Classification of Riau Batik Motifs Using the Convolutional Neural Network (CNN) Algorithm

Dhea Amanda  Ramadhan; Dian  Ramadhani

doi:10.31258/ijeepse.7.3.201-211

Dhea Amanda Ramadhan Universitas Riau, Indonesia
Dian Ramadhani Universitas Riau, Indonesia

DOI: https://doi.org/10.31258/ijeepse.7.3.201-211

Abstract viewed: 120 times

pdf downloaded: 38 times

Keywords: Classification, CNN, Deep learning, Riau Batik.

Abstract

Riau Batik, a treasured cultural heritage, faces challenges in its preservation due to limited public awareness of its unique motifs. This research aims to bridge the knowledge gap by developing a website-based classification system that can identify and recognize Riau batik patterns, offering round-the-clock accessibility to users. By leveraging the Convolutional Neural Network (CNN) algorithm, the classification system was trained using a dataset of 1,440 images. The model was fine-tuned through optimization of batch size and epoch parameters to maximize classification accuracy. The training process culminated in a model with an accuracy of 89%, achieved using a batch size of 16 and 50 epochs. This system seeks to elevate public appreciation and knowledge of Riau Batik, thereby contributing to the preservation of its cultural and historical significance. The accessible classification tool presents a practical approach to ensuring the motifs and legacy of Riau Batik are preserved for future generations. The proposed CNN-based model demonstrates the potential to enhance digital engagement with traditional culture through modern technology, facilitating widespread recognition and appreciation of Riau's rich batik heritage.

References

UNESCO, "Indonesia's Batik as Intangible Cultural Heritage," 2009.

Survey on Riau Batik Awareness, "Public Awareness of Riau Batik Motifs," 2023.

Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, pp. 436-444, 2015.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Advances in Neural Information Processing Systems (NIPS), vol. 25, pp. 1097-1105, 2012.

R. Yamashita, M. Nishio, R. K. Do, and K. Togashi, "Convolutional neural networks: an overview and application in radiology," Insights into Imaging, vol. 9, pp. 611-629, 2018.

Firman et al., "Classification of West Java Batik Motifs using Convolutional Neural Network," 2023.

Tungki Ari et al., "Application of the Convolutional Neural Network Algorithm for Solo Batik Motif Image Classification," in Proceedings of the Indonesian National Conference on Batik Technology, 2020.

H. Fonda, Y. Irawan, and A. Febriani, "Riau Batik Classification Study," 2020.

Romi et al., "SVM Algorithm for Banten Batik Classification," Journal of Batik Technology, vol. X, pp. 85-95, 2019.

M. Deni et al., "KNN Algorithm for Javanese Batik Classification," Indonesian Journal of Batik Technology, vol. 7, no. 3, pp. 120-130, 2019.

P. Supriono, Encyclopedia of the Heritage of Batik: The Identity that Unites the Nation's Pride. Yogyakarta: CV Andi Offset (Andi Publisher), 2016.

R. F. Alya, M. Wibowo, and P. Paradise, "Classification of Batik Motif Using Transfer Learning on Convolutional Neural Network (CNN)," J. Tek. Inform., vol. 4, no. 1, pp. 161–170, 2023.

L. Utari and A. Zulfikar, "Penerapan Convolutional Neural Networks Menggunakan Edge Detection Untuk Identifikasi Motif Jenis Batik," TeknoIS: Jurnal Ilmiah Teknologi Informasi dan Sains, vol. 13, no. 1, pp. 110–123, 2023.

K. Azmi, S. Defit, and S. Sumijan, "Implementation of Convolutional Neural Network (CNN) for the Classification of West Sumatra Clay Batik," J. Unitek, vol. 16, no. 1, pp. 28–40, 2023.

A. R. Syulistyo, D. S. Hormansyah, and P. Y. Saputra, "SIBI (Sistem Isyarat Bahasa Indonesia) translation using Convolutional Neural Network (CNN)," IOP Conference Series: Materials Science and Engineering, vol. 732, no. 1, 2020.

T. Bariyah, M. A. Rasyidi, and N. Ngatini, "Convolutional Neural Network for Multi-Label Classification Method on Batik Motifs," Techno.Com, vol. 20, no. 1, pp. 155–165, 2021.

F. Y. Tember, I. Najiyah, T. Informatics, and F. T. Information, "Classification of West Java Batik Motifs Using Convolutional Neural Network," vol. 12, pp. 282–292, 2023.

T. A. Bowo, H. Syaputra, and M. Akbar, "Application of Convolutional Neural Network Algorithm for Classification of Solo Batik Image Motifs," J. Softw. Eng. Ampera, vol. 1, no. 2, pp. 82–96, 2020.

H. Fonda, "Klasifikasi Batik Riau Dengan Menggunakan Convolutional Neural Networks (CNN)," Jurnal Ilmu Komputer, vol. 9, no. 1, pp. 7–10, 2020.

R. Wiryadinata, M. R. Adli, R. Fahrizal, and R. Alfanz, "Klasifikasi 12 Motif Batik Banten Menggunakan Support Vector Machine," J. EECCIS, vol. 13, no. 1, pp. 60–64, 2019.

S. D. Euis Oktavianti, A. Ichsan, and M. Laya, "Implementasi Algoritma Convolutional Neural Network Untuk Mendeteksi Pengguna Masker," Semin. Nas. Inov. VOKASI, vol. 2, no. 1, 2023.

N. Ibrahim et al., "Klasifikasi Tingkat Kematangan Pucuk Daun Teh menggunakan Metode Convolutional Neural Network," ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 10, no. 1, p. 162, 2022.

A. Anhar and R. A. Putra, "Perancangan dan Implementasi Self-Checkout System pada Toko Ritel menggunakan Convolutional Neural Network (CNN)," ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 11, no. 2, p. 466, 2023.

D. Ramadhani et al., "Analisa Algoritma Naïve Bayes Classifier (NBC) Untuk Prediksi Penjualan Alat Kesehatan," Indonesian Journal of Informatic Research and Software Engineering (IJIRSE), vol. 3, no. 2, pp. 119–126, 2023.