Student dropout is a persistent challenge in the digital era of higher education. Using the chi-square test, this study examines whether two digital academic indicators—Grade Point Average (GPA) and attendance—are associated with dropout risk. We analyzed SIAKAD records for 100 Information Systems students at Bina Sarana Informatika University (cohorts 2020–2022), categorizing GPA (<2.75 vs. ?2.75) and attendance (<80% vs. ?80%). Results show a significant association between GPA and dropout (?² = 21.54, p < 0.001) and between attendance and dropout (?² = 29.85, p < 0.001). Students with low GPA and low attendance formed the highest-risk group (75% dropout), while high GPA and high attendance corresponded to the lowest risk (5%). These findings translate into simple, replicable rules for early detection and timely intervention: students below the GPA and attendance thresholds should trigger counseling, remedial support, or study-load adjustments. The contribution of this study is practical and immediate. Unlike approaches that rely on complex predictive models, a basic chi-square framework applied to routinely collected SIAKAD data yields actionable risk profiles that institutions can adopt with minimal technical overhead.
Satya Arisena Hendrawan (Thu,) studied this question.