Design and build IoT-based noise monitoring and analysis tools in hangars Aviation Polytechnic

Abstract

Noise in aircraft hangars is a crucial problem in the aviation industry that can threaten the health, safety, and productivity of workers. The Surabaya Aviation Polytechnic hangar, especially the Diploma 3 Aircraft Engineering program, produces high noise levels due to aircraft engine maintenance and testing activities that have the potential to endanger students, technicians, and instructors. This research aims to design and build an Internet of Things (IoT)-based noise monitoring system that is able to detect, monitor, and analyze noise levels in real-time at the AMTO 147D-10 Hangar of the Surabaya Aviation Polytechnic. The research method used an experimental approach with prototype development that integrated the FC-04 sound sensor, ESP32 microcontroller, 128x64 pixel OLED display, and a web-based monitoring platform using Node.js. The system is designed with portable dimensions of 21.5 x 14.5 x 8.5 cm and utilizes Wi-Fi, HTTP, and WebSocket communication protocols for data transmission to cloud servers. The test was carried out
at three measurement points with a distance of 1 meter, 3 meters, and 5 meters from the noise source in the form of a grinding machine, then the results were compared with the standard Sound Level Meter of the Blue Gizmo BG325 brand. The results showed that the system had very high accuracy with an accuracy percentage of 99.293%-99.882% at a distance of 1 meter with an error rate of 0.118%-0.707%, an accuracy of 98.583%-99.527% with an error of 0.473%- 1.417% at a distance of 3 meters, and an accuracy of 95.970%-96.786% with an error of 3.214%-4.030% at a distance
of 5 meters. The measured noise level ranges from 81.17-84.43 dB which is close to the safe threshold of 85 dB according to OSHA standards and the Minister of Manpower Regulation No. 5 of 2018. The system provides an effective solution for continuous noise monitoring with early warning features, real-time data visualization, and historical data storage that support strategic decision-making in occupational safety and health management in aviation vocational
education environments.