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All Journal Jurnal Rekayasa elektrika
Muhammad Hafidz Udzri
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Convolutional Neural Network for Hand Gesture Detection in an IoT-Based Smart Lock System Wandi Ridwansyah; Muhammad Hafidz Udzri; Qonita Banafsaj; Unang Sunarya
Jurnal Rekayasa Elektrika Vol. 21 No. 4 (2025): Vol. 21, No. 4, December 2025
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v21i4.755

Abstract

In the modern era, security is a major concern, with many cases of theft involving traditional locks. This research aims to develop an IoT-based smart lock system that can be controlled by an android application. The system uses Convolutional Neural Network (CNN) for hand gesture recognition as the control method and TensorFlow Lite for inference on mobile devices. Assessment showed an average accuracy of 96.80% for the closed hand gesture (closing the door) and 96.27% for the open hand gesture (opening the door), with a response time of 0.1 seconds. The system improves efficiency and security and provides easy remote access. Despite challenges such as gesture recognition in low-light conditions, the system provides an innovative solution for improved facility security.
Co-Authors Qonita Banafsaj Unang Sunarya Wandi Ridwansyah