Paper for Issue

Intelligent Automation of Segregation and Monitoring System for Sustainable Waste Management

Abstract

This study examined the development and acceptability of an intelligent automation system designed for waste segregation and monitoring at Cebu Technological University–Main Campus. The system integrated artificial intelligence (AI) and Internet of Things (IoT) technologies to improve waste segregation efficiency, support real-time monitoring, and enhance sustainable waste management practices. A descriptive research design was applied. Data were gathered through a structured questionnaire administered to faculty members, technical experts, and students with National Certificate II (NCII) qualifications in electronics-related fields. The evaluation of the developed system followed selected dimensions of Garvin’s Quality Model, including performance, features, reliability, and durability, together with the Technology Acceptance Model (TAM) indicators of perceived usefulness and perceived ease of use. Statistical analysis involved frequency distribution, percentage computation, weighted mean, and correlation analysis. The findings indicated high levels of positive perception regarding the system’s performance, operational reliability, functional features, and durability. Respondents also expressed strong acceptance of the system, particularly in terms of its ease of operation, efficiency in waste monitoring, and usefulness in improving waste management processes. Correlation analysis revealed significant relationships between perceived quality dimensions and system acceptability, suggesting that improvements in system performance and reliability increased user acceptance. The results demonstrate that intelligent automation technologies can support efficient waste segregation and monitoring while promoting environmental sustainability. The study recommends continued development of AI- and IoT-based waste management solutions, user training programs, and further system evaluation to enhance long-term operational effectiveness and adoption in institutional waste management programs.