Analisis Kinerja Model Support Vector Machine (Svm) Melalui Optimasi Parameter dengan Genetic Algorithm (Ga) pada Klasifikasi Penyakit Ginjal Kronis

Abstract

This study uses hyperparameter optimization to improve accuracy in classifying chronic kidney disease (CKD) through the Support Vector Machine (SVM) algorithm. SVM is combined with the Grid Search technique for hyperparameter optimization, and SVM is combined with the Genetic Algorithm (GA) as a more adaptive optimization method. The test results show that combining SVM with Grid Search produces an average accuracy of 73.75%. However, when SVM is optimized using the Genetic Algorithm, the classification accuracy increases significantly to an average of 97.5%. The model optimized with the Genetic Algorithm performs significantly in both target classes. For class 0 (non-CKD), this model achieves a precision of 96.00%, a recall of 100%, and an F1-score of 98.00%. Meanwhile, for class 1 (CKD), the precision is recorded at 100%, a recall of 93.00%, and an F1-score of 96.00%. Genetic Algorithm for SVM hyperparameter optimization substantially improves the classification ability in detecting CKD, providing more precise results than the SVM-Grid Search method. The combination approach of SVM and Genetic Algorithm offers a more effective solution in classifying chronic kidney disease, which can improve the accuracy of medical diagnosis and help in better clinical decisionmaking