Abstract
The heart is the central center in the human circulatory system. A malfunction of the heart that is not functioning is a condition in which the heart cannot carry out its duties properly. Selection of features that can reduce a very large dataset and in a data set that is not suitable can use a reduction model. The classification process is strongly influenced by an attribute. Various types of inappropriate redundancy have a negative effect on classification accuracy. Heart disease data was taken from the UCI Machine Learning Repository dataset. In this study, the researchers used the K-Nearest Neighbor (KNN) algorithm where the K-Nearest Neighbor algorithm can classify the results of heart disease accurately. The results are as follows 1.67358 rank one 1.33949 rank two, 1.27260 rank three, 1.2528 rank four, 1.24193 rank last
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