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Performance Comparison of Machine Learning Algorithms for Ikat Weaving Classification
Corresponding Author(s) : Moch. Sjamsul Hidajat
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 10, No. 1, February 2025
Abstract
Ikat weaving is a rich traditional heritage of Kota Kediri, Indonesia, with a diverse array of intricate motifs that reflect the cultural richness of the region. As new motifs emerge and information about older designs fades, manual identification becomes time-consuming and difficult. This study leverages machine learning technology, specifically XGBoost, Random Forest, and Neural Network algorithms, to automate the classification of these weaving patterns. The dataset consisted of 600 images, split into 480 images (80%) for training and 120 images (20%) for testing, representing four distinct weaving motifs: "Gumul Weaving, Bolleches Weaving, Kuda Kepang Weaving, and Sekar Jagad Weaving." The study achieves high accuracy, with precision, recall, and F1-score all reaching 100%, underscoring its potential to not only improve the efficiency of motif identification, but also play a crucial role in preserving and promoting Indonesia's cultural heritage. Future research should focus on further optimizing these algorithms and expanding datasets to capture a broader range of ikat motifs. Additionally, enhancing the application of this model can contribute to a deeper understanding and broader appreciation of Kota Kediri’s cultural wealth through digital platforms.
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A. Pratiwi and A. Fauzi, “Implementation of Deep Learning on Flower Classification Using CNN Method,” Jurnal Teknik Informatika (JUTIF), vol. 5, no. 2, pp. 487–495, 2024. https://doi.org/10.52436/1.jutif.2024.5.2.1674
Y. Rizki, R. Medikawati Taufiq, H. Mukhtar, and D. Putri, “Klasifikasi Pola Kain Tenun Melayu Menggunakan Faster R-CNN,” IT Journal Research and Development, vol. 5, no. 2, pp. 215–225, Jan. 2021. https://doi.org/10.25299/itjrd.2021.vol5(2).5831
F. Charli, H. Syaputra, M. Akbar3, S. Sauda, and F. Panjaitan, “Implementasi Metode Faster Region Convolutional Neural Network (Faster R-CNN) Untuk Pengenalan Jenis Burung Lovebird,” 2020. https://doi.org/10.51519/journalita.volume1.isssue3.year2020.page185-197
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V. Sathananthavathi, K. Ganesh Kumar, and M. Sathish Kumar, “Secure visual communication with advanced cryptographic and ımage processing techniques,” Multimed Tools Appl, 2023. https://doi.org/10.1007/s11042-023-17224-6
Z. Azouz, B. Honarvar Shakibaei Asli, and M. Khan, “Evolution of Crack Analysis in Structures Using Image Processing Technique: A Review,” Electronics (Basel), vol. 12, no. 18, p. 3862, Sep. 2023. https://doi.org/10.3390/electronics12183862
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J. Ou et al., “Coupling UAV Hyperspectral and LiDAR Data for Mangrove Classification Using XGBoost in China’s Pinglu Canal Estuary,” Forests, vol. 14, no. 9, p. 1838, Sep. 2023. https://doi.org/10.3390/f14091838
M. A. Rasyidi, T. Bariyah, Y. I. Riskajaya, and A. D. Septyani, “Classification of handwritten javanese script using random forest algorithm,” Bulletin of Electrical Engineering and Informatics, vol. 10, no. 3, pp. 1308–1315, Jun. 2021. https://doi.org/10.11591/eei.v10i3.3036
E. H. Rachmawanto, D. R. I. M. Setiadi, N. Rijati, A. Susanto, I. U. W. Mulyono, and H. Rahmalan, “Attribute Selection Analysis for the Random Forest Classification in Unbalanced Diabetes Dataset,” in 2021 International Seminar on Application for Technology of Information and Communication (iSemantic), 2021, pp. 82–86. https://doi.org/10.1109/iSemantic52711.2021.9573181
M. Daviran, M. Shamekhi, R. Ghezelbash, and A. Maghsoudi, “Landslide susceptibility prediction using artificial neural networks, SVMs and random forest: hyperparameters tuning by genetic optimization algorithm,” International Journal of Environmental Science and Technology, vol. 20, no. 1, pp. 259–276, Jan. 2023. https://doi.org/10.1007/s13762-022-04491-3
M. Ferriol-Galmés et al., “Building a Digital Twin for network optimization using Graph Neural Networks,” Computer Networks, vol. 217, Nov. 2022. https://doi.org/10.1016/j.comnet.2022.109329
A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, “A survey of the recent architectures of deep convolutional neural networks,” Artif Intell Rev, vol. 53, no. 8, pp. 5455–5516, Dec. 2020. https://doi.org/10.1007/s10462-020-09825-6
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