SISTEM PAKAR DIAGNOSA PENYAKIT TIDAK MENULAR MENGGUNAKAN METODE CASE BASED REASONING

Abstract

Non-communicable diseases (PTM) is a very substantial problem, considering that the pattern of occurrence greatly determines the health status in an area and also the success of improving health status in a country globally (NTT health profile, 2017), the largest increase will occur in middle countries and poor. Based on data from the World Economic Forum April 2015, the potential losses due to non-communicable diseases in Indonesia in the 2012-2030 period reached 4.47 trillion dollars. According to the results of the Basic Health Research (Riskesdas) from the Indonesian Ministry of Health in 2018 showed that an increase in noncommunicable diseases such as kidney and heart. The number of patients with kidney disease based on the diagnosis of doctors aged over 15 years rose from 2.0% in 2013 to 3.8% in 2018. While the number of heart disease sufferers in 2018 reached 19.6%, based on the doctor's diagnosis reached 2, 9% of the number of female heart sufferers is more than 1.6% compared to males reaching 1.3%, this shows that heart sufferers who have not been diagnosed by health workers reach 16.7% of the total population of Indonesia. Lack of information that is known by sufferers about the causes of the disease, symptoms of the disease and the types of diseases that attack the body, can be fatal and even fatal. The purpose of this study is to create an expert system application to diagnose non-communicable diseases by applying a case-based reasoning method to help sufferers diagnose certainty in non-communicable diseases and provide solutions and information to handle diseases that have been detected in the patient's body. From the results of system testing using confusion matrix in heart disease, the sensitivity value is 100%, the specificity is 62%, the accuracy is 75%, and in kidney disease the sensitivity value is 100%, the specificity is 59%, the accuracy is 72%. With the value of the system test results obtained, the authors conclude that the system is able to diagnose diseases of the heart and kidneys.