Perbandingan Metode CBR dan Dempster-Shafer pada Sistem Pakar Terintegrasi Layanan Kesehatan

Abstract

Infectious disease is a very dangerous disease with a high mortality rate. Delays in handling the spread of an infectious disease can be minimized using an expert system. This study uses an expert system as a disease consulting service that is integrated with the health care system. Integration with the health care system is used for the knowledge acquisition process. The knowledge base on the expert system uses patient medical record data obtained through the health care system. The expert system can diagnose infectious diseases of sore throat (Pharyngitis), diphtheria, dengue fever, Typhoid fever, tuberculosis, and leprosy. The knowledge acquisition process produces 43 symptoms. These symptoms are used to diagnose new cases using Case-Based Reasoning (CBR) and Dempster-Shafer methods. In the CBR method, the similarity measurement process is determined by comparing the K-Nearest Neighbor, Minkowski Distance, and 3W-Jaccard similarity measurement methods. The expert system obtains accuracy values ​​for the CBR K-Nearest Neighbor, CBR Minkowski Distance, and CBR 3W-Jaccard methods at a threshold of 70%, respectively 65.71%, 80%, and 85.71%. The average length of retrieve time required for each similarity method is 0.083s, 0.107s, and 6.325s, respectively. While the diagnosis of disease with Dempster-Shafer gets an accuracy value of 88.57%.