CLASSIFICATION OF AMERICAN SIGN LANGUAGE USING SCALE INVARIANT FEATURE TRANSFORM FEATURES AND ARTIFICIAL NEURAL NETWORKS - Alviando, Muhammad Restu; Al Rivan, Muhammad Ezar; Yoannita, Yoannita (Algoritme Jurnal Mahasiswa Teknik Informatika, 2020)
American Sign Language (ASL) is a sign language in the world. This study uses the neural network method as a classification and the scale invariant feature transform (SIFT) as feature extraction. Training data and test data for ASL images were extracted using the SIFT feature, then ANN training was conducted using 17 training functions with 2 hidden layers. There are architecture used [250-5-10-24], [250-5-15-24] and [250-15-15-24] so there are 3 different ANN architectures. Each architecture is performed 3 times so that there are 9 experiments (3 x 3 trials run the program). Determination of the number of neurons concluded by the training function is selected by the best test results on the test data. Based on the training function and the extraction of SIFT features as input values ​​in the neural network it can be concluded that from 17 training functions, trainb with neuron architecture [250-5-10-24] becomes the best training function producing an accuracy value of 95%, precision of 15 % and recall 5%.