Trackternship: A Web-Based Internship Management and Student Employability Prediction System Using Random Forest Algorithm

Abstract

Internship or On-the-Job Training (OJT) programs play an important role in preparing students for professional practice and workforce readiness. However, many higher education institutions continue to utilize manual and spreadsheet-based internship management processes, resulting in inefficient document handling, delayed report generation, fragmented monitoring procedures, and limited employability assessment capabilities. To address these challenges, this study developed TRACKTERNSHIP, a web-based internship management and employability prediction system for the Internship Program Office of Urdaneta City University. The study utilized a descriptive-developmental research design supported by Design Science Research (DSR) principles, and the Rapid Application Development (RAD) methodology in developing the system. TRACKTERNSHIP integrated functionalities for OJT tracking, file and document management, student progress monitoring, employability prediction, and automated report generation. The Random Forest Algorithm was integrated to analyze internship-related performance indicators and generate employability predictions for student interns. The developed system was evaluated using ISO/IEC 25010 Software Quality Standards, System Usability Scale (SUS), and User Acceptance Testing (UAT) involving student interns, OJT coordinators, and the Director of the Internship Program Office. Results revealed that the system achieved high software quality and usability evaluation results with an overall weighted mean of 4.39 interpreted as Strongly Agree, while the Random Forest Algorithm achieved an overall prediction accuracy of 91.4%. The findings indicate that TRACKTERNSHIP improved internship monitoring efficiency, centralized document management, employability analytics, and institutional reporting procedures. The study demonstrates the potential of integrating machine learning and web-based technologies in improving internship management and supporting data-driven employability assessment in higher education institutions.