Enhancing the fuzzy inference system using genetic algorithm for predicting the optimum production of a scientific publishing house

As a scientific publishing house, Indonesian Institute of Sciences (LIPI) Press' encountered some problems in publication planning, mainly predicting the optimum production publications. This study aimed to enhance fuzzy inference system (FIS) parameters using genetic algorithm (GA). The enhancements led optimally predict number LIPI Press publications for following year. predictors used were work units, workers, and process duration. dataset covered five years range total Press. Firstly, an expert set up denoted as FIS expert. Next, we performed GA by applying K-fold validation splitting training data testing data. revealed prediction with that composed both population size (30), probability crossover (0.75), mutation (0.01), generations (150). experiment results show our enhanced outperformed approach.