Kombinasi Metode Certainty Factor dan Fuzzy Tsukamoto dalam Pradiagnosa Penyakit Gagal Ginjal Kronis

Abstract

Expert systems aim to combine human knowledge with systems, that is, so that computers can solve problems in the same way that experts usually do. Expert systems can also be used in diagnosing disease to determine the type of disease suffered as an initial diagnosis based on the symptoms to be followed up. In this study, the method used to develop an expert system for chronic kidney failure was using the Fuzzy Tsukamoto and Certainty Factor methods. The data search process starts from the symptoms experienced by the user and lab results of anemia, creatinine and eGFR then the final results obtained from this study are an Expert System Application for pre-diagnosing Kidney Disease with the Tsukamoto Method and Certainty Factor. The results obtained from this study, namely the certainty factor method obtained a patient's disease confidence level of 99.48% where according to these results according to experts and with the Fuzzy Tsukamoto method the results obtained for the stage of chronic kidney failure were 73.9 where these results were included in the VV High Risk category.