Klasifikasi Penyakit Menular Seksual Menggunakan Naïve Bayes

Abstract

The number of sufferers of Sexually Transmitted Diseases (STD) in Indonesia is starting to increase. One example of the case is in the city of Malang, in 2014 as many as 466 people suffer from HIV and 14 people suffer from syphilis. According to the Malang city health report, the average patient is 25 to 49 years old. Some are asymptomatic, where the patient does not feel any symptoms and is not even detected until a medical examination is carried out. In detecting this PMS can use information technology. One way is to build an expert system that applies the Naïve Bayes algorithm to help classify the STD you suffer based on the symptoms you feel. The accuracy results obtained in this study amounted to 76.67%.