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- Fuzzy Inference is one method that can solve the problem of uncertainty in a decision-making or classification well. In inference, fuzzy rules that represent the need of expert knowledge in the relevant fields, so that the classification given decision or be appropriate expert knowledge. However there are times when experts are less able to represent the rules of the appropriate knowledge or knowledge that there is need of too many rules, so we need a method that can generate rules based on the data given expert. At issue troke s disease risk detection, it also occurs because of the research that has been done by taking the direct rule of experts, it turns out less than the maximum accuracy, still 82.89%. Substractive methods Clustering and Fuzzy C-Means (FCM) could generate rules by grouping algorithm, in which the existing training data are grouped in common and the rules of the group raised. Differences in the two methods are in determining the center of the cluster and assign each incoming data which groups. Based on research that has been done, substractive average Clustering membrika better accuracy is 84.46%, while 73.81% FCM. However, in the processing time FCM faster at 16.75 seconds to give an average processing time of 13:02 seconds.

Performance Comparison of Rule Generation Method Substractive Clustering and Fuzzy C-Means Clustering on Sugeno's Inference for Stroke Risk Detection