<![CDATA[One of the global health problems today is diabetes, the prevalence of which continues to increase and therefore requires an effective method for its classification. The purpose of this study is the implementation of Orange Data Mining in the classification of diabetes using the Decision Tree method. The selection of these specifications is due to the fact that the resulting model is easy to understand and can be interpreted. The data analyzed were taken from a public diabetes dataset that includes various health attributes. The analysis process was carried out through preprocessing, splitting, and Juvenile Decision Tree model training. The results showed that the Decision Tree model achieved an accuracy of up to 85% with adequate sensitivity and specificity. decision. Therefore, the conclusion of the study is that increasing the accuracy and quality of diabetes classification can be achieved by the Decision Tree method in Orange Data Mining.]]>
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