PENERAPAN DATA MINING UNTUK MENGETAHUI POLA PEMILIHAN PROGRAM STUDI MAHASISWA BARU UIN SUNAN KALIJAGA MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING

IRWANTO, NIM. 12650064 (2016) PENERAPAN DATA MINING UNTUK MENGETAHUI POLA PEMILIHAN PROGRAM STUDI MAHASISWA BARU UIN SUNAN KALIJAGA MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

The admission process for new students at State Islamic University of Sunan Kalijaga abundant produce data that covers personal data of students . It will continue to take place every year so that the data stored in the database will be many more . It is unfortunate if the data are not put to good use as a positive thing for the university. This study uses data mining application with the k-means clustering methods in order to know the pattern of election of a new study program for students in the Islamic State University of Sunan Kalijaga. The raw data that has been obtained is then carried out pre-processing data that includes data cleansing, data integration, data selection and transformation of data. Then after the raw data through these stages, the next step is to do data mining techniques using k-means clustering algorithm. Where in this stage, the data are similar and the same characteristics are grouped within a particular cluster. Attributes that are used in this technique is a program of study, majors in schools, and The origin of the school. Once the data mining process, there are three clusters are formed. Since each cluster that can be seen voting patterns of students to courses. The tendency to choose can be seen in the first cluster, where the cluster is a program of study that is most in demand by students. From the data as many as 5705 students, 2299 students are contained in the first cluster, there are 2101 students in the second cluster and 1305 students entered in the third cluster. From the results of this study can be seen that the first cluster is the highest value, so the tendency of students to choose courses at UIN Sunan Kalijaga can be determined by looking at the data in the first cluster and follow the second and third.

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