Classification of Diabetes Disease Using Naive Bayes Case Study : Siti Khadijah Hospital - Ida Lailatul Qurnia; Eko Prasetyo; Rifki Fahrial Zainal (JEECS (Journal of Electrical Engineering and Computer Sciences), 2016)
Less knowledge about symptoms and how to treat the disease of diabetes mellitus as well as a number of specialist diabetes mellitus which is still limited is one of the causes of the growing number of people affected by the disease. Diabetes disease classification system development aims to predict the type of diabetes patient or user who already suffer from diabetes mellitus. Therefore this system is made to diagnose the type of diabetes through laboratory test results, namely in the form of gender, age, disease history, family history, systolic, diastolic tensi tensi, temperature, pulse, blood sugar, fasting blood sugar JPP and Random blood sugar. That is by using the method of naive bayes as a method to process data on the patient's diagnosis. Test results of this system indicates that the system is able to predict the type of diabetes in patients, from the amount of data as much as 200 patient data, with an output that is the form of Diabetes Without Complications, Diabetes Type II and Normal but obtained the lowest accuracy rating of 39% and the value of the highest accuracy of 80%.