Analisis Data PMB di STITEK Bontang dengan FP Growth dan Apriori Untuk Mendukung Strategi Promosi di Masa Pandemi


  • Arief Wibowo Universitas Budi Luhur
  • Rina Megawati Universitas Budi Luhur
  • Henry Universitas Budi Luhur


Data PMB, Data mining, FP-Growth, Apriori, CRISP-DM


Data on new student admissions is always available and owned by all universities, both private and public. One of the private universities that has this data is STITEK Bontang (Bontang College of Technology). This data can be used as a reference or database owned by STITEK Bontang to be used optimally. This utilization is used as strategic information for new student admissions during the pandemic. This study aims to group new student data using data mining techniques. The data mining technique used is the FP Growth algorithm and the Apriori Algorithm. For the research steps using the CRISP-DM methodThis research is used to determine the right Promotion Strategy. Determining the right promotional strategy will be able to reduce promotional costs and achieve the right promotion goals. 1) Fp-Growth and Apriori methods in building a knowledge base from a collection of student databases accepted at STITEK Bontang by showing the relationship between student identity and the study program that the student chooses. 2) Obtained the most entrances with a lift ratio of 2.