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Yudistira Bagus Pratama
Computer Science Study Program, Universitas Muhammadiyah Bangka Belitung, Indonesia
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Classification of Batik Cual Bangka Belitung Based on Deep Learning: YOLOv11 Approach Andika Saputra; Zikri Wahyuzi; Yudistira Bagus Pratama
Journal of Informatics and Vocational Education Vol. 9 No. 1 (2026): Journal of Informatics and Vocational Education - March
Publisher : Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/joive.v9i1.3208

Abstract

The rapid growth of artificial intelligence, particularly deep learning, has enabled significant advancements in computer vision and automated image recognition. However, the application of these technologies to traditional cultural artifacts remains limited, especially within the domain of Indonesian textile heritage. Batik Cual Bangka Belitung, which features intricate ornamentation and visually similar motifs, presents unique classification challenges that conventional Convolutional Neural Network (CNN) models struggle to address effectively. To overcome these limitations, this study introduces an automatic motif classification system using the YOLOv11 architecture, a state-of-the-art object detection model capable of identifying and distinguishing fine-grained visual patterns. The research follows a systematic pipeline consisting of dataset collection, curation, manual motif labeling, image preprocessing, model configuration, training, and testing. A curated dataset of Batik Cual images was augmented and divided into training, validation, and testing subsets to ensure robust evaluation. Experimental results demonstrate strong model performance, achieving a precision of 0.934, recall of 0.808, mAP50 of 0.950, and mAP50–95 of 0.8172. These findings confirm that YOLOv11 can accurately detect motif regions and classify them under varying structural and textural conditions. The study contributes not only a reliable technical framework for recognizing Batik Cual motifs, but also supports digital preservation efforts and future cultural computing applications.
Co-Authors Agustine, Putri Cahyani Al Ahfaz Reza Ramdani Altiarika, Eka Andesta Granitio Irwan Andika Saputra Ari Ardiansyah Ari Gunawan Arif, Maulana Arvi Pramudyantoro Azzani, Arlina Feijriah Bayu Saputra Christivo, Ryevilgo Dalimunthe, Nurzaidah Putri Ega Noviani Eka Altiarika Elda Elda Emikawati Erick Prayogo Walton, Erick Prayogo Fabian, Cecep Fani Februari Farhan Aflah Pratama Fas’ya, Alvia Fermanda, Syandhu Dea Haiyudi Haiyudi Haiyudi, Haiyudi Hendrik, Maulina Hevitria Hevitria Jauhar Ridloni Marzuq Marwani Marwani Mega Sukma Mega Sukma Mega, Iful Rahmawati Micha Arnola Muhamad Mustaqim N. Nurjanah Nabela, Silvio Juliana Okta, Evan Dwi Pramesti, Diana Pratama, Suprayuandi Purnamasari, Ratno Putri, Elsa Hana Rahma Ramdani, Al Ahfaz Reza Randi Atul Aufa resti, A. Resti Supraeni Riski Andriani Safitri, Ananda Dwi Setiawan, Rifki Hanif Sevtian, Andre Shinta Pramudya Wardani Sitthipon Art-in Sofwan Arif Agil Suheni Suheni Suheni Sukamto, Ika Sumiyarsi Suprayuandi Pratama Suprayuandi Pratama Tito Dian Permana Vebrian, Rajab Wiwit Febriyanti Yurdayanti, Yurdayanti Zaliman, Iski Zikri Wahyuzi