Development of Innovative Model for Waste Management System Using Internet of Things (IoT) and Machine Learning

Effective and efficient waste management system is a significant challenge in coastal areas. This research proposes the development of innovative model for waste management system using IoT and machine learning. The system consists of hardware such as cameras and image sensors installed in coastal locations to monitor the volume and types of waste. The image data collected from these devices is stored and analyzed using image processing techniques. The YOLO (You Only Look Once) algorithm is used to identify and classify the types of waste. The identified data is then used to train a machine learning model, which allows for the prediction of future waste volume and types. These predictions are used to optimize waste collection schedules, reduce operational costs, plan the types of waste processing, and minimize environmental impact. With this holistic approach, the system is expected to enhance the efficiency and sustainability of waste management, providing an innovative solution to waste management issues in coastal areas.

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Publication Details

Published
2024-10-24
DOI
https://doi.org/10.1109/icic64337.2024.10957619
Primary Topic
Smart Systems and Machine Learning
Type
article
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Development of Innovative Model for Waste Management System Using Internet of Things (IoT) and Machine Learning

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Development of Innovative Model for Waste Management System Using Internet of Things (IoT) and Machine Learning

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article en

Abstract

Effective and efficient waste management system is a significant challenge in coastal areas. This research proposes the development of innovative model for waste management system using IoT and machine learning. The system consists of hardware such as cameras and image sensors installed in coastal locations to monitor the volume and types of waste. The image data collected from these devices is stored and analyzed using image processing techniques. The YOLO (You Only Look Once) algorithm is used to identify and classify the types of waste. The identified data is then used to train a machine learning model, which allows for the prediction of future waste volume and types. These predictions are used to optimize waste collection schedules, reduce operational costs, plan the types of waste processing, and minimize environmental impact. With this holistic approach, the system is expected to enhance the efficiency and sustainability of waste management, providing an innovative solution to waste management issues in coastal areas.

Universitas Muhammadiyah Magelang (ID), Universitas Batam (ID), Universitas Komputer Indonesia (ID), Universitas Dian Nuswantoro (ID)
Responsible consumption and production, Industry, innovation and infrastructure
Openalex Percentile: Top 65%
Smart Systems and Machine Learning
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