<![CDATA[Heart disease is the leading cause of death worldwide and a chronic disease with significant risks. According to the 2022 Basic Health Research (Riskesdas), heart disease cases in Indonesia reached more than 15 million. Many patients are unaware of the early symptoms of heart disease until serious complications such as heart attacks or heart failure develop. To facilitate early detection, a system capable of predicting heart disease risk quickly and accurately is needed. The aim of this research is to create an expert system that can identify heart disease using the Decision Tree (C4.5) classification summary and optimize parameters to achieve the highest level of accuracy. Several important C4.5 algorithm parameters, including criteria, maximum depth, and pruning, as well as the number of folds in Cross-Validation, were optimized. The Kaggle Heart Disease dataset, which has 500 entries and seven characteristics (including one unique attribute), served as the source dataset. Data preprocessing, model training, and accuracy measurements both before and after optimization are all steps in the research process. Comparing the accuracy figures obtained before and after optimization is how the evaluation is conducted. The research shows that the accuracy value was around 70.00% before optimization and increased to 78.00% after optimization.]]>
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