Sistem Monitoring Kualitas Udara Berbasis IoT (Internet of Things) dengan Notifikasi Dini dan Visualisasi Warna menggunakan Machine Learning
DOI:
https://doi.org/10.55606/juisik.v6i1.2247Keywords:
Air Quality, Internet of Things, Linear Regression, Machine Learning, Random ForestAbstract
Air pollution is an environmental problem that has a significant impact on human health, particularly in urban areas with high industrial and transportation activities. The limitations of conventional air quality monitoring systems, which are mostly static and non-real-time, highlight the need for a more adaptive and informative monitoring system. This study aims to develop an Internet of Things (IoT)-based air quality monitoring system equipped with early warning notifications and color-based visualization using Machine Learning approaches. Air quality data are collected in real-time through a hyperlocal IoT sensor network. Linear Regression and Random Forest algorithms are employed to predict the Air Quality Index (AQI) based on measured air pollutant parameters. The results show that the Random Forest algorithm outperforms Linear Regression in terms of prediction accuracy, particularly in handling nonlinear and fluctuating data. The developed system is capable of providing real-time air quality information, issuing early warnings when air quality deteriorates, and presenting intuitive color-based visualizations. Therefore, the proposed system can support public decision-making in conducting daily activities safely in polluted environments.
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