Classification of Water Potability Using Machine Learning Algorithms
M. I. Khoirul Haq, Fauzian Dwi Ramadhan, Fatimah Azzahra, Linda Kurniawati, Afrida Helen
Abstract
Clean water is one of the basic needs of everyday life. Recently, an ongoing process has been shown to improve water quality, making water less suitable for use. To solve this problem, research is done using a machine learning model. The Decision Tree Algorithm is used by Naïve Bayes algorithm in this type of machine learning to support drinking water quality. The two types of performance are compared in this work. K-fold cross credentials are used to evaluate our machine learning model. Results obtained in the decision tree algorithm have the best results in the configuration with an accuracy value of 97.23%.
Topics & Concepts
Machine learningDecision treeComputer scienceAlgorithmArtificial intelligenceNaive Bayes classifierStatistical classificationWater qualityDecision tree learningTree (set theory)Support vector machineMathematicsEcologyMathematical analysisBiologyWater Quality Monitoring TechnologiesHydrological Forecasting Using AIWater Quality Monitoring and Analysis