SIBI Alphabet Detection System Based on Convolutional Neural Network (CNN) Method as Learning Media

Abstract

Sign language is a form of communication that relies on body movements and facial expressions to interact, especially for deaf and hard-of-hearing people. The Indonesian Sign Language System (SIBI) is the official sign language in Indonesia. Until now, there is still a communication gap between deaf and hard-of-hearing people and normal people. The Computer Vision approach is expected to overcome the problem by developing a sign language recognition system. This research focuses on applying Deep Learning with the Convolutional Neural Network (CNN) method to detect hand gestures in SIBI alphabetic sign language and translate them. Hopefully, the results of this research can be the foundation for developing sign language recognition applications optimized specifically for SIBI. They can help people with disabilities and the general public communicate more effectively.