KLASIFIKASI HURUF DAN ANGKA PADA BAHASA ISYARAT MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

ARGABZI, MUHAMMAD and Samsuryadi, Samsuryadi and Rachmatullah, Muhammad Naufal (2023) KLASIFIKASI HURUF DAN ANGKA PADA BAHASA ISYARAT MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN). Undergraduate thesis, Sriwijaya University.

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Abstract

Sign language is one of the communication media that can be used by deaf people, however the use of sign language is not only be utilized by the one who disabled on it, but also can be learned and used by normal people. Classification of signals uses a convolutional neural network (CNN) algorithm which is capable of obtaining important features from each image without human assistance. In addition, the CNN algorithm is more efficient when compared to other neural network algorithms, especially for memory and complexity. AlexNet is an appropriate architecture to be applied in this research. This classification uses 34 classes, providing 8 test scenarios. The highest classification result of the 8 scenarios is 98%. The CNN algorithm can perform sign language classification with high accuracy.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi, Bahasa Isyarat, CNN, AlexNet, Deep Learning
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Muhammad Argabzi
Date Deposited:	22 May 2023 04:11
Last Modified:	22 May 2023 04:11
URI:	http://repository.unsri.ac.id/id/eprint/104310

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