Perbandingan Tingkat Akurasi antara Support Vector Machine (SVM) Menggunakan Kernel Polinomial & Kernel Radial Basis Function (RBF) Untuk Klasifikasi Penyakit Polycystic Ovary Syndrome (PCOS)

Abstract

Polycystic Ovary Syndrome (PCOS) is a hormonal disorder that is common in women of reproductive age. Early diagnosis of PCOS is very important to prevent long-term complications. In this study, the author classified PCOS data using the Support Vector Machine (SVM) method with two types of kernels, namely the Polynomial Kernel and the Radial Basis Function (RBF) Kernel, and compared it to the Logistic Regression method. The classification process uses a dataset that has been processed through the preprocessing and data division stages. The training and testing processes are carried out to evaluate model performance. The SVM model with the Polynomial Kernel produces an accuracy of 71%, while the SVM model with the RBF Kernel produces an accuracy of 72%. In comparison, the Logistic Regression method obtained the highest accuracy of 74%. These results indicate that SVM with the RBF Kernel is able to provide competitive performance compared to other methods, especially in handling non-linear data.