Implementasi Kinerja Metode Fuzzy Sugeno Untuk Mendeteksi Penyakit Diabetes

Abstract

Diabetes Mellitus (DM) is a chronic disease caused by decreased insulin secretion, resulting in an increase in blood glucose levels in humans. This disease is still difficult to cure until now. Furthermore, diabetes is the main trigger for blindness, heart and kidney disease which can cause premature death almost all over the world.

The problem faced is because of this large number, of course, every hospital and even the existing clinic needs a system that can help them quickly determine the results of all diabetes tests and classify which patients have the risk of diabetes, prediabetes even those affected by hypoglycemia or very low blood glucose levels. The main advantage of the fuzzy method is its ability to overcome uncertainty in the diagnosis of diabetes.

Expert systems with the fuzzy method can handle varying variations in symptoms and severity by assigning weight or degree of membership to each symptom or condition.This allows the expert system to generate more accurate diagnoses based on the severity of the patient's symptoms and condition.

The input variables used consisted of fasting blood sugar levels, sugar levels after meals, blood pressure, and body mass index. The output is in the form of detection results of positive and negative patients with diabetes.

In the testing stage, the researcher performs calculations manually and implements the performance of the Fuzzy Sugeno method using the Java programming language. Where the data used, sourced from the PT. Primary. It is hoped that the output of this study will be an accredited journal.