Integration of AI Models and Extreme Programming for Retail Price Prediction and Inventory Optimization
DOI:
https://doi.org/10.33019/jurnalecotipe.v12i2.4576Keywords:
Artificial Intelligence, Extreme Programming (XP), Price Forecasting, Retail Management, Stock Optimization.Abstract
Food prices in modern retail are highly volatile and complex inventory management is often an obstacle to maintaining operational efficiency. The research developed Smart Retail AI, an artificial intelligence-based application that integrates Long Short-Term Memory (LSTM) for price prediction and Extreme Gradient Boosting (XGBoost) for stock optimization. The software development method uses the Agile Extreme Programming (XP) approach, which emphasizes rapid iteration, user engagement, and continuous testing. The test results showed that all application features worked according to the specifications through Black Box Testing, while the usability test using the System Usability Scale (SUS) resulted in an average score of 87 (Excellent category). These findings confirm that the app has high reliability and an excellent level of ease of use. The novelty of the research lies in the direct integration of AI-based predictive models into real operational retail applications with the XP cycle, thus bridging the gap between algorithmic research and practical application. Overall, Smart Retail AI contributes to improving decision-making efficiency, operational responsiveness, and business sustainability in the modern retail ecosystem.
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