Integrasi Fuzzy Mamdani dan Certainty Factor pada Sistem Pakar Prediksi Penyakit Jantung

Abstract

Heart disease is a leading cause of death globally; therefore, a rapid and structured initial screening process is essential to minimize the risk of delayed treatment. The PT. XYZ clinic faces challenges in conducting initial heart disease screenings due to limited diagnostic equipment and an identification process that relies heavily on the subjective assessment of medical personnel. This study aims to implement a web-based expert system that integrates the Fuzzy Mamdani method and the Certainty Factor method to assist in the initial screening of heart disease. The Fuzzy Mamdani method processes symptom values ​​into membership degrees and generates alpha-predicate values ​​through fuzzification and inference processes, while the Certainty Factor method calculates the confidence level based on alpha-predicate values ​​and expert-assigned confidence weights for each rule. The system's knowledge base comprises 14 symptoms, three types of heart disease, and 190 rules derived through a knowledge acquisition process with cardiologists. The contribution of this study lies in the implementation of Fuzzy Mamdani and Certainty Factor integration, in which the alpha-predicate value resulting from fuzzy inference is used as the rule activation level and combined with the expert CF to determine the system confidence level for initial heart disease screening. Testing was conducted using 30 case scenarios and evaluated via a confusion matrix. The results demonstrate that all scenarios were successfully classified according to the reference labels, achieving a 100% match rate with the established knowledge base. These findings indicate that the system consistently executes inference and confidence level calculations as a tool for initial heart disease screening, although it is not intended to replace a physician's diagnosis or clinical decision-making.