Data-Driven Policy: An Analysis of Graduate Job Waiting Time and Its Implications for Higher Education Services

<ns5:p>Background Tracer study data are more valuable for institutional evaluation than for individual-level forecasting of graduate job waiting time. This study analyzes tracer study records from the Faculty of Administrative Sciences, Universitas Brawijaya, for the 2021–2023 graduating cohorts (N = 1280). Methods Six classification models were compared to classify graduates according to whether they secured employment within six months or experienced longer waiting times, while education-related determinants of employment transition were also examined. Results Logistic Regression showed the highest sensitivity to delayed employment, whereas Random Forest provided relatively more balanced classification performance. The absence of internship experience emerged as the strongest barrier to faster employment, followed by poor course quality and the absence of research project participation. Limited field-specific expertise and information technology capability were also associated with delayed employment, whereas stronger English proficiency and self-development were associated with faster employment transition. Conclusion These findings imply that universities should strengthen structured work-integrated learning, competence-oriented teaching, research engagement, digital capability, and employability development. The study contributes by repositioning tracer study data as evidence for institutional diagnosis and higher education service improvement while highlighting its complementary role alongside other approaches for individual-level prediction.</ns5:p>