Malaria is a deadly disease caused by parasites of the genus Plasmodium, spread by mosquitoes, mostly in tropical and subtropical regions. Plasmodium falciparum is dangerous among the five species of this parasite that causes malaria. Correct classification of malaria parasite species is important for diagnosis and treatment. The proposed project aims to classify five different malaria parasite species from the blood sample image of the patient using three different classifiers implemented using MATLAB. Classifiers are compared for accuracy, precision, sensitivity, and specificity. Three algorithms are used as classifiers in this project: SVM (Support Vector Machine), Random Forest (Tree assembly) and KNN (K-Nearest Neighbor). An IoT device is integrated with the results from MATLAB with Arduino controller. The test result from the classifier is transmitted to the patient and the physician using IoT and GSM module. According to the design of this project, when the victim is a carrier of Plasmodium falciparum, upon detection using MATLAB, an audible signal sounds to indicate the severity of the parasite infection to the laboratory assistant.