Visualization of Indonesian Sign Language System using Image Classification for Special Needs Students in Special Schools

Language is a vital means of communication for human life, from communicating with oneself to interacting with others. Within communication, the role of language is paramount. In Indonesia, sign language is commonly utilized by deaf individuals, guided by the Indonesian Sign Language System (SIBI). Sign language refers to gestures or movements involving fingers, hands, arms, facial expressions, head, and body In this research, we propose utilizing Image Classification, which is the process of categorizing images based on specific rules and employing machine learning algorithms. The machine learning aspect aims to recognize hand shapes in the web camera video. The interpretation of these hand shapes is trained using the Random Forest algorithm, enabling real-time video display showcasing identified hand movements. Ultimately, the displayed alphabet will be converted into a graphical format with assistance from mediapipe.