K-Means Clustering of Student Mid-Term and Final Exam Score Data

Nella Ane Br Sitepu; Agnesia Rointan Sijabat; Cindy Rounali Limbong; Lenny Evalina Pasaribu; Einson O.B Nainggolan; Michael Manulang

Authors

Nella Ane Br Sitepu Universitas Katolik Santo Thomas Medan, Fakultas Ilmu Komputer
Agnesia Rointan Sijabat Universitas Katolik Santo Thomas Medan, Fakultas Ilmu Komputer
Cindy Rounali Limbong Universitas Katolik Santo Thomas Medan, Fakultas Ilmu Komputer
Lenny Evalina Pasaribu Universitas Katolik Santo Thomas Medan, Fakultas Ilmu Komputer
Einson O.B Nainggolan Universitas Katolik Santo Thomas Medan, Fakultas Ilmu Komputer
Michael Manulang Universitas Katolik Santo Thomas Medan, Fakultas Ilmu Komputer

Keywords:

Clustering, data mining, algoritma k-means

Abstract

Clustering is a method in data mining that aims to group data based on similar characteristics. This research utilises the k-means clustering algorithm to group students based on their UTS and UAS scores, making it easier for lecturers to identify students' academic abilities. With the application of this method, it is expected to form groups of students who are intelligent, less intelligent, and moderate. In addition, this research also addresses the challenges in observing student grades which are often done manually, resulting in wasted time and effort. Through the k-means clustering approach, this research aims to improve the quality of education by providing insight for education managers in mapping student learning outcomes. The k-means method used includes determining the cluster centre point and calculating the distance between data, which is repeated until convergence is achieved. The results show that this method is effective in identifying student achievement patterns, providing a basis for decision-making to improve academic achievement in higher education.

References

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