PROTOTIPE SISTEM KEAMANAN AKSES MASUK RUANGAN BERBASIS VOICE RECOGNITION MENGGUNAKAN ALGORITMA DEEP LEARNING

SULTHONI, ADJI and Hikmarika, Hera (2023) PROTOTIPE SISTEM KEAMANAN AKSES MASUK RUANGAN BERBASIS VOICE RECOGNITION MENGGUNAKAN ALGORITMA DEEP LEARNING. Undergraduate thesis, Sriwijaya University.

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Abstract

The security system that is often used today is the conventional security system. This security system has weaknesses that allow it to be imitated and broken into. Therefore, a security system that uses biometrics is needed. The human voice is one form of biometrics that can be used to identify people (person identification) which can be utilized in the security field. The purpose of this research is to implement the use of voice as a room access security system by using deep learning algorithms in the learning process to recognize voices in real-time. In this research, there are several stages carried out, namely taking voice data, processing voice data, training voice data using 3 CNN architectures, offline testing using testing data and online testing using microphones in real time. The voice data processing used is Short-Time Fourier Transform (STFT) for the feature extraction process in the form of spectrograms. Then, the spectrogram will be processed by Convolutional Neural Network (CNN) as an identifier method. This research uses 3 types of CNN architecture, namely VGG16 architecture, AlexNet, and Own Model Architecture and obtained the best accuracy results from the 3 architectures with epoch 100 of 0.9993, 0.9915, and 1. Testing is done offline and online, offline testing is done using test data and using 3 architectures, obtained the best test results using epoch 100 of 61.76%, 94.11% and 95.09%. Furthermore, online testing is carried out in real time on a prototype in the Basic Laboratory of Control Systems and Robotics using a microphone against 15 respondents consisting of 8 respondents who are in the dataset and 7 who are not in the dataset. The real time test results obtained an average voice recognition accuracy value of 80%. The results show that the model of its own architecture has better performance for recognizing voices, and can be implemented in the room access security system.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Security System, Convolutional Neural Network (CNN), Deep Learning, Voice Recognition, Biometric, Spectogram
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:	Adji Sulthoni
Date Deposited:	27 Jul 2023 06:44
Last Modified:	27 Jul 2023 06:44
URI:	http://repository.unsri.ac.id/id/eprint/122663

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