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All Journal Jurnal Komputer Indonesia (JU-KOMI) "Journal of Data Science
Agnesia Rointan Sijabat
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Automotive Engineering Computer Science & IT Electrical & Electronics Engineering Engineering Library & Information Science Mechanical Engineering

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K-Means Clustering of Student Mid-Term and Final Exam Data Nella Ane Br Sitepu; Agnesia Rointan Sijabat; Cindy Rounali Limbong; Lenny Evalina Pasaribu; Einson O.B Nainggolan; Michael Manulang
Jurnal Komputer Indonesia (Ju-Komi) Vol. 3 No. 01 (2024): Jurnal Komputer Indonesia (JU-KOMI), Oktober 2024
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-komi.v3i01.593

Abstract

This study examines the use of the k-means clustering method in grouping students based on UAS and UTS scores to identify patterns of academic achievement. Clustering is an effective data mining technique for grouping data based on similar characteristics. By applying the k-means algorithm, this study aims to make it easier for lecturers to identify student abilities, so that they can provide appropriate support to those who need help. Data were taken from UTS and UAS scores of students at a university in Indonesia, and the results of the analysis showed that k-means clustering can group students according to their level of achievement. These findings are expected to help in the development of more effective teaching strategies and interventions, improving the quality of education and overall academic performance of students.
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
Journal Of Data Science Vol. 2 No. 02 (2024): Journal Of Data Science, September 2024
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v2i02.5417

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.
Co-Authors Cindy Rounali Limbong Einson O.B Nainggolan Lenny Evalina Pasaribu Michael Manulang Nella Ane Br Sitepu