<![CDATA[he distribution of subsidized Liquefied Petroleum Gas (LPG) at pangkalan levels is still prone to fraud, such as discrepancies between stock and distribution volumes, sales above the maximum retail price (HET), and undelivered distributions, all of which are still mostly supervised manually. These conditions make it difficult to quickly and objectively detect the presence of a fraudulent distribution pattern. This research develops a Mamdani Fuzzy Logic-based fraud detection engine that functions as a real-time monitoring system for subsidized LPG gas distribution. The system was developed using the Waterfall method, which includes requirements analysis, design, implementation, and testing, and was built with Laravel, React.js, and Inertia.js as a Single Page Application (SPA) architecture. Fraud-indication analysis involves three fuzzy input variables — remaining stock, daily distribution volume, and the Purchase Order (PO) submitted to the agent. They are processed through fuzzification, a 27-rule rule base, MIN-MAX inference, and Centroid of Area defuzzification. The output is three categories: safe (aman), alert (waspada), and irregular/indicative of fraud (menyimpang). Real transaction data from Pangkalan Gas Asmalia for May 2026 shows that a daily distribution of 18 cylinders against a daily quota of 46 cylinders resulted in a crisp output of 16.20, which falls under the Safe category and is in line with actual field conditions. This system would enable the pangkalan operators to identify early fraud signals, have more structured and objective decisionmaking, and enhance the accountability of subsidized LPG distribution.]]>
