif and color and 5 data points on Sasambo batik fabrics with the same motif but different colors. The training data rotates the image by 150 as many as 20 photos. Testing with motifs with the same color shows that the system's success rate is 83.85%. The highest average recognition for Sasambo batik cloth is in testing motifs with the same color for data in the database at 93.66%. The CNN modeling classification results indicate that the Sasambo batik cloth can be a reference for developing songket categorization using a website platform or the Android system.


Songket; Batik; Convolution Neural Network (CNN); neural network; Sasambo.

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A. A. Mohamad Morni, M. A. Samin, and R. Legino, “Floral Motifs Design on Sarawak Traditional Malay Songket,” Environment-Behaviour Proceedings Journal, vol. 6, no. SI4, 2021, doi: 10.21834/ebpj.v6isi4.2902.

S. M. Al Sasongko, E. D. Jayanti, and S. Ariessaputra, “Application of Gray Scale Matrix Technique for Identification of Lombok Songket Patterns Based on Backpropagation Learning,” International Journal on Informatics Visualization, vol. 6, no. 4, 2022, doi: 10.30630/joiv.6.4.1532.

M. Imaniar, J. Sutarto, and S. Edy Mulyono, “Songket Fabric Weaving Training in Empowering Poor Women at Home Industry,” Journal of Nonformal Education, vol. 6, no. 1, 2020, doi: 10.15294/jne.v6i1.20619.

A. Hakim, J. Jamaluddin, S. W. Al Idrus, and M. E. P. Ramandha, “The Use of Sasambo Culture in Learning Natural Product Chemistry to Support Traditional Health Tourism in Lombok and Sumbawa Islands,” Jurnal Penelitian Pendidikan IPA, vol. 6, no. 2, 2020, doi: 10.29303/jppipa.v6i2.435.

S. Ernawati, M. Muhajirin, and I. Ismunandar, “The Effect of Word of Mouth (WOM) on Purchasing Decision of Region Exclusive Fabric of West Nusa Tenggara Province (Case Study on Sasambo Fabric in Bima City),” Jurnal Terapan Manajemen Dan Bisnis, vol. 4, no. 2, 2018, doi: 10.26737/jtmb.v4i2.823.

I. Indra, Ismatul Maula, and Irlina Dewi, “The Values of Islamic Education in Use of Songket for Male and Female,” Lakhomi Journal Scientific Journal of Culture, vol. 2, no. 1, 2021, doi: 10.33258/lakhomi.v2i1.420.

P. H. P. Humairoh and G. W. Nurcahyo, “Decision Support Systems in Identifying Silungkang Songket Motifs Using the AHP Method,” Jurnal Sistim Informasi dan Teknologi, 2020, doi: 10.37034/jsisfotek.v3i1.86.

E. Tjahjaningsih, A. B. Santosa, A. P. Utomo, and U. S. Semarang, “Creative Techniques of Contemporary Batik Motifs,” The International Journal of Organizational Innovation, vol. 12, no. 3, 2020.

J. Wang, Y. Zheng, M. Wang, Q. Shen, and J. Huang, “Object-Scale Adaptive Convolutional Neural Networks for High-Spatial Resolution Remote Sensing Image Classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, 2021, doi: 10.1109/JSTARS.2020.3041859.

D. Xue et al., “An Application of Transfer Learning and Ensemble Learning Techniques for Cervical Histopathology Image Classification,” IEEE Access, vol. 8, 2020, doi: 10.1109/ACCESS.2020.2999816.

Y. Pei, Y. Huang, Q. Zou, X. Zhang, and S. Wang, “Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 4, 2021, doi: 10.1109/TPAMI.2019.2950923.

M. Muhathir, M. H. Santoso, and D. A. Larasati, “Wayang Image Classification Using SVM Method and GLCM Feature Extraction,” Journal of Informatics and Telecommunication Engineering, vol. 4, no. 2, 2021, doi: 10.31289/jite.v4i2.4524.

N. A. Wati, W. Pertiwi, Florensia, D. D. P. A, S. R. Atthariq, and Sukenda, “Classification of Batik Keraton and Pesisir Imagery Using Convolutional Neural Network,” Review of International Geographical Education Online, vol. 11, no. 3, 2021.

B. Imran and M. M. Efendi, “The Implementation of Extraction Feature using GLCM And Back-Propagation Artificial Neural Network to Clasify Lombok Songket Woven Cloth,” Jurnal Techno Nusa Mandiri, vol. 17, no. 2, 2020, doi: 10.33480/techno.v17i2.1680.

A. E. Minarno, F. D. S. Sumadi, H. Wibowo, and Y. Munarko, “Classification of batik patterns using K-nearest neighbor and support vector machine,” Bulletin of Electrical Engineering and Informatics, vol. 9, no. 3, 2020, doi: 10.11591/eei.v9i3.1971.

A. E. Minarno, A. S. Maulani, A. Kurniawardhani, F. Bimantoro, and N. Suciati, “Comparison of methods for Batik classification using multi texton histogram,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 16, no. 3, 2018, doi: 10.12928/TELKOMNIKA.v16i3.7376.

H. Hambali, M. Mahayadi, and ..., “Classification of Lombok Songket Cloth Image Using Convolution Neural Network Method (Cnn),” Pilar Nusa Mandiri …, no. 85, pp. 149–156, 2021, doi: 10.33480/pilar.v17i2.2705.

D. Hardiyanto, S. Kristiyana, D. Kurniawan, and D. A. Sartika, “Klasifikasi Motif Citra Batik Yogyakarta Menggunakan Metode Adaptive Neuro Fuzzy Inference System,” Setrum : Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer, vol. 8, no. 2, 2019, doi: 10.36055/setrum.v8i2.6545.

D. Trimakno and Kusrini, “Impact of Augmentation on Batik Classification using Convolution Neural Network and K-Neareast Neighbor,” in ICOIACT 2021 - 4th International Conference on Information and Communications Technology: The Role of AI in Health and Social Revolution in Turbulence Era, 2021. doi: 10.1109/ICOIACT53268.2021.9564000.

F. A. Putra et al., “Classification of Batik Authenticity Using Convolutional Neural Network Algorithm with Transfer Learning Method,” in 2021 6th International Conference on Informatics and Computing, ICIC 2021, 2021. doi: 10.1109/ICIC54025.2021.9632937.

Yuhandri, S. Madenda, E. P. Wibowo, and Karmilasari, “Pattern recognition and classification using backpropagation neural network algorithm for songket motifs image retrieval,” International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 6, 2017, doi: 10.18517/ijaseit.7.6.2200.

O. N. Oyelade, A. E. S. Ezugwu, T. I. A. Mohamed, and L. Abualigah, “Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm,” IEEE Access, vol. 10, 2022, doi: 10.1109/ACCESS.2022.3147821.

Z. Wu et al., “Studies on Different CNN Algorithms for Face Skin Disease Classification Based on Clinical Images,” IEEE Access, vol. 7, 2019, doi: 10.1109/ACCESS.2019.2918221.

A. Basher, B. C. Kim, K. H. Lee, and H. Y. Jung, “Volumetric Feature-Based Alzheimer’s Disease Diagnosis from sMRI Data Using a Convolutional Neural Network and a Deep Neural Network,” IEEE Access, vol. 9, 2021, doi: 10.1109/ACCESS.2021.3059658.

P. Sivakumar, N. Sri Ram Mohan, P. Kavya, and P. Vinay Sai Teja, “Leaf Disease Identification: Enhanced Cotton Leaf Disease Identification Using Deep CNN Models,” in Proceedings - 2021 IEEE International Conference on Intelligent Systems, Smart and Green Technologies, ICISSGT 2021, 2021. doi: 10.1109/ICISSGT52025.2021.00016.

T. X. Hui, S. Kasim, M. F. M. Fudzee, Z. Abdullah, R. Hassan, and A. Erianda, “A Microarray Data Pre-processing Method for Cancer Classification,” International Journal on Informatics Visualization, vol. 6, no. 4, 2022, doi: 10.30630/joiv.6.4.1523.

B. Khagi and G. R. Kwon, “3D CNN based alzheimer’s diseases classification using segmented grey matter extracted from whole-brain MRI,” International Journal on Informatics Visualization, vol. 5, no. 2, 2021, doi: 10.30630/joiv.5.2.572.

Norhikmah, A. Lutfhi, and Rumini, “The Effect of Layer Batch Normalization and Dropout of CNN model Performance on Facial Expression Classification,” International Journal on Informatics Visualization, vol. 6, no. 2–2, pp. 481–488, 2022, doi: 10.30630/joiv.6.2-2.921.

H. H. Chen, B. J. Hwang, J. S. Wu, and P. T. Liu, “The effect of different deep network architectures upon CNN-based gaze tracking,” Algorithms, vol. 13, no. 5, 2020, doi: 10.3390/A13050127.

L. R. Yee, H. Kamaludin, N. Z. M. Safar, N. Wahid, N. Abdullah, and D. Meidelfi, “Intelligence Eye for Blinds and Visually Impaired by Using Region-Based Convolutional Neural Network (R-CNN),” International Journal on Informatics Visualization, vol. 5, no. 4, 2021, doi: 10.30630/JOIV.5.4.735.

J. Wu, J. Ma, F. Liang, W. Dong, G. Shi, and W. Lin, “End-to-End Blind Image Quality Prediction with Cascaded Deep Neural Network,” IEEE Transactions on Image Processing, vol. 29, 2020, doi: 10.1109/TIP.2020.3002478.

S. Park and T. Suh, “Speculative Backpropagation for CNN Parallel Training,” IEEE Access, vol. 8, 2020, doi: 10.1109/ACCESS.2020.3040849.

R. Aprianti, K. Evandari, R. A. Pramunendar, and M. Soeleman, “Comparison of Classification Method on Lombok Songket Woven Fabric Based on Histogram Feature,” in Proceedings - 2021 International Seminar on Application for Technology of Information and Communication: IT Opportunities and Creativities for Digital Innovation and Communication within Global Pandemic, iSemantic 2021, 2021. doi: 10.1109/iSemantic52711.2021.9573223.

Y. He, Z. Lu, J. Wang, S. Ying, and J. Shi, “A Self-Supervised Learning Based Channel Attention MLP-Mixer Network for Motor Imagery Decoding,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, 2022, doi: 10.1109/TNSRE.2022.3199363.

A. Jamali, M. Mahdianpari, F. Mohammadimanesh, B. Brisco, and B. Salehi, “3-D Hybrid CNN Combined With 3-D Generative Adversarial Network for Wetland Classification with Limited Training Data,” IEEE J Sel Top Appl Earth Obs Remote Sens, vol. 15, 2022, doi: 10.1109/JSTARS.2022.3206143.

Y. Sun, M. Wang, C. Wei, Y. Zhong, and J. Xiang, “Heterogeneous Spectral-Spatial Network with 3D Attention and MLP for Hyperspectral Image Classification Using Limited Training Samples,” IEEE J Sel Top Appl Earth Obs Remote Sens, 2023, doi: 10.1109/JSTARS.2023.3271901.

L. Zhang, Y. Zhu, H. Wu, and K. Li, “An Optimized Multisource Bilinear Convolutional Neural Network Model for Flame Image Identification of Coal Mine,” IEEE Access, vol. 10, 2022, doi: 10.1109/ACCESS.2022.3170464.

G. Yildiz, A. Ulu, B. Dizdaroglu, and D. Yildiz, “Hybrid Image Improving and CNN (HIICNN) Stacking Ensemble Method for Traffic Sign Recognition,” IEEE Access, vol. 11, 2023, doi: 10.1109/ACCESS.2023.3292955.

H. Samma, S. A. Suandi, N. A. Ismail, S. Sulaiman, and L. L. Ping, “Evolving Pre-Trained CNN Using Two-Layers Optimizer for Road Damage Detection from Drone Images,” IEEE Access, vol. 9, 2021, doi: 10.1109/ACCESS.2021.3131231.

D. Lambhate, R. Sharma, J. Clark, A. Gangopadhyay, and D. Subramani, “W-Net: A Deep Network for Simultaneous Identification of Gulf Stream and Rings from Concurrent Satellite Images of Sea Surface Temperature and Height,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, 2022, doi: 10.1109/TGRS.2021.3096202.

X. Yang, L. Zhang, and Z. Wu, “A Unified Convolutional Neural Network Classifier Aided Intelligent Channel Decoder for Coexistent Heterogeneous Networks,” IEEE Syst J, vol. 15, no. 4, 2021, doi: 10.1109/JSYST.2020.3040287.

X. Feng and L. Pu, “High-Performance Visual Tracking Based on High-Order Pooling Network,” IEEE Access, vol. 10, 2022, doi: 10.1109/ACCESS.2022.3208579.