Indonesian Sign System Introduction Application with Tensorflow Lite and Firebase Authentication

Deaf and mute individuals often face communication barriers with the general public due to limited understanding of sign language. This leads to a gap in social interaction and access to various public services. Government efforts to enhance social inclusion through various policies and programs need to be accompanied by practical solutions that can help the deaf and mute interact more easily with society. This study aims to develop a mobile application that can recognize and translate Indonesian Sign Language System (SIBI) into text or speech in real-time, thus helping the deaf and mute communicate more effectively with the general public. The application is designed using TensorFlow Lite for sign language recognition and Firebase Authentication for user authentication. The application was evaluated through questionnaires involving respondents from the general public and mobile experts. The results of the general user questionnaire showed an average satisfaction percentage of 86.65%, with positive ratings for ease of use, benefits, and application interface. Meanwhile, the results of the expert mobile questionnaire showed full satisfaction with an average percentage of 100%, indicating that all application features functioned well. The findings indicate that this application is effective in recognizing and translating sign language and is well-received by the deaf, mute, and the general public.