A Comparative Analysis on the Evaluation of KNN and SVM Algorithms in the Classification of Diabetes - Limas, Agus Fahmi; Rosnelly, Rika; Hartono, Hartono; Nursie, Aly (Scientific Journal of Informatics, 2023)
Purpose: Diabetes has received a great deal of attention in medical research because of its profound effect on human health. Many factors cause this disease in the human body. Can be from food or drink that is often consumed by the human body. Diabetes cannot be cured and can only be controlled.Methods: In this study, using 2 data mining techniques namely Support Vector Machine and K-Nearest Neighbor were applied to predict diabetes. In this study, 768 diabetes data were used as trial data, consisting of training data that had been pre-processed data and 400 data cleaning data, 278 data testing data, and 50 diabetes data samples used as samples in the calculation.Result: The performance of each algorithm is analyzed differently, the results of each best algorithm will be analyzed to determine which algorithm can provide better results for predicting diabetes. The results obtained in this study get a value of 0 where the predicted value of the target class for new data is the negative class (Suffer).Novelty: This study compares the SVM and K-NN methods for diabetes classification. So, successfully implemented for data on the classification target