| dc.description.abstract | Diabetes Mellitus is a metabolic disease with characteristics of hyperglycemia that occurs due to
abnormalities in insulin secretion, insulin action or both. The detection of diabetes mellitus disease
using the dataset Pima Indians had been done by various methods, one of which implementation
methods is K-Neaarest Neighbor (KNN). One drawback of the KNN method is the determination of the
optimal parameters k. Value of k that are too high will reduce the effect of noise on the classification,
but makes the boundaries between each classification is becoming increasingly blurred, while the value
of k that is too low will result in sample taking values for the less and lead to reduced accuracy. For this
study proposes the use of Support Vector Machine (SVM) as the optimal solution of k determination. In
this study, we will implement the hybrid SVM-KNN method to be used as a method of classification of
people with diabetes using the dataset "Pima indian". Experiments done by varying the parameter
values and the kernel used to see the value of the accuracy of the hybrid SVM-KNN method.
Parameters that influence the value of C, tolerance, sigma, bias and the value of k on KNN. The highest
average value of the accuracy obtained by using SVM-KNN is 92.00% and proved to be better than
traditional SVM method average of the accuracy only 77.60% and KNN is 91%. | en_US |
