Analisis Pengaruh Stimultan Penggunaan TikTok dan Instagram terhadap Prestasi Akademik Mahasiswa - Zulatifa, Nelani Shafatia; Ridla, Abdilbar Ainur; Pratiwi, Galuh Rastika; Listyaningsih, Divita Aulia; Octavia, Amallia Putri; Hamidah, Atika Rosidah; Rosyid, Harun Al (Forum Kerjasama Pendidikan Tinggi (FKPT), 2025)
This study aims to analyze the relationship between the use of TikTok (X1) and Instagram (X2) on the academic achievement of Digital Business Undergraduate Program students of the 2022 intake, which is assessed by the average score. The study approach uses a quantitative approach with a questionnaire by 186 participants. The research instrument has been proven valid because all items meet the requirements of Corrected Item-Total Correlation > 0.30, and is reliable with Cronbach's Alpha values X1 = 0.81; X2 = 0.86; and Y = 0.87, respectively. The findings of the analysis indicate that the two independent variables do not contribute significantly to the dependent variable. The Pearson correlation results show r = –0.045 (p = 0.568) on X1 and r = 0.048 (p = 0.548) on X2, which reflects a very weak and statistically insignificant relationship. Partial regression testing using the t-test also confirmed this, with the regression coefficient of X1 being -0.0398 (t = -0.516; p = 0.606) and X2 being -0.0001 (t = -0.001; p = 0.999). Simultaneously, the F-test also indicated that the model was not significant (p > 0.05). The low R-squared value confirmed that X1 and X2 played only a small role in explaining the variation in student GPA. The regression model also met classical assumptions, including freedom from multicollinearity (VIF X1 = 1.18; VIF X2 = 1.17), no heteroscedasticity (p = 0.7829), and a linear relationship, although there were indications of mild autocorrelation (DW = 1.731). These findings confirm that the intensity of TikTok and Instagram use is not the main predictor of academic achievement, so further research is recommended to include other variables such as learning motivation, time management, academic support, quality of the learning environment, self-regulation, and the purpose and type of content consumption to build a more comprehensive academic achievement prediction model.