Clustering Of Prospective Students At Universitas Almuslim Using K-Means Clustering
Keywords:
Clustering, K-Means, Universitas Almuslim, Google Colab,, Academic InterestsAbstract
The diverse academic backgrounds of prospective new students at Almuslim University often present challenges in determining the appropriate field of study. Determining the field of study is crucial because choosing the wrong study program can impact the learning process and the development of students' potential during their studies. Selecting the right field of study allows students to learn optimally and prepare themselves for the world of work according to their interests and abilities. It can also assist the university in recommending study programs with appropriate fields of study to develop a more targeted admission strategy. This study applies the clustering method with the K-Means algorithm to help group prospective students into two fields of study: science and social science. This field grouping is based on 1000 prospective new students' data with attributes of diploma grades (Mathematics, Science, Indonesian, and Social Studies), test scores, and field interests. The analysis process carried out using the K-Means clustering method on Google Colab resulted in a calculation of 17 iterations, C1 (science) with a total of 518 people who have higher interests and values in the field of science, and C2 (social) with a total of 482 people who have higher interests and values in the field of social. This division confirms that the K-Means algorithm is able to group data based on the characteristics in the dataset. With these results, K-Means Clustering is proven effective in grouping prospective students of Almuslim University based on their academic background and interests
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