[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125102-en":3,"doc-seo-125102-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125102,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",6,"Technology","Performance Comparison of Machine Learning Algorithms for Ikat Weaving Classification - Research Study","Ikat weaving is a rich traditional heritage of Kota Kediri, Indonesia, featuring intricate motifs whose identification becomes difficult as new designs appear and older information fades. This study applies machine learning to automate motif classification using XGBoost, Random Forest, and Neural Network models. A dataset of 600 images was split into 480 for training and 120 for testing, covering four motifs: Gumul, Bolleches, Kuda Kepang, and Sekar Jagad. The models reach perfect precision, recall, and F1-score of 100%, supporting efficient and scalable motif identification. Future work focuses on further optimization and larger datasets to broaden ikat motif coverage and strengthen digital preservation and education efforts.","Performance comparison of machine learning algorithms for ikat weaving classification  \nMoch. Sjamsul Hidajat*1, Dibyo Adi Wibowo1, Ery Mintorini1  \nUniversity of Dian Nuswantoro, Kediri, Indonesia1  \nArticle Info Keywords:  \nIkat Weaving, Machine Learning, Neural Networks, Random Forest, XGBoost  \nArticle history:  \nReceived: July 14 , 2024  \nAccepted: December 03 , 2024  \nPublished: February 01 , 2025  \nCite:  \nM. S. Hidajat, D. A. Wibowo, and E. Mintorini,“Performance Comparison of Machine Learning Algorithms for Ikat Weaving Classification”, KINETIK, vol. 10, no. 1, Feb. 2025.  \n[https://doi.org/10.22219/kinetik.v10i1.2059](https://doi.org/10.22219/kinetik.v10i1.2059)  \n*Corresponding author. Moch. Sjamsul Hidajat E-mail address:  \n[moch.sjamsul.hidajat@dsn.dinus.ac.id](moch.sjamsul.hidajat@dsn.dinus.ac.id)  \nAbstract  \nIkat 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.  \n1. Introduction  \nIkat weaving is a traditional Indonesian craft that has flourished in East Java, particularly in Bandar Kidul , Kota Kediri, where this cottage industry has been passed down through generations since the Dutch colonial era. Ikat fabric can be categorized as a safeguard for artistic creations similar to batik, protecting its motifs, designs, and color compositions, representing Indonesia's cultural heritage that continues to evolve. It is crucial to inventory and identify the distinctive ikat weavings of Kediri to avoid confusion between newly created motifs attributed to known creators and older motifs whose origins are no longer known. This prevents new creations from falling into the public domain, even when their creators are known and their aesthetics are clear. Ikat weaving exhibits a diverse range of intricate motifs and variations. Manual identification is often challenging and time-consuming, especially for those without deep knowledge of these patterns. Machine learning technology offers sophisticated solutions for pattern identification and classification [1], [2] . With adequate datasets, machine learning models can be trained to recognize complex patterns with high accuracy [3], [4],[5] . Such recognition systems can serve as educational tools to enhance public knowledge about ikat weaving, applicable in various educational and training programs for both general public and students. Developing a prototype of ikat recognition system based on machine learning is an initial step to test the effectiveness and utility of this technology, laying the groundwork for more advanced systems in the future [6],[7],[8] .  \nMany researchers have applied machine learning to identify motif patterns in woven fabric images. Adri Gabriel et al. [9] proposed an approach using decision trees as classifiers combined with SqueezeNet for feature e","cbCaifI922oaTlfL","https://ap.wps.com/l/cbCaifI922oaTlfL","pdf",822529,1,8,"English","en",105,"# Introduction\n## Related Work\n## Research Approach and Dataset\n## Evaluation Results\n## Future Work","[{\"question\":\"Why is ikat motif identification challenging when designs change over time?\",\"answer\":\"As new motifs emerge and knowledge about older designs fades, manual identification becomes time-consuming and difficult, especially for people without deep pattern knowledge.\"},{\"question\":\"Which machine learning algorithms are used for ikat weaving classification?\",\"answer\":\"The study leverages XGBoost, Random Forest, and Neural Network algorithms to automate classification of ikat weaving patterns.\"},{\"question\":\"How was the dataset prepared and what motifs were included?\",\"answer\":\"A dataset of 600 images was split into 480 training (80%) and 120 testing (20%), representing four motifs: Gumul Weaving, Bolleches Weaving, Kuda Kepang Weaving, and Sekar Jagad Weaving.\"}]","Performance Comparison of Machine Learning Algorithms for Ikat Weaving Classification - 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