ANALISIS PROFILAKADEMIKALUMNI DENGAN MENGGUNAKAN METODE KLASTERISASI KMEANS PADA STIKOM UYELINDO KUPANG

ANGGRAINI, DEWI (2014) ANALISIS PROFILAKADEMIKALUMNI DENGAN MENGGUNAKAN METODE KLASTERISASI KMEANS PADA STIKOM UYELINDO KUPANG. S2 thesis, UAJY.

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Abstract

With today's technological advances, the need for accurate information is needed in everyday life. The ability of technology to collect and store various types of data far beyond the ability to analyze, summarize and extract knowledge from the data. Decision makers are trying to take advantage of the data set that has been held to gather information useful in making decisions. The college currently required to have the ability to compete by utilizing all its resources. In addition to the resources of facilities, infrastructure, and human, the data and information systems are some resources that can be used to improve the ability to compete. One of the elements that must be considered in the development of a college is to be able to compete alumni academic data. These days a higher education needs to be competitive by utilizing all resources they have. There are several resources to be considered such as infrastructure, human, data and information resources. One data considered important for higher education to compete is the alumni academic data. STIKOM Uyelindo the higher education, the case took place, has enough alumni academic data to be analyzed. Data clustering is then implemented to generate the global picture of the alumni profile. In turn, the higher education can simply takes decision based on this very information coming from clustering process. In this research, k-means algorithm is used to cluster the alumni academic data in order to show the characteristics of the alumni. The results of alumni coming from three program of study (informatics S1/D3 and information system S1), and k=3, shows that there is a distinct pattern of alumni profile which can be used to support decisions.

Item Type: Thesis (S2)
Uncontrolled Keywords: Data Analysis, Academic Alumni Profile, Clustering, K-means Clustering Algorithm
Subjects: Magister Teknik Informatika > Enterprise Inf System
Divisions: Pasca Sarjana > Magister Teknik Informatika
Depositing User: Editor UAJY
Date Deposited: 17 Jun 2014 10:24
Last Modified: 17 Jun 2014 10:24
URI: http://e-journal.uajy.ac.id/id/eprint/5273

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