PENGENALAN BAHASA ISYARAT MENGGUNAKAN SENSOR KINECT BERBASIS DEEP NEURAL NETWORK

LIMANTORO, EDWIN and Husin, Zaenal (2021) PENGENALAN BAHASA ISYARAT MENGGUNAKAN SENSOR KINECT BERBASIS DEEP NEURAL NETWORK. Undergraduate thesis, Sriwijaya University.

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Abstract

Hand gestures are one of the media that people with disabilities can use to communicate. This difference in the way of communicating results in the deaf and speech impaired having difficulty communicating with normal people. So, a system that can translate sign language into writing is needed. The method used today is still limited to the Indonesian Sign Language System Language (SIBI), while the general language used to communicate is Indonesian Sign Language (BISINDO). In addition, the method used is highly dependent on the accuracy of feature extraction. Thus, in this study, the introduction of BISINDO was developed using themethod Deep Neural Network (DNN). The data used in this study were taken with thesensor Kinect in the form of depth images from 10 respondents with a total of 3600 data obtained consisting of letters and numbers. From the parameters that have been tested, the results of the training show that the use of the stochastic gradient descent optimizer with a hidden layer of 1500 1500, an learning rate RBMof 0.01, and 1500 epochs shows the best accuracy results with thevalue loss lowestcompared to tests using other parameters. This model is then used in tests carried out 5 times for 36 classes. The accuracy results obtained are 95%. Errors that occur can be caused by the similarity of the existing hand sign language, such as the letters D, F, K, O, and R and numbers 2, 4, and 7.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: BISINDO, DNN, Kinect, klasifikasi citra
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 Edwin Limantoro
Date Deposited: 02 Dec 2021 01:43
Last Modified: 02 Dec 2021 01:43
URI: http://repository.unsri.ac.id/id/eprint/58346

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