Prediction of Vocational Students Behaviour using The k-Nearest Neighbor Algorithm

Abstract

This article discusses the implementation of the k-NN algorithm in predicting student behavior. The school is a management unit that has data that correlate with students. All student data is stored in an academic information system that can be processed to predict student behavior. One of the data assessing student behavior is in the database of counseling guidance. Some data that will be processed include attendance, lateness notes of problems, teacher responses, tuition payments, broken home. The sample being tested was the data of 100 vocational students from various classes and various majors and divided into two categories. From this experiment, it can be seen that the most accurate K value is the value of K = 1, 3 and 4. The accuracy of the testing data generated is 94.9%.

An integrative review of computational methods for vocational curriculum, apprenticeship, labor market, and enrollment problems

“…The k-nearest neighbor algorithm is a classification algorithm based on the proximity of data to other data. In VET, KNN is used for the prediction of student behavior [31]. Some of the data that will be processed include attendance, issue lateness notices, instructor responses, tuition payment, and broken houses.…”