Water Quality Classification Using K-Nearest Neighbor Algorithm

Gabriella Puteri Ayu Handiaz Mawarti, Eko Hari Rachmawanto

Abstract


Water quality is defined as how well water meets certain standards and criteria for various purposes, such as human consumption, agriculture, industry, and ecosystem preservation. Water quality parameters include various physical, chemical, and biological elements that can affect water properties and safety such as bacteria, mercury, nitrate, nitrite, and aluminum. To classify current and future water quality, machine learning-based classification models such as the K-Nearest Neighbors (KNN) algorithm, were calibrated using historical data and tested on independent datasets to evaluate classification accuracy. The result of this study is to use 20 attributes which are water quality parameters. Calculation of model evaluation using confusion matrix and k= 3,5,7 with split data 70% training data and 30% testing data resulted in an accuracy of 87.83% with a "safe" annotation

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References


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