Palembang Tie-Dye Fabric Motif Detection Software Using The Yolov10 Method - Muhammad Firmansyah; Tinaliah Tinaliah (International Journal of Health Engineering and Technology, 2026)
Palembang's jumputan cloth shops face difficulties in manually identifying motifs, which can lead to inventory errors. This study aims to develop a jumputan cloth motif detection software using YOLOv10 for real-time inventory identification and management automation on Android. The research method combines Research and Development (R&D) with an experimental quantitative approach. The population consists of fabric stocks from three Palembang shops, a sample of 400 images (Titik Tujuh, Beras Tabur, Lereng, Keong) divided into training (80%), validation (10%), and testing (10%). Instruments include a smartphone camera, YOLOv10m, and Google Colab Pro; analysis uses precision, recall, mAP50-95, and confusion matrix. The results show mAP50-95 up to 99.50%, smartphone accuracy 90-100% (SGD is superior), user satisfaction 96.52% via USE Questionnaire, but decreases in low light (Keong 40%). Conclusion: The application effectively supports business efficiency and cultural preservation with a detection time of 3-5 seconds.