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  3. Vol. 10, No. 1, February 2025
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Vol. 10, No. 1, February 2025

Issue Published : Feb 1, 2025
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Performance Comparison of Machine Learning Algorithms for Ikat Weaving Classification

https://doi.org/10.22219/kinetik.v10i1.2059
Moch. Sjamsul Hidajat
University of Dian Nuswantoro
Dibyo Adi Wibowo
University of Dian Nuswantoro
Ery Mintorini
University of Dian Nuswantoro

Corresponding Author(s) : Moch. Sjamsul Hidajat

moch.sjamsul.hidajat@dsn.dinus.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 10, No. 1, February 2025
Article Published : Feb 1, 2025

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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.

Keywords

Ikat Weaving Machine learning Neural Networks Random Forest XGBoost
Hidajat, M. S., Wibowo, D. A., & Mintorini, E. (2025). Performance Comparison of Machine Learning Algorithms for Ikat Weaving Classification. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 10(1). https://doi.org/10.22219/kinetik.v10i1.2059
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References
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References


A. A. Khan, A. A. Laghari, and S. A. Awan, “Machine Learning in Computer Vision: A Review,” EAI Endorsed Transactions on Scalable Information Systems, vol. 8, no. 32, pp. 1–11, 2021. https://doi.org/10.4108/eai.21-4-2021.169418

T. Sommerschield et al., “Machine Learning for Ancient Languages: A Survey,” Computational Linguistics, pp. 1–44, May 2023. https://doi.org/10.1162/coli_a_00481

M. Ahammed, M. Al Mamun, and M. S. Uddin, “A machine learning approach for skin disease detection and classification using image segmentation,” Healthcare Analytics, vol. 2, Nov. 2022. https://doi.org/10.1016/j.health.2022.100122

N. Chandrasekhar and S. Peddakrishna, “Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization,” Processes, vol. 11, no. 4, Apr. 2023. https://doi.org/10.3390/pr11041210

B. Arjmand et al., “Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer,” Jan. 27, 2022, Frontiers Media S.A. https://doi.org/10.3389/fgene.2022.824451

E. H. Rachmawanto and P. N. Andono, “Deteksi Karakter Hiragana Menggunakan Metode Convolutional Neural Network,” Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), vol. 11, no. 3, pp. 183–191, Dec. 2022. https://doi.org/10.23887/janapati.v11i3.50144

E. Z. Astuti, C. A. Sari, E. H. Rachmawanto, and R. R. Ali, “Comparative Study of Machine Learning Algorithms for Performing Ham or Spam Classification in SMS,” Scientific Journal of Informatics, vol. 11, no. 1, pp. 177–186, Feb. 2024. https://doi.org/10.15294/sji.v11i1.47364

I. P. Kamila, C. A. Sari, E. H. Rachmawanto, and N. R. D. Cahyo, “A Good Evaluation Based on Confusion Matrix for Lung Diseases Classification using Convolutional Neural Networks,” Advance Sustainable Science, Engineering and Technology, vol. 6, no. 1, p. 0240102, Dec. 2023. https://doi.org/10.26877/asset.v6i1.17330

A. G. Sooai and F. A. A. Dwiandri, “Pengenalan Citra Kain Tenun Nusa Tenggara Timur Menggunakan SqueezNet dan Decision Tree,” 2024. https://doi.org/10.24002/konstelasi.v4i1.9220

Y. R. Kaesmetan, “Identifikasi Citra Warna Pada Kain Tenun Lotis Timor Tengah Selatan (TTS) Menggunakan Metode Convolution Network (CNN): Penerapan Convolutional Neural Networks (CNN) untuk Identifikasi Warna dalam Kain Tenun Lotis Timor Tengah Selatan: Analisis Digital pada Warisan Budaya,” Jurnal Sistem Informasi dan Aplikasi (JSIA), vol. 2, no. 1, pp. 68–79, 2024. https://doi.org/10.52958/jsia.v2i1.7692

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

Mukhlis Santoso, Sarjon Defit, and Yuhandri, “Penerapan Convolutional Neural Network pada Klasifikasi Citra Pola Kain Tenun Melayu,” Jurnal CoSciTech (Computer Science and Information Technology), vol. 5, no. 1, pp. 177–184, May 2024. https://doi.org/10.37859/coscitech.v5i1.6713

R. Kumala, Y. Darmi, N. David Maria Veronika, and U. Muhammadiyah Bengkulu Bengkulu, “Klasifikasi Pola Motif Kain Tenun Bumpak Desa Kampai Seluma Menggunakan Metode CNNN,” Remik: Riset dan E-Jurnal Manajemen Informatika Komputer, vol. 7, no. 4, 2023. https://doi.org/10.33395/remik.v7i4.13008

T. Hendrawati et al., “JURNAL MEDIA INFORMATIKA BUDIDARMA Penerapan Deep Learning Dalam Pengenalan Endek Bali Menggunakan Convolutional Neural Network,” vol. 7, pp. 2118–2127, 2023. https://doi.org/10.30865/mib.v7i4.6721

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

A. Farzipour, R. Elmi, and H. Nasiri, “Detection of Monkeypox Cases Based on Symptoms Using XGBoost and Shapley Additive Explanations Methods,” Diagnostics, vol. 13, no. 14, p. 2391, Jul. 2023. https://doi.org/10.3390/diagnostics13142391

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

I. Fomin, V. Burin, and A. Bakhshiev, “Research on Neural Networks Integration for Object Classification in Video Analysis Systems,” in 2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), IEEE, May 2020, pp. 1–5. https://doi.org/10.1109/ICIEAM48468.2020.9112011

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KINETIK: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
eISSN : 2503-2267
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