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Ama Muzni Mahmudi
Information Technology, Pradita University
Computer Science & IT

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Journal : TEPIAN

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AI for Enhanced Efficiency in Business Waste Sorting Strategies Ama Muzni Mahmudi; Teddy Mantoro
TEPIAN Vol. 6 No. 3 (2025): September 2025
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v6i3.3379

Abstract

As the global waste crisis grows, businesses are under pressure to improve waste management. AI, especially through machine learning and image recognition, offers innovative solutions for optimizing waste sorting. By using Convolutional Neural Networks (CNNs) and deep learning models trained on extensive datasets of waste images, companies can automate the classification of materials such as plastic, glass, and metal with high accuracy. This reduces reliance on manual labor, minimizes human error, and improves the speed and precision of sorting. Cameras capture images of waste items on conveyor belts, which are then analyzed by AI algorithms in real time. These systems continuously improve through feedback loops and reinforcement learning, leading to more efficient sorting over time. The result is higher recycling rates, reduced operational costs, and enhanced sustainability outcomes. AI-based systems enable businesses to decrease waste sent to landfills, recover valuable materials, and lower costs associated with waste management. With continuous updates to their training data and the use of edge computing for real-time processing, these solutions represent a major advancement in sustainable business practices.
Co-Authors Teddy Mantoro