Data Mining for Determining The Best Cluster Of Student Instagram Account As New Student Admission Influencer

Abstract

Purpose: This study aims to apply the web data extraction method to extract student Instagram account data and the K-Means data mining method to perform clustering automatically to determine the best cluster of students' Instagram accounts as influencers for new student admissions.Design/methodology/approach: This study implemented the web data extraction method to extract student Instagram account data. This study also implemented a data mining method called K-Means to cluster data and the Silhouette Coefficient method to determine the best number of clusters.Findings/result: This study has succeeded in determining the seven best student accounts from 100 accounts that can be used as influencers for new student admissions with the highest Silhouette Score for the number of influencers selected between 5-10, which is 0.608 of the 22 clusters.Originality/value/state of the art: Research related to the determination of the best cluster of students' Instagram accounts as new student admissions influencers using web data extraction and K-Means has never been done in previous studies.