PENERJEMAHAN AMERICAN SIGN LANGUAGE UNTUK MENAMPILKAN TULISAN (SUBTITLE) MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

RAMADHAN, MUHAMMAD FAJAR and Samsuryadi, Samsuryadi and Primanita, Anggina (2024) PENERJEMAHAN AMERICAN SIGN LANGUAGE UNTUK MENAMPILKAN TULISAN (SUBTITLE) MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK. Masters thesis, Sriwijaya University.

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Abstract

Sign language is a harmonious combination of hand gestures, postures, and facial expressions. American Sign Language (ASL) is widely used because it is easier and more common to apply to everyday basics. Nowadays, American Sign Language research is starting to refer to computer vision so that everyone in the world can easily understand American Sign Language through machine learning. The study uses the Densenet201 and DenseNet201 PyTorch architectures to translate American Sign Language, then displays the translation into written form on a monitor screen. There are 4 data separation comparisons, namely 90:10, 80:20, 70:30, and 60:30. The results showed the best results on DenseNet201 PyTorch for the comparison of 70:30 train-test dataset with accuracy of 0.99732, precision 0.99737, recall (sensitivity) 0.99732, specificity 0.99990, F1-score 0.99731, and error 0.00268. The results of the translation of American Sign Language into written form were successfully carried out by performance evaluation using ROUGE-1 and ROUGE-L so that it produced a precision of 1.00000, Recall (sensitivity) 1.00000, and F1-score 1.00000.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	American Sign Language, DenseNet201, DenseNet201 PyTorch, Terjemahan, Subtitles
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General) > T11 General works > T11.5 Translating T Technology > T Technology (General) > T10.5-11.9 Communication of technical information > T11 General works > T11.5 Translating T Technology > T Technology (General) > T10.5-11.9 Communication of technical information > T11.5 Translating
Divisions:	09-Faculty of Computer Science > 55101-Informatics (S2)
Depositing User:	Fajar Ramadhan
Date Deposited:	05 Sep 2024 03:17
Last Modified:	05 Sep 2024 03:17
URI:	http://repository.unsri.ac.id/id/eprint/156723

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