Application of K-Means Clustering Method in Semester I Value Data Clustering Until Semester IV

Mardin Aprianto; Liza Yulianti; Dewi Suranti

doi:10.47679/njca.v1i1.1

Published : 23 January 2022

Application of K-Means Clustering Method in Semester I Value Data Clustering Until Semester IV

Mardin Aprianto
Liza Yulianti
Dewi Suranti

Vol. 1 No. 1 (2022) | Pages : 1-6

DOI: 10.47679/njca.v1i1.1 Reader : 86 times PDF Download : 59 times

Abstract

In the Informatics Study Program there is a concentration which is divided into 2 parts which students will choose when entering semester V. The problem that often occurs is that some students sometimes choose concentration instead of seeing the results of the scores obtained during semesters I to IV, and there are some who choose majors because he followed his friends. This is what causes students to often be unprepared at the end of the semester. Therefore, it is necessary to analyze the value of semester I to semester IV obtained by students to help recommend to students which concentration to choose. The analysis is carried out by applying the K-Means Clustering Method, which will produce 2 groups, namely Software Engineering and Network Infrastructure. The K-Means Clustering method has the ability to group large amounts of data with relatively fast and efficient computation time. The Semester I to Semester IV Value Data Grouping application was made using Visual Basic .Net programming language and SQL Server 2008 database by applying one of the data mining methods used was K-Means Clustering. The grouping is done based on student score data obtained from Semester I to Semester IV (data attached). From this data, grouping is done into 2 groups, namely Cluster C1 Software Engineering and Cluster C2 Network Infrastructure. With this application, it can assist the Study Program in providing recommendations and also material for consideration to students in choosing a major whether Software Engineering or Network Infrastructure. Based on the results of the tests that have been carried out, the application of the K-Means Clustering Method in Grouping Semester Value Data has been successfully carried out, and can provide information based on 2 groups, namely Cluster C1 (Software Engineering) and Cluster C2 (Network Infrastructure), and the functionality of the application has been running. as expected.

Data Availability

All data generated during this study are included in this published article [and its supplementary information files].

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Author Information

Mardin Aprianto

Information Study Program, Faculty Of Computer Science, Dehasen University, Bengkulu, Indonesia.

Email: edu.buletin@gmail.com

Liza Yulianti

Information Study Program, Faculty Of Computer Science, Dehasen University, Bengkulu, Indonesia.

Email: edu.buletin@gmail.com

Dewi Suranti

Information Study Program, Faculty Of Computer Science, Dehasen University, Bengkulu, Indonesia.

Email: edu.buletin@gmail.com

Article History

Submitted : 23 January 2022
Revised : 24 January 2022
Published : 23 January 2022

How to Cite This

Aprianto, M., Yulianti, L., & Suranti, D. (2022). Application of K-Means Clustering Method in Semester I Value Data Clustering Until Semester IV. Nusantara Jurnal of Computer Application, 1(1), 1–6. https://doi.org/10.47679/njca.v1i1.1