Article Sidebar

Submitted
April 26, 2025
Accepted
July 3, 2025
Published
July 6, 2025
Download
PDF
Statistic
Read Counter : 512 Download : 330

Main Article Content

Abstract

Microsleep presents a critical safety challenge for Indonesian drivers, particularly affecting long-distance transportation where existing detection methods remain costly and impractical for widespread deployment. This study introduces a novel application of Random Forest algorithm specifically tailored to Indonesian driving contexts, utilizing locally-sourced accident data combined with driver behavioral surveys to predict microsleep likelihood. Unlike previous studies that relied primarily on physiological monitoring or international datasets, this research leverages accessible vehicle and environmental variables including driving duration, road conditions, weather patterns, and work schedules from National Transportation Safety Committee (KNKT) records spanning 2013-2023. The Random Forest model, configured with 100 trees and maximum depth of 10, demonstrated 87.50% overall accuracy with perfect recall (1.00) for microsleep detection when validated using stratified k-fold cross-validation. This study uniquely contributes to the field by demonstrating that context-specific environmental and behavioral factors can effectively predict microsleep incidents without expensive physiological monitoring, offering a practical foundation for developing cost-effective vehicle safety systems tailored to Indonesian road conditions and driving patterns. The findings provide actionable insights for transportation policy development and establish a framework for implementing affordable microsleep detection in developing countries with similar traffic characteristics.

Keywords

Microsleep Random Forest Driver Fatigue Predictive Modeling Vehicle Safety System

Article Details

License

Copyright (c) 2025 Astri Lestari, Ainun Rahmawati, Siti Shofiah, Joko Siswanto, Benny Hamdi Rhoma Putra

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to Cite
Lestari, A., Rahmawati, A., Shofiah, S., Siswanto, J., & Putra, B. H. R. (2025). Predictive Modeling of Microsleep Incidents in Indonesian Drivers Using Random Forest: A Data-Driven Approach for Road Safety Enhancement. Jurnal Sains, Nalar, Dan Aplikasi Teknologi Informasi, 4(2), 62–72. https://doi.org/10.20885/snati.v4.i2.39984
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. World Health Organization, Global status report on road safety 2023, vol. 15, no. 4. 2023.
  2. KNKT, “Buku statistik investigasi kecelakaan transportasi knkt 2022,” no. 5, 2022.
  3. M. E. Elidrissi, E. Essoukaki, L. Ben Taleb, A. Mouhsen, and M. Harmouchi, “Drivers’ drowsiness detection based on an optimized random forest classification and single-channel electroencephalogram,” Int. J. Electr. Comput. Eng., vol. 13, no. 3, pp. 3398–3406, 2023, doi: 10.11591/ijece.v13i3.pp3398-3406.
  4. M. L. S. Zainy, G. B. Pratama, R. R. Kurnianto, and H. Iridiastadi, “Fatigue Among Indonesian Commercial Vehicle Drivers: A Study Examining Changes in Subjective Responses and Ocular Indicators,” Int. J. Technol., vol. 14, no. 5, pp. 1039–1048, 2023, doi: 10.14716/ijtech.v14i5.4856.
  5. A. Guerra, V. Gadhiya, and P. Srisurin, “ASEAN Engineering Journal ,” ASEAN Eng. J., vol. 12, no. 3, pp. 27–37, 2022, doi: https://doi.org/10.11113/aej.v12.17601.
  6. P. Philip et al., “Fatigue, sleepiness, and performance in simulated versus real driving conditions,” Sleep, vol. 28, no. 12, pp. 1511–1516, 2005, doi: 10.1093/sleep/28.12.1511.
  7. A. Bagoes Prasetya, B. Kurniawan, and I. Wahyuni, “Faktor-Faktor yang Berhubungan dengan Safety Driving pada Pengemudi Bus Ekonomo Trayek Semarang-Surabaya di Terminal Terboyo Semarang,” J. Kesehat. Masy., vol. 4, no. 3, pp. 292–302, 2016, [Online]. Available: http://ejournal-s1.undip.ac.id/index.php/jkm.
  8. Y. M. Charisma, Ekawati, and W. Baju, “Faktor-Faktor yang berhubungan dengan defensive driving pada pengemudi Bus Rapid Transit (BRT) Trans Semarang Koridor II,III, dan, VI,” J. Kesehat. Masy., vol. 7, no. 1, pp. 365–373, 2019, [Online]. Available: http://ejournal3.undip.ac.id/index.php/jkm.
  9. W. G. Putro, A. P. Siregar, H. M. Hasan, and M. Melizsa, “Faktor-Faktor Yang Berhubungan Dengan Perilaku Aman Berkendara Pada Pengemudi Bus Trayek Lebak Bulus/Ciputat-Bandung Di Pt Primajasa Perdanaraya Utama Tahun 2022,” J. Heal. Res. Sci., vol. 2, no. 01, pp. 21–31, 2022, doi: 10.34305/jhrs.v2i1.476.
  10. I. Imanuddin, F. Alhadi, R. Oktafian, and A. Ihsan, “Deteksi Mata Mengantuk pada Pengemudi Mobil Menggunakan Metode Viola Jones,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 18, no. 2, pp. 321–329, 2019, doi: 10.30812/matrik.v18i2.389.
  11. M. E. Arianto, J. D. Saptadi, and A. Nurhasanah, “Jurnal Ilmu Kesehatan Masyarakat Faktor-Faktor yang Berhubungan dengan Perilaku Aman,” no. April 2023, pp. 101–109, 2024.
  12. D. F. Widyastuti, Nur Rachmi: Brilianti, “The Impact of Drowsiness on Road Traffic Accidents in Yogyakarta,” J. Sci. Res. Educ. Technol., vol. 3, no. 4, pp. 1651–1661, 2024.
  13. K. Khotimah and A. Sjafruddin, “Analysis of Driver Fatigue Caused By Highway Hypnosis in Monotonous Geometrics of Road: State of the Arth Review,” Int. Conf. Civil, Struct. Transp. Eng., no. June, 2024, doi: 10.11159/iccste24.175.
  14. Y. Uchiyama et al., “Convergent validity of video-based observer rating of drowsiness, against subjective, behavioral, and physiological measures,” PLoS One, vol. 18, no. 5 May, pp. 1–16, 2023, doi: 10.1371/journal.pone.0285557.
  15. T. Åkerstedt, A. Anund, J. Axelsson, and G. Kecklund, “Subjective sleepiness is a sensitive indicator of insufficient sleep and impaired waking function,” J. Sleep Res., vol. 23, no. 3, pp. 242–254, 2014, doi: 10.1111/jsr.12158.
  16. K. Kapeller, C; Ogawa, H; Schalk, G; Kunii, N; Coon, WG; Scharinger, J; Guger, C; Kamada, “Real-Time Detection and Discrimination of Visual Perception Using Electrocorticographic Signals,” J. Neural Eng. Accept., pp. 0–23, 2018, [Online]. Available: https://iopscience.iop.org/article/10.1088/2053-1583/abe778.
  17. S. M. Shah, S. Zhaoyun, K. Zaman, A. Hussain, M. Shoaib, and P. Lili, “A Driver Gaze Estimation Method Based on Deep Learning,” Sensors, vol. 22, no. 10, pp. 1–22, 2022, doi: 10.3390/s22103959.
  18. L. Breiman, “Random Forestsの寄与率を用いた効率的な 特徴選択法の提案,” 中部大学工学部情報工学科 卒業論文, pp. 5–32, 2001.
  19. S. K. Satapathy, S. Saravanan, S. Mishra, and S. N. Mohanty, “A Comparative Analysis of Multidimensional COVID-19 Poverty Determinants: An Observational Machine Learning Approach,” New Gener. Comput., vol. 41, no. 1, pp. 155–184, 2023, doi: 10.1007/s00354-023-00203-8.
  20. J. Zhang et al., “A Machine Learning Method for the Risk Prediction of Casing Damage and Its Application in Waterflooding,” Sustain., vol. 14, no. 22, 2022, doi: 10.3390/su142214733.
  21. O. O. Ajayi, A. M. Kurien, K. Djouani, and L. Dieng, “Analysis of Road Roughness and Driver Comfort in ‘Long-Haul’ Road Transportation Using Random Forest Approach,” Sensors, vol. 24, no. 18, 2024, doi: 10.3390/s24186115.
  22. B. D. Hartanto, “Analisis Perilaku Pengemudi Truk Serta Kontribusinya Pada Kecelakaan,” J. Penelit. Transp. Darat, vol. 23, no. 1, pp. 79–87, 2021, doi: 10.25104/jptd.v23i1.1749.
  23. F. Yan, M. Liu, C. Ding, Y. Wang, and L. Yan, “Driving style recognition based on electroencephalography data from a simulated driving experiment,” Front. Psychol., vol. 10, no. MAY, 2019, doi: 10.3389/fpsyg.2019.01254.
  24. E. Ardizzone, M. La Cascia, and M. Morana, “Probabilistic corner detection for facial feature extraction,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 5716 LNCS, pp. 461–470, 2009, doi: 10.1007/978-3-642-04146-4_50.
  25. A. Sorayaie Azar et al., “Application of machine learning techniques for predicting survival in ovarian cancer,” BMC Med. Inform. Decis. Mak., vol. 22, no. 1, pp. 1–24, 2022, doi: 10.1186/s12911-022-02087-y.
  26. W. Shariff et al., “Neuromorphic Driver Monitoring Systems: A Computationally Efficient Proof-of-Concept for Driver Distraction Detection,” IEEE Open J. Veh. Technol., vol. 4, no. October, pp. 836–848, 2023, doi: 10.1109/OJVT.2023.3325656.
  27. D. Adiyanto, B. Kurniawan, and I. Wahyuni, “Faktor-Faktor Yang Berhubungan Dengan Perilaku Safety Driving Pada Pengemudi Bus Rapid Transit Trans Semarang Koridor I,” J. Kesehat. Masy., vol. 9, no. 1, pp. 96–103, 2021.
  28. D. Fevyer and R. Aldred, “Rogue drivers, typical cyclists, and tragic pedestrians: a Critical Discourse Analysis of media reporting of fatal road traffic collisions,” Mobilities, vol. 17, no. 6, pp. 759–779, 2022, doi: 10.1080/17450101.2021.1981117.
  29. M. Ameen Sulaiman and I. Sarhan Kocher, “A systematic review on Evaluation of Existing Face Eyes Detection Algorithms for Monitoring Systems,” Acad. J. Nawroz Univ., vol. 11, no. 1, pp. 57–72, 2022, doi: 10.25007/ajnu.v11n1a1234.
  30. S. Badrloo, M. Varshosaz, S. Pirasteh, and J. Li, “Image-Based Obstacle Detection Methods for the Safe Navigation of Unmanned Vehicles: A Review,” Remote Sens., vol. 14, no. 15, pp. 1–26, 2022, doi: 10.3390/rs14153824.
  31. J. B. Awotunde et al., “An Ensemble Tree-Based Model for Intrusion Detection in Industrial Internet of Things Networks,” Appl. Sci., vol. 13, no. 4, 2023, doi: 10.3390/app13042479.
  32. A. Al Ali, A. M. Khedr, M. El-Bannany, and S. Kanakkayil, “A Powerful Predicting Model for Financial Statement Fraud Based on Optimized XGBoost Ensemble Learning Technique,” Appl. Sci., vol. 13, no. 4, 2023, doi: 10.3390/app13042272.
  33. W. Yotsawat, K. Phodong, T. Promrat, and P. Wattuya, “Bankruptcy prediction model using cost-sensitive extreme gradient boosting in the context of imbalanced datasets,” Int. J. Electr. Comput. Eng., vol. 13, no. 4, pp. 4683–4691, 2023, doi: 10.11591/ijece.v13i4.pp4683-4691.
  34. T. Chen, X. Shi, and Y. D. Wong, “Key feature selection and risk prediction for lane-changing behaviors based on vehicles’ trajectory data,” Accid. Anal. Prev., vol. 129, pp. 156–169, 2019, doi: 10.1016/j.aap.2019.05.017.
  35. S. Mcmurray and A. H. Sodhro, A Study on ML-Based Software Defect Detection for Security Traceability in Smart Healthcare Applications, vol. 23, no. 7. 2023.
  36. I. Tessaro, V. C. Mariani, and L. dos S. Coelho, “Machine Learning Models Applied to Predictive Maintenance in Automotive Engine Components,” p. 26, 2020, doi: 10.3390/iecat2020-08508.
  37. M. Munsarif and M. Sam’ansafuan, “Peer to peer lending risk analysis based on embedded technique and stacking ensemble learning,” Bull. Electr. Eng. Informatics, vol. 11, no. 6, pp. 3483–3489, 2022, doi: 10.11591/eei.v11i6.3927.
  38. O. S. Durowoju, R. O. Obateru, S. Adelabu, and A. Olusola, “Urban change detection: assessing biophysical drivers using machine learning and Google Earth Engine,” Environ. Monit. Assess., vol. 197, no. 4, 2025, doi: 10.1007/s10661-025-13863-4.
  39. Y. Huang, Y. Mao, L. Xu, J. Wen, and G. Chen, “Exploring risk factors for cervical lymph node metastasis in papillary thyroid microcarcinoma: construction of a novel population-based predictive model,” BMC Endocr. Disord., vol. 22, no. 1, pp. 1–13, 2022, doi: 10.1186/s12902-022-01186-1.
  40. M. E. Sahin, “Real-Time Driver Drowsiness Detection and Classification on Embedded Systems Using Machine Learning Algorithms,” Trait. du Signal, vol. 40, no. 3, pp. 847–856, 2023, doi: 10.18280/ts.400302.
  41. R. Wang, L. Cai, J. Zhang, M. He, and J. Xu, “Prediction of Acute Respiratory Distress Syndrome in Traumatic Brain Injury Patients Based on Machine Learning Algorithms,” Med., vol. 59, no. 1, 2023, doi: 10.3390/medicina59010171.
  42. L. Vinet and A. Zhedanov, “A ‘missing’ family of classical orthogonal polynomials,” J. Phys. A Math. Theor., vol. 44, no. 8, pp. 1–13, 2011, doi: 10.1088/1751-8113/44/8/085201.
  43. M. Ferrara and L. De Gennaro, “How much sleep do we need?,” Sleep Med. Rev., vol. 5, no. 2, pp. 155–179, 2001, doi: 10.1053/smrv.2000.0138.
  44. J. F. May and C. L. Baldwin, “Driver fatigue: The importance of identifying causal factors of fatigue when considering detection and countermeasure technologies,” Transp. Res. Part F Traffic Psychol. Behav., vol. 12, no. 3, pp. 218–224, 2009, doi: 10.1016/j.trf.2008.11.005.
  45. G. Borghini, L. Astolfi, G. Vecchiato, D. Mattia, and F. Babiloni, “Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness,” Neurosci. Biobehav. Rev., vol. 44, pp. 58–75, 2014, doi: 10.1016/j.neubiorev.2012.10.003.
  46. Y. Feng, S. Yu, H. Peng, Y. R. Li, and J. Zhang, “Detect Faces Efficiently: A Survey and Evaluations,” IEEE Trans. Biometrics, Behav. Identity Sci., vol. 4, no. 1, pp. 1–18, 2022, doi: 10.1109/TBIOM.2021.3120412.
  47. G. Maycock, “Sleepiness and driving: The experience of U.K. car drivers,” Accid. Anal. Prev., vol. 29, no. 4 SPEC. ISS., pp. 453–462, 1997, doi: 10.1016/s0001-4575(97)00024-9.
  48. W. W. Wierwille and L. A. Ellsworth, “Evaluation of driver drowsiness by trained raters,” Accid. Anal. Prev., vol. 26, no. 5, pp. 571–581, 1994, doi: 10.1016/0001-4575(94)90019-1.
  49. S. R. Shinde, S. D. Thepade, A. M. Bongale, and D. Dharrao, “Comparison of Fine-Tuned Networks on Generalization for Face Spoofing Detection,” Rev. d’Intelligence Artif., vol. 38, no. 1, pp. 93–101, 2024, doi: 10.18280/ria.380110.
  50. S. Vasavi, K. Aswarth, T. Sai Durga Pavan, and A. Anu Gokhale, “Predictive analytics as a service for vehicle health monitoring using edge computing and AK-NN algorithm,” Mater. Today Proc., vol. 46, pp. 8645–8654, 2021, doi: 10.1016/j.matpr.2021.03.658.
  51. S. S. Sharan, R. Viji, R. Pradeep, and V. Sajith, “Driver Fatigue Detection Based on Eye State Recognition Using Convolutional Neural Network,” Proc. 4th Int. Conf. Commun. Electron. Syst. ICCES 2019, no. Icces, pp. 2057–2063, 2019, doi: 10.1109/ICCES45898.2019.9002215.