Implementasi Metode Support Vector Machine pada Klasifikasi Diagnosis Penyakit Hipertensi - Elnaz Putri, Hilda Zaqya; Fahmi, Hisyam (Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang, 2024)
Hypertension is one of the leading causes of death worldwide. This disease is often referred to as the silent killer because it can lead to death without noticeable symptoms, leaving those affected unaware of their condition. Therefore, early detection and management of hypertension are crucial. This research aims to obtain the classification of hypertension using the Support Vector Machine (SVM) method by utilizing various attributes such as age, smoking habits, lifestyle, blood pressure, and hypertension diagnosis, as well as determining the accuracy level of hypertension classification results using the SVM method. The SVM method is trained with various kernel parameters and hyperparameters to find the best model. The research findings indicate that the best model for classifying hypertension using the SVM method employs the RBF kernel with parameters = 100 and (gamma) = 0,1, achieving an accuracy of 97.15%. This demonstrates that the SVM method is capable of classifying hypertension very well and significantly contributes to the early detection and management of hypertension.