Design and Implementation of an AI-Driven Smart Waste Sorting System: A Java-Based Simulation for Enhancing Recycling Efficiency

This study presents the design and implementation of an AI-driven smart waste sorting system supported by a Java-based simulation framework to enhance recycling efficiency. The research addresses persistent challenges in manual waste sorting—such as misclassification, high labor dependency, and contamination—by integrating machine learning models for waste identification and a real-time decision workflow simulated through Java. A multi-class classification model was trained using CNN, SVM, and Random Forest algorithms, with CNN achieving the highest accuracy (94.8%). The simulation replicates waste flow dynamics, sorting decisions, and throughput variations, enabling systematic evaluation of model performance under different scenarios. Experimental results show significant improvements in sorting accuracy, purity rate, and operational efficiency compared to traditional manual sorting. The proposed system demonstrates that AI-powered classification, combined with a modular Java simulation environment, can serve as an effective tool for advancing intelligent waste management technologies. This work offers practical insights for future smart waste infrastructures and provides a reusable platform for researchers to test and optimize sorting algorithms before real-world deployment.