KLASIFIKASI TINGKAT KECANDUAN SMARTPHONE  PADA REMAJA MENGGUNAKAN ALGORITMA RANDOM FOREST

Hema Nuramadini; Nining Rahaningsih; Irfan Ali; Ratna Dewi Lestyorini

Authors

Hema Nuramadini STMIK IKMI Cirebon, Indonesia
Nining Rahaningsih STMIK IKMI Cirebon, Indonesia
Irfan Ali STMIK IKMI Cirebon, Indonesia
Ratna Dewi Lestyorini STMIK IKMI Cirebon, Indonesia

Keywords:

kecanduan Smartphone, Random Forest, machine learning, klasifikasi, remaja

Abstract

Penggunaan Smartphone yang berlebihan di kalangan remaja semakin meningkat dan berpotensi menimbulkan kecanduan yang berdampak pada aspek psikologis, sosial, dan akademik. Penelitian ini bertujuan untuk mengklasifikasikan tingkat kecanduan Smartphone pada remaja menggunakan algoritma Random Forest berdasarkan data perilaku digital. Dataset diperoleh dari Teen Phone Addiction Dataset berisi 600 data dan 21 fitur yang mencakup durasi penggunaan harian, aktivitas media sosial, gim, serta faktor demografis dan psikososial. Tahapan penelitian meliputi acquisition data, pra-pemrosesan (data cleaning, label encoding, normalisasi, dan penyeimbangan data menggunakan SMOTE), pelatihan model, evaluasi hyperparameter menggunakan Grid Search dan k-fold cross validation, serta analisis hasil.Model Random Forest menunjukkan performa terbaik dengan akurasi 89%, precision dan recall yang seimbang, serta weighted F1-score 0,89. Fitur yang paling berpengaruh meliputi Daily Usage Hours, Time on Social Media, dan Time on Gaming, yang menunjukkan bahwa intensitas keterlibatan digital memiliki korelasi kuat dengan tingkat kecanduan Smartphone . Hasil penelitian ini memberikan kontribusi pada pengembangan sistem pendeteksi dini risiko kecanduan Smartphone berbasis machine learning, yang dapat digunakan sebagai dasar dalam upaya edukasi, intervensi preventif, dan pengambilan kebijakan terkait kesehatan digital remaja.

References

A. Almadhor et al., “Multi-Class Adaptive Active Learning for Predicting Student Anxiety,” Ieee Access, vol. 12, pp. 58097–58105, 2024, doi: 10.1109/access.2024.3391418.

S. M. M. Hasan, M. P. Uddin, Md. A. Mamun, M. I. Sharif, A. Ulhaq, and G. Krishnamoorthy, “A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders,” Ieee Access, vol. 11, pp. 15038–15057, 2023, doi: 10.1109/access.2022.3232490.

S. M. H. Mirsadeghi, H. Bahşi, R. Vaarandi, and W. Inoubli, “Learning From Few Cyber-Attacks: Addressing the Class Imbalance Problem in Machine Learning-Based Intrusion Detection in Software-Defined Networking,” Ieee Access, vol. 11, pp. 140428–140442, 2023, doi: 10.1109/access.2023.3341755.

T. Guo, W. Zhao, M. Alrashoud, A. Tolba, S. Firmin, and F. Xia, “Multimodal Educational Data Fusion for Students’ Mental Health Detection,” Ieee Access, vol. 10, pp. 70370–70382, 2022, doi: 10.1109/access.2022.3187502.

T. A. A. Abdullah et al., “Sig-Lime: A Signal-Based Enhancement of Lime Explanation Technique,” Ieee Access, vol. 12, pp. 52641–52658, 2024, doi: 10.1109/access.2024.3384277.

N. R. Raji, R. M. S. Kumar, and C. L. Biji, “Explainable Machine Learning Prediction for the Academic Performance of Deaf Scholars,” Ieee Access, vol. 12, pp. 23595–23612, 2024, doi: 10.1109/access.2024.3363634.

J. Liang et al., “Student Modeling and Analysis in Adaptive Instructional Systems,” Ieee Access, vol. 10, pp. 59359–59372, 2022, doi: 10.1109/access.2022.3178744.

A. Raza et al., “Preventing Road Accidents Through Early Detection of Driver Behavior Using Smartphone Motion Sensor Data: An Ensemble Feature Engineering Approach,” Ieee Access, vol. 11, pp. 138457–138471, 2023, doi: 10.1109/access.2023.3340304.

B.-S. Lin et al., “Fall Detection System With Artificial Intelligence-Based Edge Computing,” Ieee Access, vol. 10, pp. 4328–4339, 2022, doi: 10.1109/access.2021.3140164.

D. Rangelov, M. Boerger, N. Tcholtchev, P. Lämmel, and M. Hauswirth, “Design and Development of a Short-Term Photovoltaic Power Output Forecasting Method Based on Random Forest, Deep Neural Network and LSTM Using Readily Available Weather Features,” Ieee Access, vol. 11, pp. 41578–41595, 2023, doi: 10.1109/access.2023.3270714.

I. Ahmed, M. Alkahtani, Q. S. Khalid, and F. M. Alqahtani, “Improved Commodity Supply Chain Performance Through AI and Computer Vision Techniques,” Ieee Access, vol. 12, pp. 24116–24132, 2024, doi: 10.1109/access.2024.3361756.

A. Raza et al., “Preventing Road Accidents Through Early Detection of Driver Behavior Using Smartphone Motion Sensor Data: An Ensemble Feature Engineering Approach,” Ieee Access, vol. 11, pp. 138457–138471, 2023, doi: 10.1109/access.2023.3340304.

H. S. Mputu, A. Abdel-Mawgood, A. Shimada, and M. S. Sayed, “Tomato Quality Classification Based on Transfer Learning Feature Extraction and Machine Learning Algorithm Classifiers,” Ieee Access, vol. 12, pp. 8283–8295, 2024, doi: 10.1109/access.2024.3352745.

T. Guo, W. Zhao, M. Alrashoud, A. Tolba, S. Firmin, and F. Xia, “Multimodal Educational Data Fusion for Students’ Mental Health Detection,” Ieee Access, vol. 10, pp. 70370–70382, 2022, doi: 10.1109/access.2022.3187502.

S. Vhaduri, S. V. Dibbo, and Y. Kim, “Deriving College Students’ Phone Call Patterns to Improve Student Life,” IEEE Access, vol. 9, pp. 96453–96465, 2021, doi: 10.1109/ACCESS.2021.3093493.

A. A. Norman et al., “Technology Dependency and Impact During COVID-19: A Systematic Literature Review and Open Challenges,” Ieee Access, vol. 11, pp. 40741–40760, 2023, doi: 10.1109/access.2023.3250770.

A. Obeid, R. Ibrahim, and A. Fadhil, “Extended Model of Expectation Confirmation Model to Examine Users’ Continuous Intention Toward the Utilization of E-Learning Platforms,” Ieee Access, vol. 12, pp. 40752–40764, 2024, doi: 10.1109/access.2024.3373190.

H. Han, Z. Feng, W. Du, S. Guo, P. Wang, and T. Xu, “Remote Sensing Image Classification Based on Multi-Spectral Cross-Sensor Super-Resolution Combined With Texture Features: A Case Study in the Liaohe Planting Area,” Ieee Access, vol. 12, pp. 16830–16843, 2024, doi: 10.1109/access.2024.3358812.

K. Katsarou, G. Yu, and F. Beierle, “WhatsNextApp: LSTM-Based Next-App Prediction With App Usage Sequences,” Ieee Access, vol. 10, pp. 18233–18247, 2022, doi: 10.1109/access.2022.3150874.

B. Fu, Q. Shang, T. Sun, and S. Jia, “A Distracted Driving Detection Model Based on Driving Performance,” Ieee Access, vol. 11, pp. 26624–26636, 2023, doi: 10.1109/access.2023.3257238.

Y. Zhang, Y. He, H. Zhang, J. Li, Y. Li, and J. Liu, “Classification Method of Voltage Sag Sources Based on Sequential Trajectory Feature Learning Algorithm,” Ieee Access, vol. 10, pp. 38502–38510, 2022, doi: 10.1109/access.2022.3164675.

T. Xie, Q. Shang, and Y. Yu, “Automated Traffic Incident Detection: Coping With Imbalanced and Small Datasets,” Ieee Access, vol. 10, pp. 35521–35540, 2022, doi: 10.1109/access.2022.3161835.