Pendidikan Matematika Inklusif untuk Siswa Tunarungu: Pengembangan Model YOLO Ringan untuk Deteksi Operator Aritmetika Dua Tangan - Putu Nanditha Nayottama; Made Gede Sunarya; Putu Hendra Suputra (CV. ALIM'SPUBLISHING, 2026)
This study presents a real-time recognition system for two-handed arithmetic operators (Plus, Minus, Multiply, Divide) to support inclusive mathematics education for deaf students. Utilizing the YOLO26 architecture, the research evaluates four configurations—Nano and Small variants at 640px and 720px resolutions—trained on a newly developed private dataset of 2,828 images. Experimental results demonstrate exceptional accuracy, with all configurations achieving mAP50 scores above 0.99. The YOLO26-s variant at 720px provided the highest precision (0.9842), while benchmarking on a local GTX 1650 GPU confirmed real-time viability with frame rates reaching up to 88 FPS. Even when deployed on a mobile CPU (i5-12500H), the lightweight Nano models maintained a functional execution speed of 23 FPS. These findings confirm that optimized deep learning models can accurately interpret complex mathematical gestures on consumer-grade hardware. Ultimately, this lightweight framework successfully bypasses heavy skeletal tracking overhead, providing a highly robust and accessible foundation for interactive digital educational media.