| dc.description.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. | en_US |
