The Impact of Data Splitting on Graph-Based Dropout Prediction Using Subgraph Matching and Graph Edit Distance

Student dropout remains a persistent issue in higher education, affecting institutional effectiveness and student success rates. This paper proposes a graph-based predictive model that employs subgraph matching and Graph Edit Distance (GED) to identify students at high risk of dropout. By modeling students and courses as an undirected bipartite graph, the system detects structural similarities between student profiles. The proposed model was evaluated using a dataset of 282 students from a private university in Indonesia under three data-splitting scenarios: 70/30, 79/21, and 89/11. Evaluation metrics include precision, recall, F1-score, and accuracy. The model achieved its best performance at the 89/11 split, with an accuracy of 91%, precision of 1.00, recall of 0.88, and an F1-score of 0.94. Results suggest that increasing the proportion of training data enhances generalization and prediction accuracy. GED demonstrated effectiveness in capturing subtle structural distinctions among student–course relationships, enabling early dropout risk identification. The primary contribution of this study is the development of a graph-analytic framework for dropout prediction, offering an alternative to traditional models such as logistic regression and decision trees that lack relational awareness. Future work will incorporate behavioral and socio-economic attributes to further improve prediction outcomes.