Penerapan Algoritma Naive Bayes untuk Klasifikasi Demam Berdarah Dengue di RSUD dr. Achmad Darwis

Dengue Hemorrhagic Fever (DHF) is a disease transmitted through the bite of the Aedes Aegypti mosquito. Limapuluh Kota Regency BPS stated that the morbidity rate due to dengue fever was 14.40% per 100,000 population, this figure jumped high from the previous year with a morbidity rate of 3.30% per 100,000 population. The main symptoms of dengue fever are fever that lasts for 2-7 days, pain felt in the muscles and joints accompanied by a rash or no rash, dizziness, and even vomiting blood. Dengue infection can cause various clinical symptoms, ranging from dengue fever, dengue hemorrhagic fever, to dengue shock syndrome. Based on this, there is a need for a classification method that can help and facilitate early diagnosis of dengue fever. The method used is Naive Bayes by classifying dengue positive and dengue negative patients. The aim of this research is to determine the results of the classification of patients suffering from dengue fever, as well as to determine the level of accuracy using the Naive Bayes method. Based on research that has been carried out, the results of the classification of patients are 58 correct and 14 patients classified incorrectly. The accuracy results obtained in this algorithm were quite high, namely 80%, while the sensitivity was 65% and the specificity was 86.5%.