NEW STUDENT CLUSTERIZATION BASED ON NEW STUDENT ADMISSION USING DATA MINING METHOD

Abstract

The process of admitting new students to the Faculty of Information Technology (FTI) at Universitas Budi Luhur produces a large amount of student data in the form of student profile data and other data. This happens causing a buildup of new student data, thus affecting the search for information on that data. This study aims to classify regular undergraduate admissions data at the Faculty of Information Technology (FTI) Universitas Budi Luhur by utilizing the data mining process using the clustering technique. The algorithm used for clustering is the K-Means algorithm. K-Means is a non-hierarchical clustering data method that can group student data into several clusters based on the similarity of the data, so that student data with the same characteristics is grouped in one cluster and those with different characteristics are grouped in another cluster. An implementation using RapidMiner is used to help find accurate values. This research produced a description of what clusters were formed from data on regular undergraduate admissions at the Faculty of Information Technology (FTI) at Universitas Budi Luhur. This will help recommend decision-making to determine the marketing promotion strategy for each study program at Universitas Budi Luhur. Based on the results of the K-Means algorithm cluster, it can also be seen which majors or study programs are of interest in each school from which new students come.

Keywords

• k-means algorithm
• data mining
• new student clustering