PENERAPAN DATA MINING MENGGUNAKAN ALGORITMA FP-GROWTH UNTUK PENENTUAN STRATEGI PROMOSI FAKULTAS ILMU KOMPUTER UNIVERSITAS KUNINGAN - Heru Budianto; Jejen Riana (Sistem Informasi Fakultas Ilmu Komputer Universitas Kuningan, 2020)
Nowadays, competition between campuses is increasing in order to attract as many new students as possible. This has an impact on management at the faculty level. Limited funding and efficiency demands cause managers at the faculty level to try to find the right strategy to achieve the student targets that have been determined. The right strategy is able to minimize the use of funds and achieve the targets set. This study aims to analyze data mining techniques with the help of the FP-Growth algorithm. FP-Growth algorithm which is a development of the Apriori algorithm. FP-Growth is one alternative algorithm that can be used to determine the set of data that most often appears (frequent item sets) in a data set. The study was conducted by observing several research variables that are often considered by faculties in determining the promotion objectives, namely the Latest Education, Departments, Study Program Choices. The results of this study are in the form of a strategy determination recommendation using the FP-Growth algorithm that uses the concept of FP-Tree development in searching for Frequent Itemset