Optimization of Convolutional Neural Network Algorithm for Indonesian Sign Language Classification

Sign language serves as a primary mode of communication for individuals who are deaf or speech impaired, using hand gestures to convey meaning visually. While it facilitates communication among the deaf community, it presents challenges for interaction with those who rely on spoken language. This study aims to recognize hand signs representing the letters A to Y (excluding J and Z) in the Indonesian Sign Language (SIBI) using image-based input. A custom dataset was collected through personal photo shoots and used to train a Convolutional Neural Network (CNN) implemented in Python using the TensorFlow library. The study also focuses on optimizing the CNN architecture to achieve high classification accuracy. Evaluation using a confusion matrix on the test data resulted in an overall accuracy of 87.1%, while real-time testing achieved an accuracy of 90.25%. The number of convolutional filters and dropout rates was adjusted to prevent underfitting and overfitting during model training.