Klasifikasi Kualitas Air Minum menggunakan Penerapan Algoritma Machine Learning dengan Pendekatan Supervised Learning

Savitri, Lidya
Nursalim, Rahmat

Diophantine Journal of Mathematics and Its Applications
Vol. 2 No. 1 (2023)

The need for the provision and service of clean water from time to time is increasing which is sometimes not matched by the ability and knowledge of clean water. The majority of people still do not know whether water is suitable for consumption or not. The quality of drinking water can be distinguished based on the mineral parameters contained in the water. This article will explain the classification of water sample data by applying a Machine Learning Algorithm, which includes modeling with Logistic Regression, Support Vector Machine (SVM), Random Forest Classifier, K- Nearest Neighbor(KNN), XGBoost Classifier. Classification models produce varying degrees of accuracy. The highest accuracy is obtained in the Random Forest Classifier model with an accuracy rate of 78%. Analysis of drinking water quality with machine learning algorithms is very easy to understand, because the results of this study produce very simple results so that they are easy to understand