Implementasi Metode Forward Chaining dan Certainty Factor Pada Sistem Pakar Diagnosis Penyakit Sinusitis

Abstract

Sinusitis is similar to symptoms of minor illnesses such as runny nose, cough, and headache. Mild symptoms cause people to ignore these symptoms often. Besides, limited costs and doctor's practice hours make consultations difficult. Signs of infection that are not treated quickly can cause complications, and the infection can spread to the eye sockets or the brain. One of the ways to diagnose sinusitis early is to use an expert system so that in this study implemented the Certainty Factor and forward chaning methods to create a system that can diagnose sinusitis according to the symptoms felt and provide information about the disease and how to treat sinusitis symptoms early. Forward chaining is used as an inference method, and the certainty factor is used to calculate the level of probability of disease based on the expert's belief value and the symptoms of sinusitis selected by the user. The data used is disease data consisting of four types of sinusitis and fifteen symptoms. Based on the results of black box testing, the system that has been built functions well as expected. Expert systems in diagnosing have an accuracy of 70%.