Klasifikasi Penyakit Cacar Monyet Menggunakan Metode Support Vector Machine

Anugrah, Wendy
Haerani, Elin
Yusra, Yusra
Oktavia, Lola

Journal of Computer System and Informatics (JoSYC)
Vol 5 No 3 (2024): May 2024

Monkey pox is a zoonotic disease caused by the monkey pox virus and this disease is very dangerous. Monkey pox can be detected in advance by using information contained in patient data and applying machine learning techniques. This study aims to classify monkey pox using the Support Vector Machine (SVM) method. This test is carried out using a confusion matrix by comparing the ratio of training data and test data with a ratio of 70:30, 80:20, 90:10 and using the RBF kernel. Based on the test results, the highest ratio results were obtained at 90:10 with the best accuracy value of 65% with SVM parameter testing, namely the value C= 10 and y (gamma)= 1. Based on the results of tests carried out using the Support Vector Machine method, the accuracy values ​​were quite good.