Implementation of the Single Moving Average Algorithm for Stock Prediction in a Web-Based POS System at My Perfume
Keywords:
Keywords: Point of Sale, Simple Moving Average, Stock Forecasting, MAPE, Next.js.Abstract
In the competitive retail industry, inventory management is a determining factor in business sustainability. The main problem faced by the “My Perfume” retail store is uncertainty in stock management due to reliance on manual recording methods and the owner's intuition. This situation often leads to operational inefficiencies in the form of overstocking, which burdens working capital, and stockouts of best-selling products, which result in lost sales. This study aims to overcome these problems by designing and implementing a Single Moving Average (SMA) forecasting algorithm into a web-based Point of Sale (POS) system. The system was developed using a systematic Waterfall model, utilizing Modern Web Architecture technology consisting of Next.js for a responsive user interface (frontend) and Express.js for server logic management (backend). The SMA algorithm was chosen for its ability to dampen fluctuations in stationary sales data to produce stable prediction trends. The system's performance was evaluated through accuracy testing using the Mean Absolute Percentage Error (MAPE) method on historical sales transaction data. The test results show that the system is capable of predicting stock requirements with a high degree of precision, producing an average error value of only 3.33%. Referring to forecasting capability evaluation standards, this value is classified as Highly Accurate. These findings prove that integrating forecasting methods into POS systems effectively helps business owners make precise procurement decisions, minimize the risk of losses, and support business management transformation towards a data-driven approach.
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