Modeling Automatic Waste Sorting Using Ultrasonic Sensors - Akhmad Wakhid; Marselina Junia Sipit (ASTEEC, 2025)
Waste management remains a critical challenge due to the increasing volume of solid waste and the inefficiency of manual sorting processes. This study develops and implements an Internet of Things (IoT)-based automatic waste sorting system using an ESP32 microcontroller. The proposed system integrates ultrasonic sensors, an inductive proximity sensor, and an MQ135 gas sensor to automatically detect and classify metal and non-metal waste. The system also connects to the Blynk platform to enable real-time monitoring and notification capabilities, allowing users to observe system conditions remotely. Experimental evaluation is conducted using 100 waste samples consisting of 50 metal objects and 50 non-metal objects. The results show that the system correctly classifies 48 metal samples and 42 non-metal samples. Meanwhile, 8 non-metal samples are misclassified as metal, and 2 metal samples are incorrectly detected. Based on these results, the system achieves an overall classification accuracy of 90%, indicating reliable performance in distinguishing between metal and non-metal waste materials. Further evaluation using precision, recall, and F1-score metrics confirms the effectiveness of the proposed system. The metal class achieves a precision of 85.71%, a recall of 96%, and an F1-score of 90.57%. For the non-metal class, the system records a precision of 95.45%, a recall of 84%, and an F1-score of 89.39%. These results demonstrate balanced classification performance for both categories. Therefore, the developed IoT-based automatic waste sorting system provides a practical and reliable approach for improving waste management efficiency and supporting intelligent waste processing based on material characteristics.