PENGENALAN KARAKTER SIBI MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK

Abstract

Communication between humans is an important thing for the activities of daily life. However, Humans were created with the advantages and disadvantages of each person and one of them is the difficulty of establishing or communicating and interacting for people who are deaf and speech impaired. Meanwhile, what solutions can be given about these shortcomings. So that there is no gap in society.

This has led to the development of sign language recognition systems, which can automatically translate sign language into text and speech with effective pre-processing and accurate sign classification. According to recent developments in the field of deep learning, neural networks may have broad implications and implementations for sign language analysis. In the proposed system, Convolutional Neural Network (CNN) is used to classify sign language images because convolutional networks are faster in feature extraction and image classification than other classifiers.

CNN architecture is carried out in 3 stages, 25 epochs, 50 epochs and 100 epochs. Based on the experiments conducted, the accuracy value obtained continues to increase in each stage, starting from 91.03%, 92.69% to the highest accuracy value in the training process of 94.25%. Likewise, the data prediction process also increases in each stage, starting from 90.62%, 93.75% until the highest accuracy value in data prediction is same 95.83%.