Result: Implementation Fuzzy Mamdani Algorithm To Predict Web Based Inventory

Mamdani's fuzzy algorithm enables the use of fuzzy logic to overcome the uncertainties and ambiguities associated with inventory predictions. This study describes implementing the Mamdani fuzzy algorithm to predict web-based inventory. Fuzzy algorithms allow specific reasons to deal with the uncertainties and ambiguities associated with inventory predictions. We collect relevant inventory data, including input variables such as the number of items sold, customer demand, and other factors that affect inventory. We also use historical inventory data to create the Mamdani fuzzy model. We implement fuzzification by specifying a linguistic variable for each input variable and converting the numeric to a linguistic value using a predefined membership function, then build a  rule-based fuzzy Mamdani which includes a set of rules that relate language values as input variables with linguistic values of output variables., i.e., inventory prediction. After the inference process, we apply defuzzification using the Mamdani method to convert the linguistic values of the output variables into numeric values that can be used in practice. Through this implementation, we managed to integrate the power of Mamdani's fuzzy algorithm with web technology so that users can access the inventory prediction system online. This system can assist inventory managers in making better decisions in production planning, stock procurement, and delivery schedule. This system is expected to increase efficiency and optimize inventory availability in a rapidly changing business environment.