Sistem Deteksi Kosakata Bahasa Isyarat Secara Real Time dengan Tensorflow Menggunakan Metode Convolutional Neural Network - Widjaya, Vincent Surya; Wasito, Ito (JURNAL MEDIA INFORMATIKA BUDIDARMA, 2024)
Most people are not used to communicating via sign language because sign language is not a mandatory language to learn so very few people understand how to communicate using sign language. This problem for people with special needs to interact and communicate with other people, especially those who do not understand how to communicate using sign language. Convolutional Neural Network is the method that will be used in this research because this method is one of the deep learning methods that currently provides the best results in object detection. The Convolutional Neural Network method is able to imitate human abilities in the image recognition system of the human visual cortex, so that it is able to process visual information. This research used Tensorflow to develop various models as well as work related to other statistical analysis. The evaluation metrics results obtained from this research show precision of 82.99%, recall of 84.42% and f1-score of 83.68%. The value of average precision is 0.830% and average recall is 0.844%. There is also a loss value produced by this model of 0.039065. For accuracy results from real time sign language detection, the accuracy value was 94.64%.