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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Muhamad Fauzan Alfiandi
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Perancangan Sistem Pengamanan Ganda pada Brankas menggunakan Convolutional Neural Network berbasis Raspberry Pi Muhamad Fauzan Alfiandi; Fitri Utaminingrum; Edita Rosana Widasari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 9 (2022): September 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In human life, one of the most important things is security. Security works to prevent, protect assets, physical or digital items that we own from theft and lost items. According to the data from Indonesian National Police Yogyakarta Region, the number of theft cases in 2021 has reached 1219 cases, and that's why a protection system is necessary as an effort to guard against any thief. The commonly used protection system for physical items is a safety box. Technological advancements especially hardware, encourage people to help, simplify and solve problems. Microcontroller technology is currently evolving. Microcontroller serves a digital processing purpose and certain program and instruction can be made according to what we want. Technological advancements can be associated with the security field such as biometric face recognition. This face recognition system can recognize a person's face. To construct a protection system preventing theft, this research uses double security on a safety box, PIN and face detection. Applying the deep learning Convolutional Neural Network for face detection so the system can detect the safety box owner's and not the owner's face. PIN number combination must be inputted to lock the safety box using a solenoid lock. The purpose of this research is to construct a double security safety box without risking losing a key. According to the test results, the system can detect the owner's face object with 83% accuracy, 81% precision, 86% recall with 8.19 seconds of computing time, 100% success rate of PIN input, face detection and keypad integration to solenoid lock test results with a 100% success rate.
Co-Authors Utaminingrum, Fitri Widasari, Edita Rosana