SIBI-Based Gesture Recognition System Using Random Forest for Hearing-Impaired Communication

Individuals with hearing impairments often face communication barriers when interacting with people unfamiliar with sign language. One officially recognized sign system in Indonesia is the Indonesian Sign System (SIBI), which conveys meaning through hand gestures. Most existing sign language recognition studies focus on single-hand gestures, limiting expressiveness. This study proposes a two-hand gesture recognition system based on digital image processing to translate SIBI gestures into alphabetic letters, while additional gestures enable text control functions. The dataset consists of 29 gesture classes with 1,000 images per class, totaling 29,000 images, and is divided into training and testing sets using a train–test split. A Random Forest classifier is employed to handle high-dimensional landmark coordinate data. Experimental results demonstrate a classification accuracy of 99.97%. The system is implemented as a real-time, user-friendly application. Although high accuracy is achieved, potential overfitting due to the controlled dataset is identified as a limitation. Future work will focus on improving generalization using more diverse real-world data.