Automated Recognition of Batik Aceh Patterns Using Machine Learning Techniques
DOI:
10.47709/brilliance.v4i2.4831Keywords:
Aceh batik patterns, Machine learning, CNN, EfficientnetDimension Badge Record
Abstract
This research focuses on the automatic recognition of Aceh batik patterns using machine learning techniques. Utilizing a Convolutional Neural Network (CNN) model based on EfficientNet, a dataset consisting of 1,200 Aceh batik images was processed through various stages, from data collection to model training and evaluation. The images are divided into three main classes: Bungong Jeumpa, Ceplok, and Kerawang. The data processing steps include normalization, resizing, and data augmentation to ensure better variation. The model was trained using 75% of the data as a training set and 25% as a testing set. The results indicate that the model performed excellently, achieving an accuracy rate of 98%. According to the classification report, the model achieved an average precision, recall, and F1-score of 0.98. The Kerawang category achieved the highest precision at 100%, while the Bungong Jeumpa and Ceplok categories had F1-scores of 0.98 and 0.97, respectively. These findings demonstrate the potential of machine learning methods in recognizing Aceh batik patterns with high accuracy, supporting the preservation of local culture through technology.
Abstract viewed = 27 times
References
Dahlia, P., Izzati, F., & Br Sembiring, S. (2023). Tradisi Peusijuk Sebagai Inspirasi Penciptaan Desain Motif Aceh Pada Media Batik. Gorga?: Jurnal Seni Rupa, 12(2), 590. doi:10.24114/gr.v12i2.50782
Fahcruroji, A. R., Madona Yunita Wijaya, & Irma Fauziah. (2024). Implementasi Algoritma Cnn Mobilenet Untuk Klasifikasi Gambar Sampah Di Bank Sampah. PROSISKO: Jurnal Pengembangan Riset Dan Observasi Sistem Komputer, 11(1), 45–51. doi:10.30656/prosisko.v11i1.8101
Gunawan, D., & Setiawan, H. (2022). Convolutional Neural Network dalam Citra Medis. KONSTELASI: Konvergensi Teknologi Dan Sistem Informasi, 2(2), 376–390. doi:10.24002/konstelasi.v2i2.5367
M. Muhathir, N. Khairina, R. Karenina Isabella Barus, M. Ula, and I. S. (2023). “Preserving Cultural Heritage Through AI: Developing LeNet Architecture for Wayang Image Classification”. (IJACSA) International Journal of Advanced Computer Science and Applications, 14(9).
M, L. (2024). Research on Textile Pattern Recognition Based on Artificial Intelligence. In 2024 IEEE 7th Eurasian Conference on Educational Innovation (ECEI), (pp. 335-338.).
Malika, M., & Widodo, E. (2022). IMMPLEMENTASI DEEP LEARNING UNTUK KLASIFIKASI GAMBAR MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) PADA BATIK SASAMBO. In Pattimura Proceeding: Conference of Science and Technology.
Novita, V. D., Haryono, N. A., & R, I. D. E. K. (2016). Klasifikasi Motif Batik Semen Berdasarkan Ekstraksi Polar Fourier Transform Dan K-Nearest Neighbour, (November), 263–268.
Potrimba, P. (2023). What is EfficientNet? The Ultimate Guide.
Rangkuti, A. H. (2014). Klasifikasi Motif Batik Berbasis Kemiripan Ciri dengan Wavelet Transform dan Fuzzy Neural Network. ComTech: Computer, Mathematics and Engineering Applications, 5(1), 361. doi:10.21512/comtech.v5i1.2630
Rizal, F., Hasyim, F., Malik, K., & Yudistira, Y. (2022). Implementasi Algoritma Convolutional Neural Networks (CNN) Untuk Klasifikasi Batik. COREAI: Jurnal Kecerdasan Buatan, Komputasi Dan Teknologi Informasi.
Sembiring, S., Fauziana Izzati, & Putri Dahlia. (2024). Analisis Semiotik Motif Peusijuk Pada Karya Batik Aceh. DESKOVI?: Art and Design Journal, 7(1), 66–70. doi:10.51804/deskovi.v7i1.16574
Sulistiyanti, Sri & Setyawan, Fx Arinto & Komarudin, M. (2016). PENGOLAHAN CITRA DASAR DAN CONTOH PENERAPANNYA.
Tan, M., & Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning (pp. 6105-6114. PMLR).
Tan, M. (2023). efficientnet-improving-accuracy-and-efficiency-through-automl-and-model-scaling. Retrieved 11 October 2024, from https://research.google/blog/efficientnet-improving-accuracy-and-efficiency-through-automl-and-model-scaling/
Wiryadinata, R., Adli, M. R., Fahrizal, R., & Alfanz, R. (2019). Klasifikasi 12 Motif Batik Banten Menggunakan Support Vector Machine. Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems), 13(1), 60–64. doi:https://doi.org/10.21776/jeeccis.v13i1.570
ZAMAN, B., & Khoirudin, K. (2021). Klasifikasi Citra Batik Menggunakan Co-Occurrence Matrices Berbasis Wavelet Filter. Jurnal Pengembangan Rekayasa Dan Teknologi, 5(2), 123. doi:10.26623/jprt.v17i2.4594
Downloads
ARTICLE Published HISTORY
How to Cite
Issue
Section
License
Copyright (c) 2024 Eka Utaminingsih, Ilham Sahputra
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.