Purpose: This study aims to develop a mobile web-based inventory management system for Toko Percetakan Diraza by integrating the Fuzzy Sugeno method to improve stock prediction accuracy and reduce errors caused by manual record-keeping, which previously led to stock shortages, overstock, and inefficient monitoring.

Research Methodology: The research used inventory and sales data from January to June 2025, consisting of 229 product records across six categories. The system applied the first-order Fuzzy Sugeno method with two input variables: inventory level and sales data. Development followed the Waterfall model using PHP, MySQL, and Bootstrap. Validation was conducted through manual calculation comparison and black-box testing.

Results:The system calculation results are consistent with the manual Fuzzy Sugeno computations. For the sample product “Ordinary Map B” (inventory: 36 units; sales: 31 units), the system generated a stock demand prediction of 23 units, which corresponds to the manual calculation results. The implementation of the system enhances stock monitoring efficiency and minimizes the risk of manual recording errors.

Conclusions: The Fuzzy Sugeno method can be effectively implemented in a mobile web-based system to provide accurate and adaptive stock predictions for small businesses.

Limitations: The study only used two main inputs inventory levels and sales data without considering additional factors such as lead time or seasonal demand variations.

Contributions: This study contributes to the field of inventory management and decision-support systems by demonstrating that Fuzzy Sugeno can effectively support small businesses, especially printing stores, in predicting stock needs and improving operational efficiency.