KLASIFIKASI AMERICAN SIGN LANGUAGE MENGGUNAKAN FITUR SCALE INVARIANT FEATURE TRANSFORM DAN JARINGAN SARAF TIRUAN

  • Muhammad Restu Alviando STMIK Global Informatika MDP
  • Muhammad Ezar Al Rivan STMIK Global Informatika MDP
  • Yoannita Yoannita STMIK Global Informatika MDP
Keywords: SIFT, Neural Network, ASL

Abstract

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%.

 

Published
2020-10-10
How to Cite
Alviando, M. R., Al Rivan, M. E., & Yoannita, Y. (2020, October 10). KLASIFIKASI AMERICAN SIGN LANGUAGE MENGGUNAKAN FITUR SCALE INVARIANT FEATURE TRANSFORM DAN JARINGAN SARAF TIRUAN. Jurnal Algoritme, 1(1), 1-11. Retrieved from http://jurnal.mdp.ac.id/index.php/algoritme/article/view/403