PENERAPAN ALGORITMA K-MEANS CLUSTERING SEBAGAI STRATEGI PROMOSI PENERIMAAN MAHASISWA BARU PADA UNIVERSITAS HASYIM ASY’ARI JOMBANG

  • Imam Mahmudi Program Studi S1 Teknik Informatika, Fakultas Teknologi Informasi, Universitas Hasyim Asy’ari
  • Aries Dwi Indriyanti Program Studi S1 Teknik Informatika, Fakultas Teknologi Informasi, Universitas Hasyim Asy’ari
  • Indana Lazulfa Program Studi S1 Teknik Informatika, Fakultas Teknologi Informasi, Universitas Hasyim Asy’ari

Abstract

Admission of new students at the Hasyim Asy'ari University in Jombang is held every year. To more of the new
students, admissions Committee conducted several promotions as very important early activities such as: online,
banners, brochures, school events, and orally with student roles and Alumni. The number of competition in
finding new student applicants, requiring the University of Hasyim Asy'ari to conduct analysis of several ways
of promotion that have been done so that the promotion strategy can be seen which is more precise and effective.
This research will conduct grouping/clustering of districts or cities based on certain attributes in a Web-based
application. The method used in this study is a K-means clustering algorithm that can group student data into
multiple clusters based on similar attribute agreements. The attributes used are hometown, online, oral,
banners/billboards, brochures and events. At this Peletitian generate a total of 5 clusters (k = 5) with the first
cluster 20 hometown with the most effective promotional media online and oral, the second cluster of 31 Origin
cities with the most effective promotional media oral and online, the third cluster of 4 cities originating with
media The most effective promotion of events and banners, the fourth cluster of 15 hometown with the most
effective promotional media brochures and oral, the fifth cluster 2 hometown with the most effective promotional
media oral and event. The results of this study were used as a recommendation to determine a promotional
strategy based on the promotional media of each cluster formed.
Keywords: Promotion strategy, Admission of New Students, K-means Algorithm, Clustering

Published
2020-04-22