Resiklo: An IoT Based Waste Management System Implementing Neural Networks and QR Codes for Recycling Incentives
Maria Allyza A. TOQUIRE, Adrian Cristopher NEBASA, Einid Hope H. TANDOG, Loise Poleen L. CULANNAY, Jenelyn M. ARANAS
Abstract. Waste management remains a major challenge in many communities due to increasing waste generation, inefficient segregation practices, and limited participation in recycling programs. This study proposes RESIKLO, a smart waste management system developed for Guinayangan, Quezon. The system integrates Internet of Things (IoT)–enabled smart bins, machine learning–based waste classification, and QR code technology to support an incentive-based recycling program. Ultrasonic sensors monitor real-time waste bin fill levels, while a mobile application allows users to track waste disposal activities and redeem digital recycling rewards. The system was evaluated based on software quality characteristics, including functional suitability, performance efficiency, compatibility, usability, reliability, security, and maintainability. Results showed high user satisfaction with mean scores ranging from 4.3 to 4.6, interpreted as Very Satisfactory to Excellent, with usability receiving the highest rating. These findings indicate that RESIKLO effectively enhances waste monitoring, improves waste segregation practices, and encourages community participation in recycling initiatives.
Keywords
Waste Management, IoT, Neural Networks, QR Codes, Recycling Incentive
Published online 5/10/2026, 9 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Maria Allyza A. TOQUIRE, Adrian Cristopher NEBASA, Einid Hope H. TANDOG, Loise Poleen L. CULANNAY, Jenelyn M. ARANAS, Resiklo: An IoT Based Waste Management System Implementing Neural Networks and QR Codes for Recycling Incentives, Materials Research Proceedings, Vol. 66, pp 381-389, 2026
DOI: https://doi.org/10.21741/9781644904152-35
The article was published as article 35 of the book Advanced Materials and Sustainable Energy Technologies
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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