K-Means Algorithm for Clustering Students Based on Areas of Expertise (A Case Study)
DOI:
https://doi.org/10.62123/aqila.v1i1.23Keywords:
Students Expertise, K-Means, Clustering, PredictionAbstract
Every student of the Software Engineering Technology study program, the Computer and Informatics Department of the Politeknik Negeri Medan in the lecture process is required to take all the courses in the curriculum. From the courses in the curriculum, there are several subject groups that shape student expertise, such as Software Engineering, System Analyst, Database Administrator, and IT Entrepreneur. It is hoped that this expertise will later be used as a reference by students in carrying out their thesis at the end of lectures. The purpose of this study is to group students based on their respective expertise based on data processing of student course scores related to each skill. The data used is data on student scores for batches of 2020 and 2021 with a range of courses from semester 1 to semester 3. The data is tested by implementing the K-Means algorithm. The results of the tests that have been carried out show the grouping of students based on their expertise, with 7 times the number of iterations. Then, data testing was carried out with the RapidMiner application to get the results of the distribution of cluster members obtained, including 12 students occupying Software Engineer skills, 21 students with System Analyst skills, 5 students with Database Administrator skills, and 31 students with IT Entrepreneur skills, along with the distribution chart. Thus, the K-Means algorithm is quite good at grouping students based on their expertise
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