Diabetes is a disease that is often encountered in all circles of society. From the data in the can even occur annually an increase in people with diabetes in the world. Many of the symptoms of the early stages that can be used as a reference to a person in the prediction of symptoms of diabetes or not. Hence the importance of prevention so that the increase going to health and could be spared from the disease diabetes. Based on those reasons here. researchers conducted the study by using the method of decision tree (C4.5) with the aid of rapid miner. The Data obtained is the data classification of the early stages of diabetic patients. To test the training and trials with a ratio of 90:10 using the split data on the application of rapid miner and process the decision tree method in addition add performance to calculate the accuracy. The results can be in the form of a decision tree that can be made a role in the testing dataset. In addition, the results of the evaluation data in the can the level of accuracy of 88.46 % where the amount of the percentage is said to be in the category of good classification.
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