Open AccessKLASIFIKASI AMERICAN SIGN LANGUAGE MENGGUNAKAN FITUR SCALE INVARIANT FEATURE TRANSFORM DAN JARINGAN SARAF TIRUAN
Author(s) -
Muhammad Restu Alviando,
Muhammad Ezar Al Rivan,
Yoannita Yoannita
Publication year - 2020
Publication title -
algoritme jurnal mahasiswa teknik informatika
Language(s) - English
Resource type - Journals
ISSN - 2775-8796
DOI - 10.35957/algoritme.v1i1.403
Subject(s) - scale invariant feature transform , artificial intelligence , pattern recognition (psychology) , computer science , artificial neural network , feature extraction , test data , sign (mathematics) , invariant (physics) , feature (linguistics) , mathematics , mathematical analysis , linguistics , philosophy , programming language , mathematical physics
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%.