Mamdani Fuzzy Expert System for Online Learning to Diagnose Infectious
Diseases

Mamdani Fuzzy Expert System for Online Learning to Diagnose Infectious
Diseases

Mamdani Fuzzy Expert System for Online Learning to Diagnose Infectious
Diseases

E-learning and expert systems can be implemented for learning in the health sector. Through the e-learning system, prospective
health workers can analyze problems by exploring the material in the system. However, material learning alone is less effective,
so case study-based learning using an expert system is needed to strengthen understanding. The research applies an expert
system to online learning to diagnose several infectious diseases. The disease diagnosis process uses the backward chaining
method and the Mamdani fuzzy inference system. The fuzzy Mamdani inference system determines the intensity of disease
severity so that appropriate treatment recommendations can be made. The test findings on 15 test datasets yielded a backward
chaining accuracy value of 100%. Three test scenarios were used to establish the test using the Mamdani fuzzy inference
method. Scenario 1: Testing with the Center of Gravity defuzzification and Fuzzy Mamdani Min inference system Tests
employing the Fuzzy Mamdani Min inference method and center average defuzzification are used in Scenario 2. Scenario 3
involves testing using the Fuzzy Mamdani Product Inference System with Center Average Defuzzification. The average outcome
for the intensity of disease severity utilizing the Fuzzy Mamdani Min inference system with Center of Gravity defuzzification
was greater than that of the two test scenarios that were suggested, which was 49.43%.

Istiadi1
, Emma Budi Sulistiarini2
, Rudy Joegijantoro3
, Anik Vega Vitianingsih4
, Affi Nizar Suksmawati5

Universitas Widyagama Malang

29-12-2022

Fajar bagus W

PDF

Indonesia

Text