<![CDATA[The high mortality rate from heart disease in Indonesia is largely caused by delayed diagnosis, which stems from low public awareness regarding early screenings. Limited access to accurate health information exacerbates this situation, creating a critical gap between disease onset and medical intervention. This research proposes the development of a classification model for the early detection of heart disease using the Fuzzy K-Nearest Neighbor (Fuzzy KNN) algorithm. This method was chosen for its ability to indicate whether an individual has heart disease and to manage the uncertainty within symptom data, aiming to provide an initial recommendation that can increase public awareness. The model's performance was rigorously evaluated using k-fold cross-validation to ensure valid results. The findings show a significant trade-off. At a k-value of 9, the model achieved a recall of 0.64. However, this was accompanied by a precision of 0.23 and an average accuracy of approximately 0.75. Nevertheless, Fuzzy KNN shows significant potential as an early detection tool due to its strong capability in minimizing the risk of missed patients (false negatives).]]>
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