B-spline curve interpolation modeling using intuitionistic alpha cut for uncertainty data

For the uncertainty data problem, traditional methods are incapable of handling them that caused inaccuracy in data analysis and prediction. Uncertainty data often occurs during the data collection phase and cannot be used to generate geometric models directly. Therefore, this paper discusses the B-Spline curve interpolation modeling using intuitionistic alpha cut for the uncertainty data. Fuzzy set theory, intuitionistic fuzzy set, and geometry modeling are used and integrated with one each other to solve the data with uncertainty and generate the mathematical model. In detail, there are three main processes employed, the first of which is the fuzzy set theory applied to defined uncertainty data followed by the intuitionistic fuzzy set, which is used to consider the membership and non-membership values of the alpha before proceeding to the fuzzification and defuzzification process. Next, geometric modeling is utilised to construct mathematical geometry in the form of curves, which is the B-spline curve interpolation function. As a result, various numerical examples, as well as their algorithms for generating the desired curve, are shown.