Comparison of investor detection algorithm in internet of things based home security systems
DOI:
https://doi.org/10.35335/mandiri.v12i2.245Kata Kunci:
Home Security, Intruder Detection, YoloAbstrak
Empirical studies state that the environment is the main factor that influences crime patterns, so closed circuit television (CCTV) is an option to reduce the risk of crime. However, CCTV is less effective because it requires high bandwidth & storage and cannot provide notifications. Therefore, a technology called the internet of things (IoT) has emerged so that CCTV or webcams can work together with sensors to detect the presence of intruders and provide notifications. This research proposes a system that detects intruders and sends notifications to home owners without being tied to time and place. This system is usually referred to as a smart home security system. This research aims to compare intruder detection algorithms in IoT-based home security systems. This research method uses the internet of things (IoT) in smart homes or home security by comparing the accuracy and processing time of the HoG+SVM and Yolo V3 algorithms. The results of the system implementation show that the most accurate intruder detector is the Yolo V3 algorithm with an accuracy of 99% and a processing time of 14.852 seconds. This processing time can be accelerated by using a Graphics Processing Unit (GPU) with higher specifications.
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