Determination of Student Subjects in Higher Education Using Hybrid Data Mining Method with the K-Means Algorithm and FP Growth

(1) * Larissa Navia Rani Mail (Universitas Putra Indonesia YPTK Padang, Indonesia)
(2) Sarjon Defit Mail (Universitas Putra Indonesia YPTK Padang)
(3) L. J. Muhammad Mail (Federal University, Kashere, Gombe State, Nigeria)
*corresponding author


The large number of courses offered in an educational institution raises new problems related to the selection of specialization courses. Students experience difficulties and confusion in determining the course to be taken when compiling the study plan card. The purpose of this study was to cluster student value data. Then the values that have been grouped are seen in the pattern (pattern) of the appearance of the data based on the values they got previously so that students can later use the results of the patterning as a guideline for taking what skill courses in the next semester. The method used in this research is the K-Means and FP-Growth methods. The results of this rule can provide input to students or academic supervisors when compiling student study plan cards. Lecturers and students can analyze the right specialization subject by following the pattern given. This study produces a pattern that shows that the specialization course with the theme of business information systems is more followed by students than the other 2 themes


Hybrid Data Mining K-Means Algorithm FP Growth Algorithm Higher Education



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