PENGENALAN BAHASA ISYARAT INDONESIA SECARA REALTIME MENGGUNAKAN RECURRENT NEURAL NETWORK

ANDREAS, YOEL and Hikmarika, Hera (2022) PENGENALAN BAHASA ISYARAT INDONESIA SECARA REALTIME MENGGUNAKAN RECURRENT NEURAL NETWORK. Undergraduate thesis, Sriwijaya University.

	Text RAMA_20201_03041281722060.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy
	Text RAMA_20201_03041281722060_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy
Preview	Text RAMA_20201_03041281722060_0007127801_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (592kB) | Preview
	Text RAMA_20201_03041281722060_0007127801_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (692kB) | Request a copy
	Text RAMA_20201_03041281722060_0007127801_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (456kB) | Request a copy
	Text RAMA_20201_03041281722060_0007127801_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (386kB) | Request a copy
	Text RAMA_20201_03041281722060_0007127801_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (182kB) | Request a copy
	Text RAMA_20201_03041281722060_0007127801_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (297kB) | Request a copy
	Text RAMA_20201_03041281722060_0007127801_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (2MB) | Request a copy

Abstract

Hand gestures are one of the communication media that people with disabilities can use to communicate. In daily life, deaf people often have difficulty communicating with normal people because not everyone understands sign language. Thus, a sign language interpreter that allows people who do not know sign language to communicate with deaf people is needed. The methods that are often used today are still limited to the Indonesian Sign Language System (SIBI), while the common language used to communicate is Indonesian Sign Language (BISINDO). In addition, the method used is also still very dependent on the accuracy of feature extraction. So, in this research a system was developed that is used to recognize BISINDO using the Recurrent Neural Network (RNN) method. The data used in this study was taken with a webcam in the form of a video that will be converted into frames and arrays. Data was taken from 3 respondents with a total of 3.240 videos and 97.200 array data consisting of letter and number characters. From the parameters that have been tested, the training results show that the use of the Adam optimizer with a learning rate of 0.0001, and 500 epochs shows the best accuracy with a minimum loss value compared to testing using other parameters. This model was then used in realtime testing which was carried out 5 times for 36 classes. The accuracy result obtained is 81.67%. The error that occurs can be caused by the similarity of the existing hand signal language, such as the letters I, J, D, P, M, and N.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	BISINDO, RNN, Klasifikasi Realtime
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1-9971 Electrical engineering. Electronics. Nuclear engineering > TK1 Electrical engineering--Periodicals. Automatic control--Periodicals. Computer science--Periodicals. Information technology--Periodicals. Automatic control. Computer science. Electrical engineering. Information technology.
Divisions:	03-Faculty of Engineering > 20201-Electrical Engineering (S1)
Depositing User:	Mr. Yoel Andreas
Date Deposited:	03 Aug 2022 07:50
Last Modified:	03 Aug 2022 07:50
URI:	http://repository.unsri.ac.id/id/eprint/75664

Actions (login required)

View Item