Thesis College of Engineering University of Rizal System-Morong 2024

The study aimed to design a smart plastic bottle trash can with the following features: solar-powered, automatic sorter, and monitoring. Furthermore, it aimed to test the performance efficiency of the smart plastic bottle trash can with an automatic sorter in terms of accumulated points and demerit points. Additionally, the study aimed to determine the level of acceptability in terms of Functional Suitability, Usability, Reliability, Security, and Maintainability. The study was conducted at the University of Rizal System, Morong Campus during the School Year 2023–2024. The study analyzed responses from 318 respondents. The researchers used a Likert Scale as an instrument to determine the level of acceptability of the Smart Plastic Bottle Trash Can with Automatic Sorter. Data was collected using a tailored questionnaire checklist with a 5-point Likert scale, facilitating systematic data gathering and providing insights into the sentiments and perspectives of respondents regarding the evaluated subject. The researchers used developmental and descriptive methods of research. Developmental research is the systematic study of designing, developing, and evaluating programs, processes, or products that must meet the criteria of consistency and effectiveness. The findings reveal that the accumulated points from Test 1 to Test 6, as shown in Table 1, consistently increased whenever the system device detected a plastic bottle. For the demerit points from Test 1 to Test 3, as shown in Table 2, points were consistently deducted whenever the system device did not detect a plastic bottle. According to the assessment from the respondents, Functional Suitability received the highest weighted mean of 4.42, with a verbal interpretation of “Extremely Acceptable.” Usability and Security received the lowest weighted mean of 4.36, with a verbal interpretation of “Extremely Acceptable,” and Reliability and Maintainability received 4.37, with a verbal interpretation of “Extremely Acceptable.” The composite weighted mean is 4.38, with a verbal interpretation of “Extremely Acceptable.” The Smart Plastic Bottle Trash Can with Automatic Sorter was successfully developed using various sensors, especially capacitance and IR sensors, along with machine learning to detect and sort plastic bottles. An ultrasonic sensor was used to monitor the number of plastic bottles in the trash can. The system device uses solar-powered energy to operate. Upon login, users are greeted with a personalized dashboard. They can view their department's points and leaderboard rankings, modify login codes for added security, and access usage history specific to their department. Administrative users have additional features, including viewing points for all departments, managing login codes, and accessing comprehensive usage history. The UI also includes a trashcan level indicator and device status section for real-time monitoring and alerts of system errors. The system consistently accumulates points when a bottle is detected and deducts points if the detected object is not a bottle. As presented in the findings, the system device has high acceptability among respondents. The study recommended that the Smart Plastic Bottle Trash Can with Automatic Sorter increase the size of the bin and provide clear instructions on how and where users from their department can utilize the points accumulated through their points system. Additionally, improving the screen's detail level is recommended to enhance user interaction. Developing a comprehensive user’s manual to facilitate device utilization is also essential. Furthermore, future researchers can utilize the findings of this research to advance the development and implementation of similar waste management systems. Keywords: Machine Learning, Internet of Things, Plastic Bottle