KOMPARASI METODE SUPPORT VECTOR MACHINE DAN NAÏVE BAYES DALAM KLASIFIKASI PELUANG PENYAKIT SERANGAN JANTUNG - Musthofa Galih Pradana; Pujo Hari Saputro; Dhina Puspasari Wijaya (Universitas Alma Ata, 2022)
The death rate in the world per year is 17.9 million due to cardiovascular disease, including heart and blood vessel disorders. This needs to be given more attention to anticipate the possible risk of a heart attack. One of the contributions in the field of technology to provide useful information about the risk of heart disease is by using a data processing approach or data mining technique by classifying the vulnerability to heart disease risk. The classification method used is Support Vector Machine and Naïve Bayes. The classification method will be carried out in a comparative process and the method that has the best accuracy will be sought. The scenarios used are 2 test scenarios, namely dividing the training data by 20% in scenario 1 and 40% in scenario 2. The final results of the research obtained are the best accuracy in the Support Vector Machine with scenario 1 of 87%.