In today's highly competitive business environment, companies have realized that the key to survival and success is effectively utilizing the data obtained from various business processes. By processing this data and identifying trends and patterns, businesses can gain valuable insights that inform their decision-making process. However, decision-making can be complex and difficult due to the many factors that must be considered. This has led to the development of fuzzy datasets, which help to narrow down the scope of trends and patterns in order to obtain more accurate decisions. This paper aims to address the challenges posed by fuzzy datasets by improving the accuracy and precision of the decision-making process through association rule mining. Additionally, the paper tackles the issues of entity relationship modeling in database tables and proposes mechanisms to overcome the challenges posed by ER modeling. The study also seeks to enhance existing algorithm resulting in a new algorithm that standardizes the process of finding the most appropriate result from tables comprising of fuzzy data. Overall, the proposed study aims to bring maturity to the decision-making process by improving the accuracy of fuzzy datasets and standardizing the algorithms used to process them. The aim is to create a new approach that can effectively mine patterns and relationships between different variables in ER models related to cybercrime. By incorporating fuzzy logic, the algorithm can handle the uncertainty and imprecision that often exists in real-world cybercrime data. Overall, the study aims to improve our ability to detect and prevent cybercrime by developing a more sophisticated and nuanced approach to analyzing data