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Sistem Keamanan Menggunakan Raspberry PI Disertai Motion Detection Berbasis Internet Of Things | Basuki | RELE (Rekayasa Elektrikal dan Energi) : Jurnal Teknik Elektro

Sistem Keamanan Menggunakan Raspberry PI Disertai Motion Detection Berbasis Internet Of Things

Donni Angger Basuki, Yuliarman Saragih, Ibrahim Ibrahim

Abstract


Abstrak Sistem keamanan menggunakan kamera dengan prinsip pendeteksian gerakan berbasis Internet of Things dan mengirimkan notifikasi serta dapat dikendalikan menggunakan smartphone. Sistem akan mengirimkan notifikasi dan capture gambar gerakan ke aplikasi Pushbullet pada android. Sistem alert juga dapat dikendalikan jarak jauh menggunakan aplikasi Detector pada android. Analisis pengujian performa sistem dilakukan dengan berbagai variabel dan parameter yaitu uji pengaruh nilai tresshold, uji keberhasilan pengiriman notifikasi, uji keberhasilan kendali sistem Raspberry pi terhadap kendali aplikasi Detector, uji kecepatan pengiriman notifikasi, dan uji kecepatan respon Raspberry pi terhadap kendali aplikasi Detector. Pada uji nilai tresshold telah didapat dimana nilai yang memiliki ke akuratan pembacaan gerakan paling baik yaitu pada range 2000-2500 dengan persentase read = 100%, loss = 0% dan error = 0%. Pada uji keberhasilan pengiriman notifikasi dan kendali sistem Raspberry pi terhadap kendali aplikasi detector menggunakan provider telkomsel dan indosat memiliki persentase keberhasilan 100%. Pada uji kecepatan pengiriman notifikasi didapat rata-rata waktu untuk penggunaan provider telkomsel sebesar 0,34 sedangkan indosat sebesar 1,149s. Pada uji kecepatan respon Raspberry Pi terhadap kendali aplikasi Detector didapatkan data rata-rata waktu untuk penggunaan provider telkomsel adalah 1,154s sedangkan indosat sebesar 1,827s.

Kata kunci : OpenCV, Pengolahan Citra Digital, Motion Detection

Abstract The security system uses a camera with the principle of internet of things-based motion detection and sends notifications and can be controlled using a smartphone. The system will send notifications and capture motion images to the Pushbullet app on android. The alert system can also be controlled remotely using the Detector app on android. System performance test analysis is carried out with various variables and parameters, namely tresshold value influence test, notification delivery success test, raspberry pi system control success test to Detector application control, notification delivery speed test, and raspberry pi response speed test to Detector application control. In the tresshold value test has been obtained where the value that has the best movement reading accuracy is in the range of 2000-2500 with read percentage = 100%, loss = 0% and error = 0%. In the successful test of sending notifications and control of the raspberry pi system to control the detector application using telkomsel and indosat providers have a 100% success percentage. In the test, notification delivery speed was obtained the average time for telkomsel provider usage of 0.34 while indosat amounted to 1,149s. In the test of the response speed of raspberry pi to the control of the Detector application obtained data the average time for the use of telkomsel provider is 1,154s while indosat is 1,827s.

Keywords : OpenCV, Digital Image Processing, Motion Detection

Keywords


OpenCV; Digital Image Processing; Motion Detection

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References


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DOI: https://doi.org/10.30596/rele.v5i2.13081

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