Detection of damage contained in ATM Machines carried out by employees still has drawback, especially if there are new employees who are still confused about finding damage to the ATM machine. This study aims to build an expert system for diagnosing damage found in ATM machines at PT Advantage SCM Medan which is equipped with solutions for indicated damage. This system was built using the Naive Bayes method where this method will be able to solve this problem, this is because it is able to predict opportunities in the past and this method only requires small training data to determine the estimated parameters it need in the classification process. The design of an expert system for diagnosing damage contained in this ATM machine has 29 symptoms and 6 damages. This expert system is designed using the MySQL database and the PHP programming language. The results of this study are in the form of an accuracy of 90% which is calculated from the comparison obtained between manual data and data in the system.
                        
                        
                        
                        
                            
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