The Office Room Security System Using Face Recognition Based on Viola-Jones Algorithm and RBFN

INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi

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Title The Office Room Security System Using Face Recognition Based on Viola-Jones Algorithm and RBFN
The Office Room Security System Using Face Recognition Based on Viola-Jones Algorithm and RBFN
 
Creator E. Widjaja, Andree
Hery, Hery
Habsara Hareva, David
 
Subject Office Security
Face Recognition
Prototyping
Database
Office Security
Face Recognition
Prototyping
Database
 
Description The university as an educational institution can apply technology in the campus environment. Currently, the security system for office space that is integrated with digital data has been somewhat limited. The main problem is that office space security items are not guaranteed as there might be outsiders who can enter the office. Therefore, this study aims to develop a system using biometric (face) recognition based on Viola-Jones and Radial Basis Function Network (RBFN) algorithm to ensure office room security. Based on the results, the system developed shows that object detection can work well with an object detection rate of 80%. This system has a pretty good accuracy because the object matching success is 73% of the object detected. The final result obtained from this study is a prototype development for office security using face recognition features that are useful to improve safety and comfort for occupants of office space (due to the availability of access rights) so that not everyone can enter the office.
The university as an educational institution can apply technology in the campus environment. Currently, the security system for office space that is integrated with digital data has been somewhat limited. The main problem is that office space security items are not guaranteed as there might be outsiders who can enter the office. Therefore, this study aims to develop a system using biometric (face) recognition based on Viola-Jones and Radial Basis Function Network (RBFN) algorithm to ensure office room security. Based on the results, the system developed shows that object detection can work well with an object detection rate of 80%. This system has a pretty good accuracy because the object matching success is 73% of the object detected. The final result obtained from this study is a prototype development for office security using face recognition features that are useful to improve safety and comfort for occupants of office space (due to the availability of access rights) so that not everyone can enter the office.
 
Publisher Universitas Nusantara PGRI Kediri
 
Date 2021-02-01
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
 
Format application/pdf
 
Identifier https://ojs.unpkediri.ac.id/index.php/intensif/article/view/14435
10.29407/intensif.v5i1.14435
 
Source INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi; Vol. 5 No. 1 (2021): February 2021; 1-12
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi; Vol 5 No 1 (2021): February 2021; 1-12
2549-6824
2580-409X
 
Language eng
 
Relation https://ojs.unpkediri.ac.id/index.php/intensif/article/view/14435/1964
 
Rights Copyright (c) 2021 INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi
https://creativecommons.org/licenses/by-sa/4.0
 

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