Penerapan Convolutional Neural Network dengan Arsitektur Mobilenetv2 Pada Aplikasi Penerjemah dan Pembelajaran Bahasa Isyarat

Abstract

Sign Language is the main communication medium for deaf people, in Indonesia itself there are several types of sign language, one of which is SIBI or Indonesian Sign Language System. This type of SIBI sign language is one type of sign language used in special school (SLB) environments. Deaf children are not yet able to use sign language and others have difficulty communicating with people around them. This research aims to help deaf children learn sign language, as well as help them communicate through sign language interpreters. The methodology applied to the application is by using a Convolutional Neural Network with the MobileNetV2 architecture. CNN is an algorithm that is included in the artificial neural network category which has the advantage of having a very high level of accuracy in classification, and MobileNetV2 is a form of Convolutional Neural Network (CNN) architecture that is able to overcome the need for excess computing resources created by Google researchers. so that it can be used on mobile devices or cell phones. The model training stage obtained an accuracy value of 98.99% with a total of 100 epochs. The model trained did not experience overfitting or underfitting so the model can be used. The testing stage uses black box testing to ensure the application is running correctly. The final stage is conducting research using TAM (Technology Acceptance Model) to measure the level of approval of the application using a Likert scale. Testing using black box testing was successful and met expectations, and the implementation of the application received an approval rate of 78.4% on average.