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02391nam a22002417a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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ft8767 |
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20251105130528.0 |
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251105b ||||| |||| 00| 0 eng d |
| 041 ## - LANGUAGE CODE |
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engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
T58.5 C38 2024 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Catacutan, Issaiah Neil T.; Lee Symon S.; Legarda, Mari Grazela P. |
| 245 ## - TITLE STATEMENT |
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| Title |
BINSIGHT: Waste Sorting Enhanced with Machine Learning and Reward System |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
. |
| Name of producer, publisher, distributor, manufacturer |
IEEE |
| Date of production, publication, distribution, manufacture, or copyright notice |
c2024 |
| 300 ## - PHYSICAL DESCRIPTION |
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Capstone Project: (Bachelor of Science in Information Technology) - Pamantasan ng Lungsod ng Maynila, 2025 |
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text |
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text |
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text |
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unmediated |
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unmediated |
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unmediated |
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volume |
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volume |
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volume |
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ABSTRACT: Inefficient waste disposal continues to pose a significant environmental challenge, contributing to pollution and undermining recycling efforts. In response to this issue, researchers developed BINSIGHT: Waste Sorting Enhanced with Machine Learning and Reward System, and IoT-based solution aimed at improving waste segregation and promoting responsible disposal habits. The project sought to address the shortcomings of traditional waste management systems, particularly the lack of effective waste classification and insufficient motivation for proper disposal BINSIGHT incorporates a machine learning model capable of categorizing waste into biodegradable, non- biodegradable, and recyclable types. It also integrates IoT sensors for real-time waste detection and includes a reward system designed to encourage user participation and sustained engagement. Pilot testing was conducted at Pamantasan ng Lungsod ng Maynila (PLM) with 52 participants interacting with the system. The machine learning model demonstrated a high level of accuracy in waste classification, and participants responded positively to the overall experience. Feedback highlighted the system’s potential to enhance environmental awareness and improve waste management practices within the community. By combining smart technology with behavioral incentives, BINSIGHT offers a practical and scalable approach to modern waste management challenges. Future developments may focus on expanding the training dataset to further refine classification accuracy and enhancing the reward mechanism to boost user motivation and participation. |
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Filipiniana |
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academic writing |
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Thesis/Dissertation |