Implementasi Algoritma K-Means Clustering Pada Penerimaan Mahasiswa Baru (Studi Kasus: Universitas Katolik Widya Mandira Kupang)

LESTARI, Febriana Dhyah (2021) Implementasi Algoritma K-Means Clustering Pada Penerimaan Mahasiswa Baru (Studi Kasus: Universitas Katolik Widya Mandira Kupang). Undergraduate thesis, Unika Widya Mandira.

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Abstract

Data mining can be used for institutions, schools, colleges, and so on. Colleges can be used to process existing data such as student data, employee data, lecturers, and so on. Every year the college will open new student admissions. Therefore, the college will produce new student data that must be stored such as name, address, school origin, and so on. Not all universities take advantage of the large amount of student data other than just for administration. This study uses the application of data mining with the k-means clustering method in order to know the pattern of selection of study programs for new students at UNWIRA Kupang by grouping student data. Student data are grouped based on the similarity of the data so that data with the same characteristics will be in one cluster. The attributes used are study programs, school origin, average UAN scores. Based on the data mining process with clustering techniques using the K - Means algorithm which is applied to applications with student data input, information is obtained from the 5000 data tested, namely 1,112 students entering cluster 1, 2,288 students entering cluster 2, 2203 students entering cluster 3. Results The results of this study are used as a basis for making decisions to determine strategies to promote each study program at UNWIRA Kupang. based on the results of the k-means algorithm cluster, it can be seen the majors/study programs of interest in each school.

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