Algoritma Apriori untuk Menampilkan Korelasi Nilai Akademik dengan Kelulusan Mahasiswa: Data Mining - Selvia Lorena Br Ginting (Komputika : Jurnal Sistem Komputer, 2017)
The number of student data that increases every year certainly results in data accumulation in universities. A data processing technique is needed hence the data that accumulates is not difficult to analyze. This research was conducted to analyze the relationship between student academic data and graduation categories. Varied processing techniques need to be adjusted to the needs of data analysis, the method used in this research is the Apriori algorithm, which is the Association algorithm that uses knowledge of the frequency of previously known attributes to process further information. This research is carried out by utilizing academic data and student graduation data, namely by finding the percentage of the relationship between the value of student courses to graduation categories using data mining. Graduation categories are measured from the length of study students and GPA, while the academic data used is the value of student courses. The information displayed is a value of support (Support Value) and confidence (Certainty Value).