Implementation of Data Mining for Predicting Student Graduation Using the K-Nearest Neighbor Algorithm at Jambi Muhammadiyah University

Shandy Amandha; Hetty Rohayani; Kevin Kurniawansyah

doi:10.24014/ijaidm.v7i1.26150

Implementation of Data Mining for Predicting Student Graduation Using the K-Nearest Neighbor Algorithm at Jambi Muhammadiyah University

Shandy Amandha, Hetty Rohayani, Kevin Kurniawansyah

Abstract

Graduation is one of the assessment items in the accreditation process of a tertiary institution. So that if students graduate on time, it will help assess the accreditation of a tertiary institution. The method used is K-Nearest Neighbor (K-NN). This method is used to classify objects based on learning data closest to the object. This research aims to predict the graduation of Jambi Muhammadiyah University students, whether it is worth graduating on time or not graduating on time. In research using K-NN to predict student graduation, the results were that the K-NN approach in this study produced an accuracy value of 93.33%. The result is testing with a value of K = 5 using 50 training data for Jambi Muhammadiyah University students who have graduated in 2022; then data testing is tested with training data that has been tested before, 4 students who graduated not on time and who graduated on time were 46 students.

Keywords

Classification; Data Mining; Graduation; Higher Education; K-Nearest Neighbor

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DOI: http://dx.doi.org/10.24014/ijaidm.v7i1.26150

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