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Journal : International Journal of Research in Vocational Studies (IJRVOCAS)

River Flood Early Warning System Based on Internet of Things in Binjai City Muhammad Rusdi; Meidi Wani Lestari; Yuvina; Fitria Nova Hulu
International Journal of Research in Vocational Studies (IJRVOCAS) Vol. 2 No. 4 (2023): IJRVOCAS - Special Issues - International Conference on Science, Technology and
Publisher : Yayasan Ghalih Pelopor Pendidikan (Ghalih Foundation)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53893/ijrvocas.v2i4.161

Abstract

Flood is an event of inundation of land, which is usually dry, by water originating from water sources around the land. Binjai City is an area prone to flash floods. This is because in Binjai City there are 5 (five) upstream rivers namely the Bingei river, Mencirim river, Bangkatan river, Diski river and Rambai river. A flood early warning system is a series of systems that function to notify an impending flood disaster. With the existence of a flood early warning system, it can provide information to the community and can reduce victims or losses due to the community's unpreparedness in dealing with flood disasters. This study aims to create a prototype of a river flood early warning system based on the internet of things (IoT). The method used is to design and create a flood early warning system prototype, then perform system testing. The system is designed using the Arduino Mega2560 microcontroller as the system control center, the HC-SR04 ultrasonic sensors and the ESP32-Cam camera module as system input, as well as buzzer, LCD and website as system output. The transmission medium used is wireless via a 4G WiFi Modem connected to the internet. System prototype testing will be carried out in the Bangkatan river area in Binjai City. From the results of the discussion, it was found that the river flood warning system using the HC-SR04 ultrasonic sensor and the ESP32-Cam camera module based on the Internet of Things was successfully designed and implemented in prototype form and worked well. Ultrasonic sensors work well in measuring river water level with an average error percentage of 3.642%. The ESP32-Cam camera module works well in capturing images of river water conditions up to a distance of 200 cm. (9 pt).