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All Journal International Journal of Management Science and Information Technology (IJMSIT)
Aditya Saputra, Eka
Unknown Affiliation
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Continuous Regression Models for Mapping the Smartphone Addiction Spectrum Using Random Forest Regressor Aditya Saputra, Eka; Sunarya, I Made Gede; Suputra, Putu Hendra
International Journal of Management Science and Information Technology Vol. 6 No. 1 (2026): January - June 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v6i1.7098

Abstract

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.
Co-Authors I Made Gede Sunarya Putu Hendra Suputra