<![CDATA[Purpose: This study aims to develop and evaluate a tree-based survival machine learning framework for predicting graduate study duration and identifying students at risk of delayed graduation. The study addresses the growing need for accurate educational time-to-event prediction to support academic monitoring and data-driven decision-making in higher education institutions, including IPB University. Methods: A quantitative predictive analysis was conducted using data from 3,417 master students from the 2020–2022 cohorts. Four tree-based survival models were evaluated, namely Survival Tree (ST), Extremely Randomized Survival Tree (EST), Random Survival Forest (RSF), and Gradient Boosting Survival (GBS). The analysis used right-censored survival data with a 42-month observation period. Model evaluation was conducted using repeated stratified random split validation (10 repetitions) with Concordance Index (C-index) and Integrated Brier Score (IBS) metrics. Risk stratification was subsequently performed using the best-performing model based on predicted survival probabilities at a 24-month time horizon. Result: GBS achieved the best overall predictive performance with the highest mean C-index (0.659) and the lowest mean IBS (0.194), indicating superior discrimination and prediction accuracy compared to ST, EST, and RSF. The repeated evaluation results also demonstrated stable predictive performance across data partitions. Risk stratification successfully separated students into low-, medium-, and high-risk groups with significantly different survival patterns. High-risk students generally tended to be older, have lower undergraduate GPA, were more often male, and more frequently originate from private undergraduate institutions. Novelty: This study provides a comparative evaluation of multiple tree-based survival machine learning models within an educational time-to-event framework. The integration of repeated survival model evaluation with practical student risk stratification offers both methodological and applied contributions for academic monitoring and early intervention strategies in higher education.]]>
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