Publikasi Scopus 926 artikel (Per 14 Maret 2022)

Wibowo V.V.P., Rustam Z., Hartini S., Maulidina F., Wirasati I., Sadewo W.
57221911837;26422482100;57211529061;57221906584;57221806240;55014544900;
Ovarian cancer classification using K-Nearest Neighbor and Support Vector Machine
2021
Journal of Physics: Conference Series
1821
1
012007
Department of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Department of Neurosurgery, Faculty of Medicine, University of Indonesia, Dr. Cipto Mangunkusumo National General Hospital, Indonesia
Wibowo, V.V.P., Department of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Rustam, Z., Department of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Hartini, S., Department of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Maulidina, F., Department of Mathematics, University of Indonesia, Depok, 16424, Indonesia; Wirasati, I., Department 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
Ovarian cancer is one of the common malignancies in women and a known cause of death. This condition occurs when a tumor appears from the growth of abnormal cells in the ovary. It causes about 140.000 deaths out of 225.000 cases annually. Most women with ovarian cancer do not have distinctive signs and symptoms even at the late stage. Therefore, diagnosis at an early stage is necessary because it has a significant impact on the survival rate. Machine learning with various methods can be used in the medical field to classify diseases. Among the many methods, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) were used and analyzed in this study to classify ovarian cancer. The data used were from Al Islam Bandung Hospital consisting of 203 instances with 130 labeled ovarian cancer and 73 as non-ovarian. The results showed that the KNN produced higher results than SVM with 90.47% of accuracy and 94.11% of F1-score, while SVM produced accuracy and F1-score values of 90.47% and 92.30% respectively. © Published under licence by IOP Publishing Ltd.
Diagnosis; Diseases; Motion compensation; Nearest neighbor search; F1 scores; K nearest neighbor (KNN); K-nearest neighbors; Late stage; Medical fields; Ovarian cancers; Survival rate; Support vector machines
IOP Publishing Ltd
17426588
Conference Paper
Q4
210
18731