Educational Data Mining in Accounting: Market Segmentation Strategy for Financial Management of New Student Admissions
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Abstract
In higher education, effective financial management is a key factor in ensuring institutional sustainability, including at Universitas Terbuka. One of the main challenges in financial management is the ability to accurately analyze and predict new student enrollment. This study applies an Educational Data Mining (EDM) approach in accounting to develop market segmentation strategies that enhance financial management efficiency at Universitas Terbuka. The methods used include Growth Ratio and Naïve Bayes Classifier (NBC), utilizing data obtained from the national Senior High School database managed by the Indonesian Ministry of Education, Culture, Research, and Technology. The analysis results indicate that with the application of Growth Ratio calculations, the national SLTA graduate absorption rate is projected to increase by 1.2% annually. Based on this trend, the projected absorption rate of SLTA graduates into Universitas Terbuka is expected to grow to 7.2% in 2024, 8.4% in 2025, and 9.6% in 2026. These findings reflect a positive growth trend in new student enrollment, providing a strategic basis for Universitas Terbuka in making data-driven financial and resource management decisions. Thus, the implementation of Educational Data Mining techniques has the potential to be an innovative solution for supporting data-driven financial planning in higher education.
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