Sustainability of Quality Management by Implementing Data Mining to Predict Academic Achievement

Abstract

This research aims to predict the academic achievement of Pamulang University students using Educational Data Mining (EDM) techniques. With the increasing number of students and the complexity of academic data, it is important to apply methods that can analyze and predict learning outcomes to improve learning strategies and academic support. This study collected data from various sources, including course grades, attendance, and participation in extracurricular activities. The collected data is then analyzed using EDM techniques such as decision trees, neural networks, and support vector machines to identify patterns and factors that contribute to student academic achievement. The results of the analysis show that factors such as attendance, involvement in campus activities, and previous test scores have a significant influence on academic achievement. This research provides valuable insights for the development of targeted interventions, with the aim of improving academic outcomes and facilitating more effective learning strategies at Pamulang University. These findings also offer contributions to further research in the field of EDM and its application in higher education contexts.