The Effectiveness of Smart Waste Recycling Management Applications - Sulistio, Beni; S, Ahmad (Journal of Computer Science Application and Engineering (JOSAPEN), 2024)
This study evaluates the effectiveness of smart waste recycling management applications, which leverage IoT sensors, AI algorithms, and big data analytics to enhance waste management efficiency. Analyzing data from five regions, it is evident that these technologies have significantly improved waste collection efficiency and recycling rates. IoT sensors optimized collection routes, resulting in a 15-23% increase in efficiency and a 10-17% rise in recycling rates, while reducing operational costs by $9,000 to $13,000 per month. AI algorithms enhanced sorting accuracy and recycling rates, particularly in regions with diverse waste types, leading to an 18% improvement in efficiency and up to a 20% increase in recycling rates. Big data analytics facilitated better decision-making and long-term planning, contributing to a 15-20% efficiency boost and a 12-17% rise in recycling rates. These findings underscore the potential of smart waste management technologies to transform waste management practices, highlighting the need for continued investment and expansion of these systems.