Mobile-Based Drug Inventory Management And Prediction System Using Multiple Linear Regression (Case Study: Idola Bunda Pharmacy)

Abstract

Manual drug inventory recording can delay recapitulation, increase recording errors, and make it difficult to estimate stock requirements for the next period. This study aimed to develop a mobile-based drug inventory management and prediction system for Idola Bunda Pharmacy and to apply Multiple Linear Regression to estimate inventory from historical data. A Research and Development design with a quantitative approach was employed using the Rapid Application Development model. Data were collected through observation, interviews, literature review, and transaction records covering purchases, sales, and inventory from January to December 2025. The system was implemented with PHP, MySQL, and Progressive Web Application technology. Sales and purchases were treated as independent variables, while inventory was the dependent variable. Testing with Woods Peppermint Syrup produced a January 2026 inventory prediction of 22 bottles compared with an actual value of 26 bottles. The Mean Absolute Percentage Error was 15.38%, which falls within the good prediction category, and black-box testing indicated that the main system functions operated according to the expected scenarios. These findings show that integrating transaction management and predictive analysis in a mobile-accessible application can support more structured inventory control and provide practical information for procurement decisions intended to reduce overstock and stockout risks.