Expert system for diagnosing EDC cash register malfunctions using the Decision Tree method - Thalia, Novi; Pardede, Akim Manaor Hara; Khair , Husnul (Journal of Intelligent Decision Support System (IDSS), 2025)
Problems with the use of Electronic Data Capture (EDC) machines at Jinjja Chicken Center Point Medan restaurant pose a significant challenge, especially since EDC machines not only function as a means of cashless payment, but also as part of the cashier's operational system. Frequent disruptions include program errors, display errors, total EDC shutdowns, line idles, and “please try again” messages. Until now, the process of reporting and repairing EDC malfunctions has been done manually by submitting a request to the bank, which often makes the diagnosis and repair process slow and inefficient. This is exacerbated by the limited technical information available to restaurant operators when customers experience disruptions. To overcome these problems, this study aims to develop an expert system for diagnosing EDC cash register malfunctions using the Decision Tree method, which is capable of mimicking the way an expert diagnoses EDC problems quickly and accurately. The Decision Tree method was chosen because it is capable of mapping the decision-making process based on attributes or symptoms that arise, to produce a conclusion in the form of the type of malfunction. This system was built using the PHP programming language and run locally using XAMPP as a web server. The research was conducted in a limited setting at the Jinjja Chicken Center Point restaurant in Medan, with five main malfunction categories as variables: Program Error, Display Error, EDC Completely Dead, Line Idle, and Please Try Again. The final result of this system development is expected to provide practical, efficient solutions that approximate the capabilities of an expert, as well as make a real contribution to the utilization of expert system technology to assist in the diagnosis of digital device damage in the service sector.