PENERAPAN DATA MINING UNTUK MENENTUKAN STRATEGI PROMOSI UNIVERSITAS PGRI YOGYAKARTA MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING

ANDI WIJANARKO, NIM. 12651082 (2016) PENERAPAN DATA MINING UNTUK MENENTUKAN STRATEGI PROMOSI UNIVERSITAS PGRI YOGYAKARTA MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

New admissions process of PGRI Yogyakarta University students generate data that are highly abundant in the form of student profile data. Based on it some hidden information could be known by doing data processing using that‟s profile‟s data and also obtaining useful information for university. Information that we get could be contribute for university as a consideration to decide new admissions promotion in the next year. This research aims to classify student data‟s into a cluster by utilizing Data Mining process using clustering techniques. The algorithm used for the cluster techniques is K-Means algorithm. K-Means is one method of non-hierarchical clustering of data that can group student data into several clusters based on the similarity of the data, so the data of students who have similar characteristics are grouped into one cluster and that have different characteristics grouped in another cluster. Atributes that used in this study is student‟s country, student‟s major in a high school, and GPA for two semesters with a value above 2,75. Cluster that formed after K-Means Algorithm process is three cluster with the first cluster amounted to 379 student data, second cluster amounted to 68 student data, and the third cluster amounted to 43 student data. Cluster with the highest average GPA is the first cluster. The results of this research are used for a making decision to determine promotion strategy based on clusters formed.

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