The Implementation of the Denquefever using the dengue naive bayes algorithm

Abstract

Abstract: Dengue hemorrhagic fever (DHF) is a disease that often attacks the almost year-round population of Indonesia, especially when the rainy season comes. The althought it has performed avariety of prevention, including fumigation and cleaning of the flooded places, the disease remains to be one of the big cause of death in Indonesia. On the other hand, the people do not respond to the symptoms of dengue disease.

Research carried out aiming to build a classification application using the Naive Bayes algorithm that can provide DHF disease grade information that they suffered. Naive Bayes algorithm uses a probability model in determining a person’s class of dengue disease. Classification is based on some symptoms of dengue fever such as body temperature, fever days, pulse rate, nausea, vomitting, pain in joints, dizziness, weakness, appetite, bowel movement, reddish spots on the skin, nosebleeds and bleeding gums.

Test results showed that the built application get a good percentage in determining the class of dengue fever and canbe used as early detection to determine whether a person suffering from dengue fever or not.