<![CDATA[Effective inventory management is a crucial aspect of company operations to predict future stock requirements and product demand. This research aims to design and develop a web-based inventory system with sales prediction using Time Series Forecasting algorithms at CV Adio Loop Engineering. The development method used is waterfall with Long Short-Term Memory (LSTM) approach as a prediction model based on historical inventory transaction data. The system has comprehensive features including dashboard with information on total products, purchases, sales, categories, and suppliers; prediction module for selecting products and prediction types (demand/stock) with time estimation; master data for managing categories, products, and suppliers; transaction modules for purchasing, sales, and inventory; stock movement; low stock alerts; inventory reports; and human resource management with login/logout security system. All modules are equipped with complete CRUD functions. Test results show that the system is capable of providing accurate predictions and improving operational efficiency in inventory management and future stock requirement planning.]]>
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