<![CDATA[This research aims to address the issue of exponential rule generation in fuzzy rule-based classification systems by developing a hybrid grid partition and rough set method. Fuzzy rule-based classification systems have the potential to construct linguistically understandable models, but a major constraint is the significant increase in the number of rules with a high number of attributes, which can diminish interpretation and classification accuracy. In this study, the grid partition method is utilized to generate fuzzy rules with adaptively adjusted grid structures, thus avoiding exponential rule proliferation. The research encompasses the use of the Iris Flower dataset, rule formation while considering variable precision, and classification accuracy testing. The research findings indicate that the hybrid grid partition and rough set method produces more efficient and accurate fuzzy rules, with a classification accuracy rate of 83.33%. This method also successfully reduces the number of generated rules, making it a promising solution to tackle the issue of exponential rule increase in fuzzy rule-based classification systems. The conclusions of this research can be described based on the findings, discussions and results above are: The application of the rough set method at the beginning of rule formation can reduce the number of condition attributes and the number of redundant objects so that the rule formation process becomes more concise The grid partition method with a grid structure applying adapted techniques produces fuzzy rules that have the potential to be generated. The hybrid grid partition method and rough set method produce classification rules that do not increase exponentially. The number of classification rules generated decreases as the number of condition attributes and the number of objects classified decrease. Fuzzy rules generated by the hybrid method produce a classification accuracy rate of 83.3% with 9 data records and the number of unclassified data is 0.]]>
