Leveraging Machine Learning to Predict Academic Specialization Pathways in Higher Education

This study developed a machine learning-based model to predict academic concentration selection among information systems students at Universitas Multimedia Nusantara (UMN). A survey of 125 students from the 2024 cohort revealed that 90% experienced difficulties in choosing a specialization, primarily due to limited information on course relevance, unclear academic pathways, and career uncertainty. While the survey provides a contextual background, the predictive model was trained using historical academic performance data from the 2021–2023 cohorts. The three classification algorithms, Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost) were implemented following the CRISP-ML methodology. To address class imbalance in the dataset, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, followed by hyperparameter tuning and feature selection. The Random Forest model demonstrated superior performance, achieving an accuracy of 78.08% on the 2021–2022 cohort data, outperforming Decision Tree and XGBoost across all experimental settings. This result highlights Random Forest's robustness in this context, particularly after the integration of SMOTE and optimization procedures. The main contribution of this study lies in the application of machine learning for academic pathway prediction in an Indonesian higher education setting, providing a data-driven decision support tool to assist students in making informed and personalized specialization choices.