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@thesis{thesis,
author={Fachrurrozi Muhammad and Primanita Anggina and RASUANDI MUHAMMAD},
title ={PENGENALAN ALFABET A-Z BAHASA ISYARAT AMERICAN SIGN LANGUAGE MENGGUNAKAN METODE SUPPORT VECTOR MACHINE},
year={2023},
url={http://repository.unsri.ac.id/101458/},
abstract={Deafness is a condition where a person's hearing cannot function normally. As a
result, these conditions affect ongoing interactions, making it difficult to
understand and convey information. Communication problems for the deaf are
handled through the introduction of various forms of sign language, one of which
is American Sign Language. Computer Vision-based sign language recognition
often takes a long time to develop, is less accurate, and cannot be done directly
or in real-time. As a result, a solution is needed to overcome this problem. In the
system training process, using the Support Vector Machine method to classify
data and testing is carried out using the RBF kernel function with C parameters,
namely 10, 50, and 100. The results show that the Support Vector Machine
method with a C parameter value of 100 has better performance. This is
evidenced by the increased accuracy of the RBF C=100 kernel, which is 99%.}
}