Measuring Students' Level of Understanding in Programming Courses with the K-Means Clustering Algorithm

Ninik Tri Hartanti

Abstract


The structured programming course is one of the courses that must be taken by second semester students at Amikom University Yogyakarta. This course is a prerequisite course for the data structure course in the fourth semester, so evaluation materials are needed for the results of the learning process. These results are identical to the student's ability to understand the course material during the learning period, so the evaluation results are needed to determine whether or not students are eligible to continue to the next semester. The purpose of the study was to measure the level of understanding of students in receiving programming material. The research method is carried out by applying the Elbow method and the K-Means clustering algorithm, so that a group will be formed from each class whose category can be determined based on the level of student ability. The clustering process involves 5 criteria, namely assignment scores, UTS, UAS, Final Project and attendance. The number of clusters determined is 3, cluster1 for the highest average value, cluster2 for the average value enough, and cluster3 for the lowest average value. Based on the results of calculations using K-Means clustering, it was found that in cluster1 there were 17 students in class A and B, and 18 students in class C. Then in cluster3 there were 5 students in class A, 7 students in class B and 9 students in class C.

Keywords


K-Means; clustering; evaluation; final project

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References


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DOI: http://dx.doi.org/10.30700/jst.v12i1.1210

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