Applying Process Mining to Analyze Anomalies in Student Registration - Wisudiawan, Gede Agung Ary; Sardi, Indra Lukmana (Cattleya Darmaya Fortuna, 2025)
This study applies process mining to analyze the student course registration process using activity log data from the SIRAMA application. A process model was discovered and evaluated through conformance checking by comparing it with the standard operating procedures (SOP) to detect anomalies. Four key metrics were employed: fitness, precision, generalization, and simplicity. The evaluation results reveal a fitness score of 0.846, indicating that most events in the log are represented by the model, although some transitions remain uncaptured. The relatively low precision score of 0.69531 suggests that the model is overly permissive, allowing many potential paths that never occurred in practice. In contrast, the generalization score of 0.99992 demonstrates that the model is highly robust, capable of representing unseen but valid cases without overfitting. Further audit and analysis identified three anomalous transitions: “Start” directly followed by “DELETE,” “Start” followed by “SIAP REGISTRASI” without prior “ADD,” and “ADD” directly followed by “END.” Expert validation confirmed that these anomalies were not user-driven but caused by technical issues such as delayed log writing (non-atomic transactions) and timestamp overflow errors. To mitigate such anomalies, this study recommends enforcing ACID principles—particularly atomicity—along with strict timestamp validation and automatic correction of invalid log formats. The findings highlight that process mining is not only effective for modeling real-world academic processes but also serves as a diagnostic tool for detecting systematic deviations and improving system reliability