Continuous Regression Models for Mapping the Smartphone Addiction Spectrum Using Random Forest Regressor - Eka Aditya Saputra; I Made Gede Sunarya; Putu Hendra Suputra (Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia, 2026)
This study proposes a predictive modeling approach to measure the level of smartphone addiction in adolescents by transforming a conventional binary classification model into a continuous regression model. The use of categorical labels often fails to capture the complex spectrum of addictive behaviors, so this study implemented the Random Forest Regressor algorithm to predict addiction scores on a scale of 1.0 to 10.0. The experimental results show that the regression model is able to provide high prediction accuracy, as evidenced by the coefficient of determination obtained R^2 of 0.8607 and a Mean Absolute Error (MAE) of 0.2854. These findings confirm that the regression approach offers better data resolution in mapping the degree of digital dependency than classification methods. In practice, this model produces a continuous score that provides a dynamic tool for mental health professionals. This approach allows for objective monitoring of patient’s behavioral progress during recovery. Furthermore, this model can facilitate multilevel psychological interventions and tailored care, from early prevention to therapy for high-risk addicts.