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All Journal Ambidextrous : Journal of Innovation, Efficiency and Technology in Organization
Hairun Nisah
Muhammadiyah University of North Sumatra
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering

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Analysis of Patterns Causing Low Interest of High School Students to Continue Their Education to Higher Education Using the Association Rule Mining Algorithm (Apriori) Hairun Nisah
Ambidextrous Journal of Innovation Efficiency and Technology in Organization Vol. 5 No. 02 (2026): Ambidextrous: Journal of Innovation, Efficiency and Technology in Organization
Publisher : Takaza Innovatix Labs Ltd.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61536/ambidextrous.v5i02.623

Abstract

The low interest of high school students in continuing their education to college is influenced by various internal and external factors that have complex relationships. This study aims to identify patterns of factors causing low student interest and determine the strength of the relationship between factors using the Association Rule Mining (Apriori) algorithm. This study uses a quantitative approach with data mining methods. The study population was high school students who were at the decision-making stage of continuing their education to college, while the research sample was students who completed the research questionnaire. The research instrument was a closed questionnaire with eight indicators including interest in continuing their education, family economic conditions, academic ability, parental support, college information, social environment, personal motivation, and access to college. Data analysis was carried out through the stages of pre-processing, data transformation, formation of frequent itemsets, and evaluation of association rules based on support, confidence, and lift values. The results of the study indicate that the developed web-based application is able to generate frequent itemsets and association rules that describe the relationship between factors causing low student interest in continuing their education. The conclusion of this study shows that the Apriori algorithm can be used as a method to support educational decision-making by identifying dominant factors related to students' interest in continuing their education to college
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