<![CDATA[Hypertension is a non-communicable disease that requires accurate detection and severity assessment to support decision-making. This study aims to develop a web-based application for predicting the severity of hypertension patients by applying the K-Nearest Neighbor (KNN) and Naïve Bayes Classifier (NBC) algorithms. The dataset consists of 80 records with 17 predictor attributes and 1 class attribute representing the severity of hypertension. The research stages include data preprocessing, implementation of the KNN and NBC algorithms, accuracy testing, selection of the algorithm with the best performance, and development of a web-based prediction application. The results showed that the KNN algorithm achieved an accuracy of 81.25%, while the NBC algorithm achieved an accuracy of 83.75%. Based on the comparison, NBC was selected for implementation in the application because it achieved higher accuracy. The developed application allows users to enter patient data based on the available attributes, after which the system performs classification using NBC to predict the severity of hypertension. The results of this study provide a computerized application that can assist in predicting the severity of hypertension patients quickly and efficiently.]]>
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