Analisa Data Mahasiswa Baru Terhadap Program Studi Yang
Dipilih Di Universitas Pembangunan Nasional “Veteran†Jawa
Timur Menggunakan Teknik Data Mining
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Institusion
Universitas Pembangunan Nasional Veteran Jawa Timur
Author
Citra , Arum Sari
Subject
T55.4 Industrial engineering. Management engineering
Datestamp
2014-08-25 02:37:16
Abstract :
Universitas Pembangunan Nasional "Veteran" East Java is located in East Java, Surabaya is Indonesia's Private University which has 6 faculties with 19 courses. The number of applicants from each courses at the universitas Pembangunan Nasional "Veteran" East Java can be affected by SMA origin, parental income and home town. Based on this, the study will be categorize courses based on existing student data with the purpose of providing information to the University to prioritize programs with little interest. For information search process of the new student data UPN "Veteran" East Java is used data mining techniques, while the K-Means clustering is used to group the courses into classes based on similar data. Based on the results of the clustering method K-Means algorithm has been there 3 clusters, where cluster 1 is a courses with little interest in as many as 10 courses, cluster 2 of enthusiasts as much as 5 courses and cluster 3 is quite interested as much as 4 courses. Keyword : Courses, Data Mining, Clustering K-Means
Universitas Pembangunan Nasional "Veteran" East Java is located in East Java, Surabaya is Indonesia's Private University which has 6 faculties with 19 courses. The number of applicants from each courses at the universitas Pembangunan Nasional "Veteran" East Java can be affected by SMA origin, parental income and home town. Based on this, the study will be categorize courses based on existing student data with the purpose of providing information to the University to prioritize programs with little interest. For information search process of the new student data UPN "Veteran" East Java is used data mining techniques, while the K-Means clustering is used to group the courses into classes based on similar data. Based on the results of the clustering method K-Means algorithm has been there 3 clusters, where cluster 1 is a courses with little interest in as many as 10 courses, cluster 2 of enthusiasts as much as 5 courses and cluster 3 is quite interested as much as 4 courses. Keyword : Courses, Data Mining, Clustering K-Means
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