Indonesia is among the countries with the highest accident rates in the world. Fatigue and drowsiness are among the main causes of the increased risks of accidents in the road transport sector. Sleep-related factors (quality and quantity, time of day) and work-related factors significantly affect the development of fatigue. The EEG signal indicator is often referred to as the gold standard for measuring fatigue and drowsiness. However, previous studies focused primarily on the trends of EEG signals under certain conditions but overlooking the development of drowsiness indicators based on EEG signals. Furthermore, existing studies still do not agree on what parameters in the EEG signal indicator are best at detecting drowsiness. Thus, this study aims to design an EEG signal-based drowsiness indicator under simulated driving conditions. Drowsy drivers were monitored through EEG signal indicators and subjective assessments. The methods used in this study include statistical significance tests, logistic regression, and support vector machine. The results showed that sleep deprivation had a significant effect on increasing alpha, beta, and theta waves. In addition, driving duration significantly increased the theta power and all EEG ratios and decreased the beta power in the alert group. The ratio of (θ + α)/β and θ/β in the SD group also showed a considerable increase in the end of driving. Furthermore, sleep status and driving duration both influenced subjective sleepiness. EEG signals combined with sleep status and driving duration factors generated acceptable model accuracies (77.1% and 90.2% in training and testing, respectively), with 90.5% sensitivity and 90% specificity in data test. Support vector machine showed better classification than that of logistics regression, with the linear kernel as the best classifier. Theta power had the highest effect in the model compared with other EEG signals.

Traffic accidents are the eighth leading cause of death worldwide, and each year, Indonesia reports an increasing number of such incidents. Human error, specifically risky driving behaviour such as distraction, is the primary contributors to the accidents. A thorough understanding of the contributing factors to traffic accidents is crucial to enhancing road safety initiatives. Therefore, this study aimed to design a model to assess the effect of road distraction, driving behaviour, and perception of risk on self-reported crashes by private car drivers in Jakarta, Indonesia, as well as formulate strategies to improve safety. This study used a diverse group of 142 drivers from Jakarta as respondents, utilizing a combination of quantitative methods, such as Partial Least Squares Structural Equation Modelling (PLS-SEM) and Pearson's Chi-square tests, complemented by questionnaire instruments such as the Driving behaviour Questionnaire (DBQ), Road Distractions Scale (RDS), and Risk Perception and Regulation Scale (RPRS). The results showed that driver distractions significantly increase the possibility of lapses, while errors, violations, and risk perception significantly affect the incident of traffic incidents. Furthermore, chi-square analysis showed that men are more likely to commit violations and are more distracted by attractive roadside objects compared to women, who reported a higher incidence of lapses and greater disturbance from weather conditions. This study offered strategic recommendations with the potential to lower accident rates and improve driving safety overall.

Fatigue among hospital nurses, resulting from demanding workloads and irregular shift schedules, presents significant risks to both healthcare workers and patient safety. This study developed a fatigue detection model using heart-rate variability (HRV) and investigated its relationship with the Swedish Occupational Fatigue Inventory (SOFI) among nurses. Sixty nurses from a hospital in Depok, Indonesia, participated with HRV data collected via Polar H10 monitors before and after shifts alongside SOFI questionnaires. A mixed ANOVA revealed no significant between-subjects differences in HRV across morning, afternoon, and night shifts. However, within-subjects analyses showed pronounced parasympathetic rebound (elevated Mean RR) and sympathetic withdrawal (reduced Mean HR) post-shift, particularly after afternoon and night shifts, contrasting with stable profiles in morning shifts. Correlation analysis showed significant associations between SOFI dimensions, specifically lack of motivation and sleepiness, with HRV measures, indicating autonomic dysfunction and elevated stress levels. Several machine-learning classifiers were used to develop a fatigue detection model and compare their accuracy. The Fine Gaussian Support Vector Machine (SVM) model achieved the highest performance with 81.48% accuracy and an 81% F1 score, outperforming other models. These findings suggest that HRV-based fatigue detection integrated with machine learning provides a promising approach for continuous nurse fatigue monitoring.

Driver fatigue is a critical factor in traffic accidents and is strongly influenced by sleep deprivation and prolonged driving. This study examines physiological and subjective fatigue responses by combining Heart Rate Variability (HRV) indicators with the Karolinska Sleepiness Scale (KSS) and Rating of Fatigue (ROF) in a controlled driving simulation. Forty participants completed four driving sessions under normal sleep and sleep-deprived conditions, with HRV recorded in each session. The results show that increased driving duration was associated with autonomic imbalance, as reflected in lower mean RR, mean HR, RMSSD, and HF, which were associated with increased fatigue levels. In contrast, sleep duration did not significantly affect most HRV indices, although it had robust effects on subjective fatigue measures (ROF and KSS). Significant correlations were observed between HRV parameters and ROF, indicating that physiological changes aligned with subjective perceptions. To complement the statistical analysis, Logistic Regression and ensemble learning models were applied to classify fatigue-related conditions. XGBoost achieved the highest performance, with an accuracy of 77%, and identified Mean RR, mean HR, and LF as the most influential predictors. These findings indicate that combining HRV metrics with machine learning improves fatigue pattern detection and offers strong potential for developing real-time fatigue monitoring systems. The study highlights the importance of integrating objective and subjective measures to evaluate driver fatigue, particularly in conditions of sleep restriction and extended driving duration.

Introduction: As the largest workforce in the healthcare industry, nurses are essential to maintaining patient safety. Personal and environmental factors, including high demands, irregular work hours, and large workloads, make nurses vulnerable to fatigue.Methods: The study involved 60 participants using several tools for data collection, including the NASA Task Load Index (NASA-TLX) to assess workload, the Swedish Occupational Fatigue Inventory (SOFI) to measured fatigue, heart rate variability (HRV) measurements to evaluated autonomic nervous system activity, electroencephalogram (EEG) analyzed for cognitive functions, and the Psychomotor Vigilance Test (PVT) to assessed cognitive performance.Results: The findings revealed that the nurses experienced high subjective workloads, particularly related to mental, physical, and temporal demands. Fatigue, especially in terms of energy depletion and sleepiness, was significantly reported. HRV data indicated a shift toward parasympathetic dominance after each shift, while EEG results showed decreased theta and alpha wave activity, suggesting increased fatigue. The PVT results showed slower reaction times and more lapses in performance, especially after night shifts, indicating cognitive impairment due to fatigue.Conclusions: These results highlight the considerable impact of high workloads, shift work, and fatigue on the health and performance of nurses. The study suggests that healthcare institutions should implement strategies to reduce workload, manage fatigue, and improve recovery to maintain optimal cognitive performance and overall well-being of nursing staff. These findings can inform policies aimed at improving working conditions and ensuring better care delivery in hospital settings.

The transition from conventional learning into the form of distance learning with an e-learning system due to the COVID-19 pandemic has led to the emergence of a phenomenon called learning loss, namely the difference between current learning outcomes and learning outcomes that should be obtained in normal conditions. The initial survey also shows that there are several obstacles in implementing the e-learning system in this situation, such as low levels of understanding, lack of interaction with teachers or other students, and the habit of continuing to study conventionally. The concept of Gamification 3.0 is considered suitable to overcome this issue because it can increase motivation, engagement, and learning achievement by considering the differences in each individual’s learning style. This gamification model was developed using the ADDIE model framework for further use as an experimental object which aims to analyze the effect of gamification on learning effectiveness and its comparison with conventional learning methods. The results showed that the gamification model was able to improve learning achievement, motivation, and student engagement in learning, although it also had a different effect for each type of learning style. Thus, the concept of gamification 3.0 can be implemented further by the recommendations of this study because it has a positive effect on learning effectiveness and provides personalization for each type of learning style in order to experience optimal results.