AUTOMATIC WASTE SEGREGATION ENHANCING EFFICIENCY, SUSTAINABILITY, AND ENVIRONMENT FRIENDLY AT BESTLINK COLLEGE OF THE PHILIPPINES BULACAN CAMPUS

Abstract

In recent years, the Philippines has faced a significant rise in waste production, reaching 40,087.45 tons per day in 2016. In response, Bestlink College Philippines – Bulacan campus launched an ICT-based waste segregation initiative involving both students and the local community. Through the integration of technology and educational efforts, the project aims to improve waste management, reduce pollution, and encourage recycling. This innovative approach not only benefits the environment but also educates students, empowering them to become responsible environmental advocates in the future. Bestlink College of the Philippines-Bulacan Campus is exploring the potential of an automatic waste segregator to improve environmental efficiency and sustainability. Through an online survey conducted via Messenger, the college is gathering data to assess the impact of the segregator without the need for experimental manipulation. This research aims to evaluate how this technology can optimize waste management practices and contribute to a more sustainable, eco-friendly future. Bestlink College of the Philippines found that the implementation of automatic waste segregation systems significantly increased student engagement in waste management. This technology not only improved the accuracy of waste sorting but also contributed to a reduction in overall waste. Students became more environmentally conscious and demonstrated a stronger commitment to sustainable practices, highlighting the effectiveness of tech-driven education in promoting environmental stewardship. This summarizes the "Automatic Waste Segregation Enhancing Efficiency" study at Bestlink College of the Philippines-Bulacan Campus. The study targeted Senior High School students, using stratified random sampling based on strand, age, and gender. It employed qualitative research and descriptive methods, collecting data through an online survey questionnaire. Statistical tools, such as percentages, were used for data analysis. The study aimed to assess the impact of automatic waste segregation on waste management efficiency and student engagement in environmental sustainability practices.