Analisis Perbandingan Algoritma Klasifikasi MLP dan CNN pada Dataset American Sign Language - Mohammad Farid Naufal; Sesilia Shania; Jessica Millenia; Stefan Axel; Juan Timothy Soebroto; Rizka Febrina P.; Mirella Mercifia (Ikatan Ahli Informatika Indonesia (IAII), 2021)
People who have hearing loss (deafness) or speech impairment (hearing impairment) usually use sign language to communicate. One of the most basic and flexible sign languages ​​is the Alphabet Sign Language to spell out the words you want to pronounce. Sign language uses hand, finger, and face movements to speak the user's thoughts. However, for alphabetical sign language, facial expressions are not used but only gestures or symbols formed using fingers and hands. In fact, there are still many people who don't understand the meaning of sign language. The use of image classification can help people more easily learn and translate sign language. Image classification accuracy is the main problem in this case. This research conducted a comparison of image classification algorithms, namely Convolutional Neural Network (CNN) and Multilayer Perceptron (MLP) to recognize American Sign Language (ASL) except the letters