Evaluations of parameters importance based on human sensory data and Bayesian rough set model

In order to realize a well-designed product that appeals to costumers, the product not only should meet the physical requirements of consumers but also has to satisfy their affective needs. Packaging design is an important factor of purchasing decision of consumers. Several research was explained the impact of packaging design to the children and their parents with statistical ways, but there are few approach to consider multi-values data set. This study propose Bayesian Rough Set model to deal some uncertainties and incomplete data in classification data. Bayesian Rough Set method has strength to solve vagueness in human sensory data with probabilistic approximation to identify the relation rules between human perception and product packaging design. This is also useful to handle heterogeneous population, contradictive between generalization versus customization, and uncertainty — inconsistency in market segmentation. For the case study in this research, we will investigate the effect of packaging design on luxury food (ex, chocolate, cake and so on) and instant food packaging preferences and its ability to influence costumers' buyer decision in-store. At the final result, the proposed method get better result than conventional method (Rough Set) as followed: 1) The proposed method get accuracy improvement rather than conventional method (Rough Set) as many as 12.07%, and confidence level as big as 10.61% 2) The proposed method also get calculation time improvement rather than conventional method (Rough Set), which is 81.25%.