Desain Sistem Monitoring Kendaraan Listrik Pengangkut Sampah Cerdas

Abstract

Waste is an unavoidable issue in daily life. Almost every human activity generates waste, thus requiring an efficient management system, especially for garbage collection vehicles. In Indonesia, garbage trucks generally still rely on fossil fuels and are not yet equipped with intelligent monitoring systems. This study aims to design and implement an intelligent monitoring system for electric garbage trucks based on the Internet of Things (IoT). The system utilizes an ESP32 microcontroller integrated with an HC-SR04 ultrasonic sensor to measure waste height, an S-type load cell to measure waste weight, and a Neo-6M GPS module for vehicle tracking. Data is transmitted to the ThingsBoard platform using the MQTT protocol in JSON format every 3 seconds and displayed through a driver dashboard. The test results show that the system successfully measures and transmits data on waste height, weight, and vehicle location in real time. The ultrasonic sensor provides reasonably accurate level estimates, while the S-type load cell produces varying accuracy levels depending on the weight distribution inside the truck bed. In the evenly distributed load scenario, the sensor achieved very high accuracy with an average of 99.65%, showing an upward trend as the load increased. When the load was concentrated in the center, accuracy remained high at 97.57%, indicating that a centrally placed sensor works optimally when the force is applied directly above it. However, when the load was focused at the front and rear ends, the accuracy dropped significantly to 58.07% and 2.5%, respectively. This decrease is attributed to the hydraulic chassis structure obstructing the transfer of force from the load to the sensor, particularly when the weight is far from the sensor's position. Tests on the left and right sides resulted in average accuracies of 88.64% and 82.95%, respectively, with a decreasing trend as the load increased. Overall, placing the sensor in the center is effective for symmetrical or centered loads but less ideal for uneven load distribution. Therefore, implementing multiple sensors in strategic positions is recommended to enhance system accuracy. Meanwhile, vehicle location monitoring using the Neo-6M GPS module worked effectively, showing minimal deviation when compared to the Garmin eTrex 10 reference device, with nearly 100% accuracy. In conclusion, the system enables efficient and real-time monitoring of electric garbage trucks and has the potential to support smarter and more integrated waste management solutions.