Klasifikasi dengan metode Support Vector Machine (SVM) dalam menentukan kualitas air minum
Abstract
The increasingly limited availability of clean water is becoming a serious global problem. The clean water crisis significantly impacts global health, including deaths from contaminated water. The time-consuming process of water testing is a significant obstacle in determining water quality. This research uses the Support Vector Machine (SVM) method for water quality classification. The data used comes from a collection of various compounds and elements used in water quality testing. This research involves the stages of data collection, data cleaning, data labeling, and data division into training data and test data with three test scenarios. Model evaluation was done by measuring accuracy, precision, and recall. The results showed that SVM with RBF kernel best predicted drinking water quality with 95% accuracy. These findings contribute to developing more accurate and efficient water quality testing methods
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