Abstract :
Competition between Private Universities (PTS) in Indonesia to get new students increasingly tight course, especially with the MEA to tap foreign universities to accept new students at competitive rates and more competitive quality,. This happens because the State Universities (PTN) has absorbed about 30% and about 10% did not continue, so the fight PTS 60% of the total number of high school graduates or the equivalent. This position makes PTS to make breakthroughs in marketing the college.Various methods are used by the marketing team of the college, namely by making breakthroughs in creative and innovative, both the conventional manner and the use of digital media with the aim of getting new students as much. One way to do this is to open the new student registration database that has been saved in large enough quantities. Database in the extract to obtain the pattern of relationships among the data. These results will provide preliminary information (Knowledge base) as a basis for determining the base map of the area students who apply to the Banks Association Institute. To do this database extraction, using data mining techniques with association rule mining algorithms and algorithms priori, while the application is used is an application program Weka 3.6.10. The database used in this research is secondary data that already exists in Marketing Bureau and the Bureau of PTI, the entire data of students who enroll in PERBANAS Institute by a period of 3 years (2013-2015), both as a student and who is not a student. Extract data is done by selecting a field / attribute to be analyzed, so it does not use traditional all existing fields. The results of data processing using the application, can describe patterns of relationships between data attributes are selected, which describes the distribution area prospective students who apply and are accepted on any existing courses at the Institut Banks Association. This pattern is used to provide input to the Bureau of marketing in an area map origin living area distribution and SMU origin of prospective and current students existing active.
Keywords: extract the data, students, data mining, data pattern