<![CDATA[Asthma is a disease characterized by chronic inflammation of the respiratory system with a relatively high recurrence rate in Indonesia. This condition highlights the need for a data-driven approach to support a more objective and systematic disease classification process. This study aims to classify asthma by applying the Decision Tree algorithm, which is implemented using RapidMiner software as an analytical tool. This research adopts the CRISP-DM framework as the research workflow, encompassing the stages of problem understanding, data understanding, data preparation, modeling, evaluation, and deployment. The dataset used is secondary data obtained from the Kaggle platform, with an initial total of 10,000 patient records. During the data preparation stage, data cleaning, transformation, feature selection, and class imbalance handling were performed, resulting in 4,866 data instances used for modeling. The evaluation results indicate that the Decision Tree model achieved an accuracy of 93.63%, with a precision value of 89.72% and a recall value of 98.56% for the asthma class. In addition to its strong performance, the resulting model is easily interpretable through clear decision rules, making it suitable as a decision-support tool for asthma disease classification.]]>
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