Valuation K-Nearest Neighbors and Naïve Bayes for Dringking Water Potability Classification

The availability of drinking water that is safe and suitable for consumption is important to support health and development. This research emphasises the importance of handling the clean water crisis through the evaluation of drinking water quality using data mining algorithms.  The dringking water quality evaluation method was selected using the K-Nearest Neighbors and Naive Bayes algorithms, replacing the manual method which is less responsive in predicting. The experimental process was conducted by utilising Kaggle website data by applying data processing and oversampling techniques to handle class imbalance in the dataset used. Bases on the research results, the accurancy of the K-Nearest Neighbors Algorithm reaches 65%, which is higher than the accuracy od the Naive Bayes Algorithm which is 64%. So it can be concluded that the K-Nearest Neighbors Algorithm is more effective in predicting the quality of water suitable for consumption. This research provides an in-depth insight into the use of technology and data analysis in dealing with the crisis in the availability of water suitable for consumption and offers suggestions for further research using more diverse methods and the use of more datasets to improve accuracy in evaluating the quality of potable water.