Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Indonesian Journal of Innovation Multidisipliner Research
Imran Iskandar
Universitas Pancasakti Makassar
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Machine Learning, Supervised Leveraging Supervised Machine Learning to Improve Accuracy in Predicting Smartphone Addiction Muhammad Nur Arafah; Imran Iskandar; Rahmat Fuadi Syam; Fitri Rahmadani; Mario Dendo; Serpasius Dappa Sudda
Indonesian Journal of Innovation Multidisipliner Research Vol. 4 No. 2 (2026): April - Juni
Publisher : Institute of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/ijim.v4i2.473

Abstract

The transformation of smartphones into essential life companions triggers new challenges in the form of digital addiction that erodes productivity and cognitive focus. In contrast to previous studies that focused on screen duration, this study explored a more critical dimension, namely the degradation of productive hours. The research aims to improve the prediction of addiction using supervised machine learning by integrating productivity variables as a core diagnostic feature. Through a database of 7,500 respondents, a comparative analysis was carried out on the Random Forest, K-Nearest Neighbors (KNN), and Naïve Bayes algorithms in The results of the study proved that the productivity variable significantly increased the predictability of the model. Random Forest consistently outperformed other algorithms across scenarios, with the highest accuracy reaching 93.32% at a 70:30 data ratio, surpassing KNN (89.66%) and Naïve Bayes (87.96%). With a specificity of 92.70%, these findings reveal that workflow disruption has a higher differentiating power than conventional duration metrics. In conclusion, productivity interference is a key indicator in identifying digital behavioral disorders. In practical terms, this research provides guidance for developers and managers in designing precise early warning systems to restore daily efficiency amid the distractions of the digital world.
Co-Authors Fitri Rahmadani Mario Dendo Muhammad Nur Arafah Rahmat Fuadi Syam Serpasius Dappa Sudda