Publikasi Scopus FKUI 2021 per tanggal 30 Juni 2021 (428 artikel)

Maulidina F., Rustam Z., Hartini S., Wibowo V.V.P., Wirasati I., Sadewo W.
57221906584;26422482100;57211529061;57221911837;57221806240;55014544900;
Feature optimization using Backward Elimination and Support Vector Machines (SVM) algorithm for diabetes classification
2021
Journal of Physics: Conference Series
1821
1
12006
Departement of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Department of Neurosurgery, Faculty of Medicine, University of Indonesia, Dr. Cipto Mangunkusumo National General Hospital, Indonesia
Maulidina, F., Departement of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Rustam, Z., Departement of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Hartini, S., Departement of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Wibowo, V.V.P., Departement of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Wirasati, I., Departement of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Sadewo, W., Department of Neurosurgery, Faculty of Medicine, University of Indonesia, Dr. Cipto Mangunkusumo National General Hospital, Indonesia
Diabetes is a disease that occurs when the blood glucose level is higher than normal and also leads to health problems. Early and accurate diagnosis needs to be carried out on individuals affected by this disease. Furthermore, excellent treatment needs to be provided to prevent worse situations. Some studies have used several machine learning methods to diagnose diabetes. Furthermore, in this study, the Backward Elimination and Support Vector Machine (SVM) algorithm was used to classify the PIMA Indians diabetes dataset. It consisted of 268 diabetic and 500 non-diabetic patients with eight attributes. Backward Elimination is a feature selection method used to remove irrelevant features based on the linear regression model. Using this method, the right features for the model was expected. This method has some advantages which include increasing training time, decreasing complexity and improving performance and accuracy. Therefore, the performance of SVM improved. Based on the experiments, it was discovered that by combining feature selection algorithm (backward elimination) and SVM, the highest accuracy obtained was 85.71% using 90% data training. Therefore, it was concluded that Backward Elimination combined with SVM algorithm is an excellent method to classify diabetes by using the PIMA Indians diabetes dataset. © Published under licence by IOP Publishing Ltd.
Classification (of information); Diagnosis; Experimental mineralogy; Feature extraction; Learning systems; Regression analysis; Feature optimizations; Feature selection algorithm; Feature selection methods; Improving performance; Linear regression models; Machine learning methods; Support vector machine algorithm; Support vector machines algorithms; Support vector machines
IOP Publishing Ltd
17426588
Conference Paper
Q3
227
17171