Sign language is a form of visual communication used by individuals who
are deaf or speech-impaired. However, many people in the general public still lack
understanding of sign language, which hinders communication between people
with disabilities and their surroundings. This research aims to develop a real-time
alphabet translator system for Indonesian Sign Language (BISINDO), implemented
as an Android application. The system utilizes a Convolutional Neural Network
(CNN) model based on the MobileNetV2 architecture, which is trained to recognize
26 alphabet letters from hand gesture images sized 128x128 pixels in RGB format.
The dataset was collected and processed through augmentation and divided into
training, validation, testing, and evaluation sets. The model was trained using
transfer learning and fine-tuning methods and then converted into TensorFlow Lite
(.tflite) format for deployment on Android devices. Evaluation results show that the
model achieved an average accuracy of 93% on the evaluation dataset. Testing the
Android application also demonstrated good real-time performance in recognizing
hand gestures. This application is expected to help bridge communication between
people with disabilities and the general public through practical and accessible
technology.