New Method in SEM Analysis Using the Apriori Algorithm to Accelerate the Goodness of Fit Model

This research aims to develop a new method in Structural Equation Modelling (SEM) analysis using the Apriori algorithm to accelerate the achievement of Goodness ofFitmodels, focusing on traditional retail purchasing decision models in Indonesia, especially in Palembang. SEM will be used to model causal relationships between variables that influence purchasing decisions in traditional retail. However, the Goodness of Fit model testing process takes a long time due to the complexity of the model. Therefore, this research uses the Apriori algorithm to filter variables that have a significant relationship in traditional retail purchasing decision models to reduce model complexity and speed up Goodness of Fit calculations. There are two stages in the research. First, the Apriori algorithm identifies frequent item sets that frequently appear among variables influencing traditional retail consumer purchasing decisions, such as product, price, and location. This pattern becomes the basis for SEM modeling, focusing on selected variables and, in the second stage, measuring the Goodness of Fit of the SEM model, namely GFI, RMSEA, AGFI, NFI, and CFI, to evaluate the suitability of the model which explains the factors that support traditional retail purchasing decisions in Palembang. The practical implications of this research are significant, as it provides a more efficient and effective method for modeling and understanding consumer behavior in the context of traditional retail. Based on other studies, if this research uses a conventional SEM approach, it does not meet the cut-off value of Goodness of Fit. Meanwhile, the results of the proposed method, namely combining Apriori into SEM, called APR-SEM, obtained a significant Goodness of Fit evaluation. The model coefficient of determination value from APR-SEM is R2 0.71, higher than the conventional model, R2 0.52. This method effectively simplifies the SEM model by identifying the most relevant relationships, thereby providing a clearer understanding of the critical factors influencing purchasing decisions in traditional retail in Palembang City.