<![CDATA[Heart disease is one of the leading causes of death worldwide and requires prompt and accurate diagnosis to reduce the risk of severe complications. Conventional diagnostic processes that rely heavily on manual analysis often take considerable time and may lead to errors, especially when handling large and complex patient data. Therefore, an intelligent system is needed to assist in the diagnostic process in a more efficient and automated manner. This study aims to develop an intelligent expert system based on machine learning for heart disease diagnosis using the Random Forest algorithm. The dataset used in this study consists of 1,025 patient records with 13 attributes representing various health conditions. The data were divided into training and testing sets using an 80:20 ratio, resulting in 820 training data and 205 testing data. The research methodology includes data preprocessing, model training using the Random Forest algorithm, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The experimental results show that the proposed model achieves an accuracy of 94.63%, with precision and recall values ranging from 0.93 to 0.96, and an F1-score of 0.95. Furthermore, the confusion matrix analysis indicates that the model correctly classified 95 negative cases and 99 positive cases, with a relatively low misclassification rate. These results demonstrate that the Random Forest algorithm has strong capability in identifying data patterns and producing stable predictions. Therefore, the developed system can be utilized as a supportive tool for early detection of heart disease in a more effective, accurate, and efficient manner]]>
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