IMPLEMENTATION OF DATA MINING FOR PREDICTING GRADUATES’ WAITING TIME AND ITS IMPLICATIONS FOR EDUCATIONAL POLICY - Wati, Desmita Ayu Rachma; Hermawan, Hermawan; Muslim, Abdul Qadir (Faculty of Administrative Science, Universitas Brawijaya, 2025)
This study aims to analyze the waiting period for employment among graduates of the Educational Administration Study Program at Universitas Brawijaya using a data mining approach with the Naïve Bayes Classifier algorithm. A major issue facing higher education in Indonesia is the high unemployment rate among graduates due to a mismatch between acquired competencies and labor market demands. This research utilizes Tracer Study data from 2019 to 2022 to classify graduate employment waiting periods based on variables such as study duration, GPA, competencies, English proficiency, communication skills, and teamwork. The classification process was conducted using RapidMiner and Microsoft Excel, resulting in an accuracy rate of 81.82%, precision of 100%, and recall of 50%. These findings indicate that the predictive model can serve as a robust foundation for formulating evidence-based education policies. The implementation of data mining contributes to curriculum development, skills training, and labor market demand mapping. This approach enhances the effectiveness of higher education institutions in preparing graduates who are competent and aligned with the dynamics of the job market.