Mamdani Fuzzy Expert System for Online Learning to Diagnose Infectious
Diseases
Dublin Core
Title
Mamdani Fuzzy Expert System for Online Learning to Diagnose Infectious
Diseases
Diseases
Subject
Mamdani Fuzzy Expert System for Online Learning to Diagnose Infectious
Diseases
Diseases
Description
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%.
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%.
Creator
Istiadi1
, Emma Budi Sulistiarini2
, Rudy Joegijantoro3
, Anik Vega Vitianingsih4
, Affi Nizar Suksmawati5
, Emma Budi Sulistiarini2
, Rudy Joegijantoro3
, Anik Vega Vitianingsih4
, Affi Nizar Suksmawati5
Publisher
Universitas Widyagama Malang
Date
29-12-2022
Contributor
Fajar bagus W
Format
PDF
Language
Indonesia
Type
Text
Files
Collection
Citation
Istiadi1
, Emma Budi Sulistiarini2
, Rudy Joegijantoro3
, Anik Vega Vitianingsih4
, Affi Nizar Suksmawati5, “Mamdani Fuzzy Expert System for Online Learning to Diagnose Infectious
Diseases,” Repository Horizon University Indonesia, accessed June 6, 2025, https://repository.horizon.ac.id/items/show/9323.
Diseases,” Repository Horizon University Indonesia, accessed June 6, 2025, https://repository.horizon.ac.id/items/show/9323.