AdUTrack: A Mobile Application Tracker for Facilities Maintenance and Waste Detection Using Machine Learning at Adamson University

Waste management and facility maintenance caused challenges at Adamson University. These challenges resulted in environmental and operational issues, due to poor planning, lack of disposal knowledge, and community demotivation. This study aims to provide a solution aligned with Sustainable Development Goal (SDG) 12, promoting sustainable practices and environmental responsibility. This research highlighted the power of technology to improve reporting and promote responsible behavior through interactive features addressing waste management and facility maintenance challenges. It featured the integration of machine learning, using mobile cameras to categorize waste for a more accurate sustainable solution. The study involved conducting surveys and system demonstrations to provide users and beneficiaries with a thorough understanding of the application’s functionalities. The proponents developed and tested the project according to the ISO 25010 Quality Model to ensure high standards and more accurate results for the survey. During evaluation, the system received excellent ratings across all the key indicators. Overall, the project received high ratings, concluding that this study effectively met the needs of the target users and aligned with the campus’ commitment to sustainable development practices.