Quality Classification of Air Quality in Medan Industrial Area Using Naïve Bayes Method
DOI:
https://doi.org/10.62123/enigma.v2i2.61Keywords:
Classification, Naive Bayes, Data mining, Air Pollution Standard Index, AirAbstract
Advances in information technology have affected various aspects of life, including efforts to monitor air quality. Clean air is a basic human need, but technological developments and increased industry and the number of motorized vehicles have caused a decline in air quality. Air pollution has various negative impacts, including health problems and global warming. To help the community and government in monitoring air quality, this study implements a data mining method with a classification technique using the Naïve Bayes Algorithm. This method was chosen because of its effective ability to predict air quality based on historical data. This study uses data from the Air Pollution Standard Index (ISPU) parameters to build a classification model that can separate air quality categories, such as Good, Moderate, Unhealthy, Very Unhealthy, and Hazardous. The results of the study are expected to provide accurate information to the public about air quality in KIM, as well as assist the government in efforts to control air pollution.
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