Komparasi Algoritma KNN dan SVM dalam Memprediksi Penyakit Stroke

Rahel Lina Simanjuntak (Universitas Negeri Medan)
Rizki Agung Ramadhan (Universitas Negeri Medan)
Theresia Romauli Siagian (Universitas Negeri Medan)
Vina Anggriani (Universitas Negeri Medan)

Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Vol. 3 No. 3 (2023): November : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer

Stroke is a serious medical condition that affects many people around the world. The ability to predict a person's stroke risk can help in effective prevention, treatment and care. In this study, a comparison between the K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms was conducted to predict stroke risk. The KNN algorithm is a method that searches for the nearest neighbors among the data points to be predicted and assigns the most common label among its neighbors. Experimental results show that both KNN and SVM can provide fairly accurate stroke predictions. However, from an operational point of view, SVM consistently performed better than KNN in terms of accuracy and precision. This research provides insight into the differences between KNN and SVM algorithms in the context of stroke prediction. The results can provide guidance for researchers and practitioners in choosing the right algorithm to predict stroke risk based on the characteristics of the available datasets.