Performa Metode Klasifikasi Tunggal dan Ensemble Model dalam Identifikasi Baku Mutu Air

Abstract

Water quality classification for the needs of recreational facilities, livestock, fisheries, and plantations is needed to determine utilization based on water quality according to national water quality standards. The methods used in this research are K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naïve Bayes (NB), and Ensemble Model. The parameters measured consisted of temperature, TDS, TSS, pH, COD, BOD, DO, and rainfall. The main objective of this research is to discover the performance of a single classification method and ensemble model on data types with unbalanced class distributions. Classification objects are divided into two classes. First, is the class for the designation of recreational facilities, fisheries, and livestock. Second, the class for the allotment of crop cultivation. The test results of the application of the KNN obtained 86%, SVM obtained 87%, and NB obtained 90.57%. Meanwhile, through the ensemble model, the results obtained are 94.43% Bagging Classifier, 94.96% Gradient Boosting Classifier, and 95.94% Adaboost Classifier