Published: 2026-05-21

Continuous Regression Models for Mapping the Smartphone Addiction Spectrum Using Random Forest Regressor

DOI: 10.35870/ijmsit.v6i1.7098

I Putu Eka Aditya Saputra, I Made Gede Sunarya, Putu Hendra Suputra
  • I Putu Eka Aditya Saputra: Universitas Pendidikan Ganesha
  • I Made Gede Sunarya: Universitas Pendidikan Ganesha
  • Putu Hendra Suputra: Universitas Pendidikan Ganesha
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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.

Keywords

Smartphone Addiction; Random Forest Regressor; Predictive Modeling; Continuous Score; CRISP-DM

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How to Cite

Aditya Saputra, E., Sunarya, I. M. G., & Suputra, P. H. (2026). Continuous Regression Models for Mapping the Smartphone Addiction Spectrum Using Random Forest Regressor. International Journal of Management Science and Information Technology, 6(1), 586-594. https://doi.org/10.35870/ijmsit.v6i1.7098
Article Information

This article has been peer-reviewed and published in the International Journal of Management Science and Information Technology. The content is available under the terms of the Creative Commons Attribution 4.0 International License.

  • Issue: Vol. 6 No. 1 (2026)

  • Section: Articles

  • Published: 2026-05-21

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Author Biographies
I Putu Eka Aditya Saputra, Universitas Pendidikan Ganesha
Google Scholar ResearchGate ORCID

Computer Science Study Program, Graduate Faculty, Universitas Pendidikan Ganesha, Buleleng Regency, Bali Province, Indonesia

I Made Gede Sunarya, Universitas Pendidikan Ganesha
Google Scholar ResearchGate ORCID

Computer Science Study Program, Graduate Faculty, Universitas Pendidikan Ganesha, Buleleng Regency, Bali Province, Indonesia

Putu Hendra Suputra, Universitas Pendidikan Ganesha
Google Scholar ResearchGate ORCID

Computer Science Study Program, Graduate Faculty, Universitas Pendidikan Ganesha, Buleleng Regency, Bali Province, Indonesia

References
  • Aditra Pradnyana, G., Anggraeni, W., Mulyanto Yuniarno, E., & Purnomo, M. H. (2025). Revealing Depression Through Social Media via Adaptive Gated Cross-Modal Fusion Augmented with Insights from Personality Traits. IEEE Access, 13, 133465–133482. https://doi.org/10.1109/ACCESS.2025.3593273
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Agung, A. A. G., Gading, I. K., Agetania, N. L. P., Prawira, A. A. G. O. A., Deng, J.-B., & Werang, B. R. (2024). Exploring Work-Related Stress among Indonesian Primary School Teachers: A Study in the Post-Covid-19 Era. Journal of Ecohumanism, 3(4), 805–815. https://doi.org/10.62754/joe.v3i4.3577
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Ahsan, M., Mahmud, M., Saha, P., Gupta, K., & Siddique, Z. (2021). Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance. Technologies, 9(3), 52. https://doi.org/10.3390/technologies9030052
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Ardyaputra, G. Y., Bagus, I., Pascima, N., & Suputra, P. H. (2026). Analisis Sentimen Penggunaan Cekat.AI dalam Menggantikan Customer Service Menggunakan Logistic Regression dan TF-IDF. Journal of Computer System and Informatics (JoSYC), 7(2), 47–56. https://doi.org/10.47065/josyc.v7i2.9292
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Blasi, A. H., & Alsuwaiket, M. (2020). Analysis of Students’ Misconducts in Higher Education using Decision Tree and ANN Algorithms. Engineering, Technology & Applied Science Research, 10(6), 6510–6514. https://doi.org/10.48084/etasr.3927
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Crowhurst, S., & Hosseinzadeh, H. (2024). Risk Factors of Smartphone Addiction: A Systematic Review of Longitudinal Studies. Public Health Challenges, 3(2). https://doi.org/10.1002/puh2.202
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Elhai, J. D., Yang, H., Rozgonjuk, D., & Montag, C. (2020). Using machine learning to model problematic smartphone use severity: The significant role of fear of missing out. Addictive Behaviors, 103. https://doi.org/10.1016/j.addbeh.2019.106261
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Emmanuel, T., Maupong, T., Mpoeleng, D., Semong, T., Mphago, B., & Tabona, O. (2021). A survey on missing data in machine learning. Journal of Big Data, 8(1), 140. https://doi.org/10.1186/s40537-021-00516-9
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Hanafi, E., Siste, K., Wiguna, T., Kusumadewi, I., & Nasrun, M. W. (2019). Temperament profile and its association with the vulnerability to smartphone addiction of medical students in Indonesia. PLOS ONE, 14(7), e0212244. https://doi.org/10.1371/journal.pone.0212244
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Hong, Y., Rong, X., & Liu, W. (2024). Construction of influencing factor segmentation and intelligent prediction model of college students’ cell phone addiction model based on machine learning algorithm. Heliyon, 10(8), e29245. https://doi.org/10.1016/j.heliyon.2024.e29245
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Indrawan, G., Setiawan, H., & Gunadi, A. (2022). Multi-class SVM Classification Comparison for Health Service Satisfaction Survey Data in Bahasa. HighTech and Innovation Journal, 3(4), 425–442. https://doi.org/10.28991/HIJ-2022-03-04-05
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Jim, E. L., Pio, R. J., Asaloei, S. I., Leba, S. M. R., Angelianawati, D., & Werang, B. R. (2024). Work-Related Stress, Emotional Exhaustion, Job Satisfaction, and Organizational Commitment of Indonesian Healthcare Workers. International Journal of Religion, 5(5), 308–316. https://doi.org/10.61707/6fzykj38
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Jiwa Permana, A. A., Sudarma, M., Sukarsa, I. M., & Hartati, R. S. (2025). Development of a Life Story-Based Digital Counseling Model to Detect Student Depression Using LSTM. JOIV: International Journal on Informatics Visualization, 9(2), 550. https://doi.org/10.62527/joiv.9.2.2642
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Joseph, C., & Maheswari, P. U. (2025). Facial emotion-based smartphone addiction detection and prevention using deep learning and video-based learning. Scientific Reports, 15(1), 18025. https://doi.org/10.1038/s41598-025-99681-7
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Ketut, N., Artalia, A., Made, I., Maysanjaya, D., & Suputra, P. H. (2026). SENTIMENT ANALYSIS OF BALI’S RAPID TOURISM GROWTH USING INDOBERT WITH INSET LEXICON AUTOMATIC LABELLING. 15(1).
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Kim, K., Yoon, Y., & Shin, S. (2024). Explainable prediction of problematic smartphone uses among South Korea’s children and adolescents using a Machine learning approach. International Journal of Medical Informatics, 186, 105441. https://doi.org/10.1016/j.ijmedinf.2024.105441
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Lee, J., & Kim, W. (2021). Prediction of problematic smartphone use: A machine learning approach. International Journal of Environmental Research and Public Health, 18(12). https://doi.org/10.3390/ijerph18126458
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Li, L., Griffiths, M. D., Mei, S., & Niu, Z. (2020). Fear of Missing Out and Smartphone Addiction Mediates the Relationship Between Positive and Negative Affect and Sleep Quality Among Chinese University Students. Frontiers in Psychiatry, 11. https://doi.org/10.3389/fpsyt.2020.00877
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Likhith, S., Chitteti, C., Dharani, M., Nivedhitha, V., Geethika, N. G., & Godwin, V. (2024). Machine Learning Model for Prediction of Smartphone Addiction. 2024 International Conference on Expert Clouds and Applications (ICOECA), 924–929. https://doi.org/10.1109/ICOECA62351.2024.00163
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Little, R., & Rubin, D. (2019). Statistical Analysis with Missing Data, Third Edition. Wiley. https://doi.org/10.1002/9781119482260
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Namlı, S., Çar, B., Kurtoğlu, A., Yılmaz, E., Tekkurşun Demir, G., Güvendi, B., Batu, B., & Aldhahi, M. I. (2025). The Relationship Between Smartphone and Game Addiction, Leisure Time Management, and the Enjoyment of Physical Activity: A Comparison of Regression Analysis and Machine Learning Models. Healthcare, 13(15), 1805. https://doi.org/10.3390/healthcare13151805
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Ndayambaje, E., & Okereke, P. U. (2025). The Psychopathology of Problematic Smartphone Use (PSU): A Narrative Review of Burden, Mediating Factors, and Prevention. Health Science Reports, 8(5). https://doi.org/10.1002/hsr2.70843
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Nurmala, I., Nadhiroh, S. R., Pramukti, I., Tyas, L. W., Zari, A. P., Griffiths, M. D., & Lin, C.-Y. (2022). Reliability and validity study of the Indonesian Smartphone Application-Based Addiction Scale (SABAS) among college students. Heliyon, 8(8), e10403. https://doi.org/10.1016/j.heliyon.2022.e10403
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Park, J. H., & Park, M. (2021). Smartphone use patterns and problematic smartphone use among preschool children. PLOS ONE, 16(3), e0244276. https://doi.org/10.1371/journal.pone.0244276
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Ratuaki, F., Gading, I. K., & Suarni, N. K. (2023). Pengaruh Konseling Realita untuk Menurunkan kecanduan terhadap smartphone dan meningkatkan Prestasi belajar. Jurnal EDUCATIO: Jurnal Pendidikan Indonesia, 9(1), 37. https://doi.org/10.29210/1202322638
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Shiferaw, B. D., Tang, J., Wang, Y., Wang, Y., Wang, Y., Mackay, L. E., Luo, Y., Yan, N., Shen, X., Zhou, T., Zhu, Y., Cai, J., Wang, Q., Yan, W., Gao, X., Pan, H., & Wang, W. (2025). Impact of digital addiction on youth health: A systematic review and meta-analysis. Journal of Behavioral Addictions, 14(3), 1129–1158. https://doi.org/10.1556/2006.2025.00081
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Srivastava, S., Verma, N., Kumar, D., Singh, N., & Kumar, K. (2025). Nomophobia as an Emerging Psychopathology Psychophysiological Mechanisms and Clinical Implications. Annals of Neurosciences. https://doi.org/10.1177/09727531251351082
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • statistik-telekomunikasi-indonesia-2019. (n.d.).
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Stone, R. (2020). The Effects of Cyberbullying as it Relates to social media: A California High School Assistant Principal and High School Counselor Perspective. https://digitalcommons.umassglobal.edu/edd_dissertations
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Upshaw, J. D., Stevens, C. E., Ganis, G., & Zabelina, D. L. (2022). The hidden cost of a smartphone: The effects of smartphone notifications on cognitive control from a behavioral and electrophysiological perspective. PLOS ONE, 17(11), e0277220. https://doi.org/10.1371/journal.pone.0277220
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Windarwati, H. D., Lestari, R., Wicaksono, S. A., Kusumawati, M. W., Ati, N. A. L., Ilmy, S. K., Sulaksono, A. D., & Susanti, D. (2022). Relationship between stress, anxiety, and depression with suicidal ideation in adolescents. Jurnal Ners, 17(1). https://doi.org/10.20473/jn.v17i1.31216
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Yosep Fredi, I Made Tegeh, & Ketut Agustini. (2026). Smartphone Use, Learner Autonomy, and Social Context in Student Creative Thinking. Indonesian Journal of Innovation Studies. https://ijins.umsida.ac.id/index.php/ijins/article/view/1758/2216
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Yu, C., Kong, X., Yu, W., Ni, X., Chen, J., & Liao, X. (2025). Machine learning models for predicting the risk of depressive symptoms in Chinese college students. Frontiers in Psychiatry, 16. https://doi.org/10.3389/fpsyt.2025.1648585
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Zhou, X., & Feng, B. (2025). Social anxiety and smartphone addiction among college students: the mediating role of loneliness. Frontiers in Psychiatry, 16. https://doi.org/10.3389/fpsyt.2025.1621900
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
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