Abstract
One way of assessing essays is to use the STAR (Situation, Task, Action, Result) method. This method aims to classify whether the essay already reflects the values of the situation, task, action, and outcome labels presented by the prospective driving teacher. Multi-label classification refers to a classification task in which each data sample can be classified into multiple categories or labels simultaneously. Various methods have been developed to perform multi-label text classification, such as MLKNN, MLTSVM, and MLP. This research aims to determine the value of the accuracy of the score. Therefore, the Multi-Label K-Nearest Neighbor, or MLKNN, algorithm can be implemented to classify essays with five labels: no star, situation, task, action, and result. With a total dataset of 110, which will be obtained from one of the assessors from the driving teacher. The MLKNN algorithm produces an accuracy rate with an average value of 15.6%.
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