Malaria is an infectious disease caused by a plasmodium parasite transmitted by the female Anopheles mosquito. Malaria disease has four types of parasites, namely Plasmodium falciparum, Plasmodium vivax, Plasmodium malariae and Plasmodium ovale. The standard method of malaria diagnosis is by conducting microscopic examination or laboratory test and Rapid Diagnostic Test (RDT). Laboratory tests have a high risk of human error whereas RDT has weaknesses in temperature sensitivity, genetic variation and antigen resistance in the bloodstream. This research offers a classification system of malaria disease by applying the Hu moment and Support vector Machine (SVM) method with 3 types of malaria parasitic objects, namely falciparum, Malariae and vivax. The classification system uses 3 SVM models, namely linear SVM, polynomial SVM and Gaussian SVM with the Falciparum class as a positive data and malariae and vivax as negative data. The best classification outcome is on the Gaussian SVM model with 96.67% sensitivity and 90% specificity. The mean accuracy of the Gaussian SVM model with a 5-fold cross Validation 90 image sample which is divided into 3 classes is 86.66%.