PEMANFAATAN ARTIFICIAL NEURAL NETWORK DENGAN METODE HEBB RULE UNTUK PENGENALAN BAHASA ISYARAT INDONESIA STATIS - Dwi Setiawan; Achmad Zakki Falani (STMIK YADIKA BANGIL, 2020)
Abstrak: Communication limitations are a social problem faced by persons with disabilities who are deaf and speech impaired. This problem is not only experienced by people who are deaf and speech impaired. Because it is less able to translate sign language this also becomes a problem for normal people in communicating with people who are deaf and speech impaired. Based on the problems outlined above, an analysis will be carried out for the introduction of static Indonesian sign language patterns. Where the data used is visual data in the form of pictures taken based on the visual form of the hand that refers to the Indonesian sign language type SIBI (Indonesian Sign System) using the artificial neural network method of hebb rule. Visual data in the form of images are obtained from capture results which are then collected and inputted through a system with the Python programming language as much as 72 training data which are then processed with several stages including preprocessing which has 3 stages namely grayscaling, edge detection, and thresholding. Furthermore, the data is processed at the segmentation stage and classification testing is performed on the test data using the hebb rule method which has a percentage of pattern recognition accuracy of 100% in the training data testing and an accuracy percentage of 80.37% in the test data obtained from the capture of 72 test data. Keywords : Hebb Rule, Artificial Neural Network, Grayscale, Edge Detection, Threshold, Ekstraksi, Bahasa Isyarat Indonesia, SIBI.