<![CDATA[Communication difficulties between the general public and people with hearing impairments due to limited access to real-time detection tools are the primary urgency of this research. This research aims to develop a cross-platform and easily accessible website-based sign language detection system, while implementing the YOLOv8 variant to remain accurate on devices with limited computing resources. The method used is Research and Development (R&D) with the AI Project Cycle framework, which includes data collection, preprocessing, modeling using the YOLOv8n variant, and implementation. The data used is sourced from the Roboflow platform, consisting of hand gesture images divided into 70% training data, 20% validation, and 10% testing. The results show that the YOLOv8n model provides high performance with a precision of 0.932, recall of 0.997, and mAP50 value of 0.995. Additionally, the model achieves an efficient inference speed averaging 2.1 ms. In conclusion, the implementation of YOLOv8 on a website-based successfully creates an accurate and responsive sign language detection system, making it suitable for assisting communication in real-world scenarios]]>
Copyrights © 2026
