Al Rivan Muhammad Ezar Primary Author mixed material bibliography Bandung Maranatha University Press 2020 Indonesia

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hlm : 442-451 Abstract Sign languages have various types, one of which is American Sign Language (ASL). In this study, ASL images from the handshape alphabet were extracted using Histogram of Oriented Gradient (HOG) then these features were used for the classification of Artificial Neural Networks (ANN) with various training functions using 3 variations of multi-layer network architecture where ANN architecture consists of one hidden layer. Based on ANN training, trainbr test results have a higher success rate than other training functions. In architecture with 15 neurons in the hidden layer get an accuracy value of 99.29%, a precision of 91.84%, and a recall of 91.47%. The test results show that using the HOG feature and ANN classification method for ASL recognition gives a good level of accuracy, with an overall accuracy of 5 neurons 95.38%, 10 neurons 96.64%, and 15 neurons with 97.32%. Informatika Sistem Informasi JUTISI 24432210 Perpustakaan Teknik UPI YAI JUTISI V6N3 Desember 2020 JUTISI3-003 Perpustakaan FT UPI YAI JUTISI V6N3 Desember 2020 cover_issue_174_en_US.png.png 59221 2023-02-27 14:30:29 2023-02-27 14:30:29 machine generated