Improving the Regional Grouping Model for Students of SMK Muthia Harapan Using K-Means Clustering Algorithm

Salma Nur Fikriani; Ade Irma  Purnamasari; Agus Bahtiar; Edi Wahyudin

doi:10.59934/jaiea.v4i3.955

Authors

Salma Nur Fikriani STMIK IKMI Cirebon
Ade Irma Purnamasari STMIK IKMI Cirebon
Agus Bahtiar STMIK IKMI Cirebon
Edi Wahyudin STMIK IKMI Cirebon

DOI:

https://doi.org/10.59934/jaiea.v4i3.955

Keywords:

Data Mining, K-Means, Knowledge Discovery in Database, Student Home Regions, SMK Muthia Harapan Cicalengka

Abstract

Education is an important aspect in human life to improve and develop self-potential. The rapid development of technology has increased the need for fast, accurate, and efficients information, including in the world of education. One of the challenges faced by SMK Muthia Harapan Cicalengka is the accumulation of student data every year. This makes it difficult to identify student data based on region of origin. This research aims to apply data mining using the K-Means Clustering method to group student data with similar characteristics. The method used in this research is Knowledge Discovery in Database (KDD) which includes the stages of data cleaning, data transformation, data mining, and evaluation. The implementation og K-Means Clustering is done using RapidMiner with attributes such as Name, Village, Department, and school of origin. The purpose of this research is to provide a targeted and strategic overview of areas that can have a significant impact on the supply of students each year. The result show that student data can be grouped into two clusters. Cluster 0 consists of 254 items and cluster 1 consists of 254 items, with a Davies-Bouldin Index (DBI) value of 0.549.

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