<![CDATA[SIBI (Indonesian Sign Language System) is a visual communication system used by the deaf and hard-of-hearing community for interaction. Learning SIBI requires interactive media to enable users to achieve precise gesture mastery. A key challenge in self-directed learning is the lack of real-time feedback to evaluate gesture accuracy, particulary in the absence of instructors or companions. To address this, integrating technology becomes essential for developing an independent and effective SIBI learning framework. This study designed a SIBI letter recognition system using the YOLOv8 model, implemented on the Android platform. The system employs a smartphone camera to detect users hand movements on real-time. Captured hand gestures are processed via the YOLOv8 object detection algorithm, with identification results displayed as bounding box and corresponding text labels in the application interface. The system was tested at three different distances, namely 0.5 meters, 1 meter, and 1.5 meters. The experimental results demonstrated high accuracy rates, achieving 98.72% at a distance of 0.5 meters, 96.15% at 1 meter, and 91.02% at 1.5 meters. These results confirm that the SIBI recognition system based on YOLOv8 is an accurate and effective educational tool, well suited to support the learning process for novice users.]]>
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