Trend Identification and Prediction of Worker Stress Rate Using Deep Learning Algorithm in Indonesia

Dear Editors,

In a recent article, Digital Innovations for Occupational Safety: Empowering Workers in Hazardous Environments, Dodoo et al. (2024) highlighted how digital innovations can improve work safety in hazardous environments. I appreciate the emphasis on four main categories of digital safety systems: wearable-based systems, augmented/virtual reality–based systems, artificial intelligence (AI)–based systems, and navigation systems. Using deep learning algorithms, it is possible to predict worker stress levels based on identified patterns and trends, which in turn can help in designing more effective interventions to prevent excessive work stress (Awada et al., 2023; Wang et al., 2023). Therefore, the integration of monitoring and prediction systems into existing safety procedures will improve the response to risks (Hu et al., 2010). However, in Indonesia, the main challenges in implementing digital innovations for workplace safety include uneven internet infrastructure, low digital literacy among workers, and the need to improve the digital readiness of society. In addition, the adaptation of supervisory systems for flexible working is also important. Overcoming these barriers will support a safer work environment with digital technology.

In Indonesia, it was shown that machine-learning methods, including classification of algorithms such as K-Nearest Neighbor, Decision Tree, and Support Vector Machine (SVM), have been successfully used to detect human stress levels based on sleep quality with a very high accuracy rate (Zulkarnain et al., 2023). In addition, reports by Wicaksono and Anggarini (2018) show that the work stress rate in Indonesia is 21%, which is relatively lower compared to the global work stress rate of 44%. This research highlights the importance of technology in understanding and managing work stress, as well as the potential of deep learning algorithms in providing effective solutions to this problem in Indonesia.

Understanding trends and predicting worker stress levels using deep learning algorithms is important as work stress is a global issue that affects employees’ mental and physical health as well as company productivity. In Indonesia, with challenges such as imbalanced digital infrastructure and low technological literacy, it is important to adopt approaches that can effectively identify and manage work stress. Deep learning algorithms, with advanced data analysis capabilities, offer a solution that can process large amounts of data to recognize patterns of stress (Cho et al., 2017). This allows companies to take preventive measures, reduce the negative impact of stress on employees, and improve their well-being.

The incorporation of technology in occupational safety systems, as described by Dodoo et al. (2024), can provide an additional layer of protection for workers. By utilizing real-time data and predictive analytics, companies can identify workers at risk of high stress and take preventive measures before serious problems occur. This will not only improve worker well-being but can also reduce workplace incidents and increase productivity.

The prevention of safety issues is a crucial aspect in any industry, and deep learning algorithms offer significant benefits in this regard. With the ability to efficiently process and analyze Big Data, these algorithms can identify patterns invisible to the human eye, predict potential incidents, and provide preventive recommendations. Preventive measures that can be taken include employee training on potential hazards, the use of personal protective equipment, and the implementation of strict safety protocols. Deep learning algorithms can assist in designing training programs tailored to the specific needs of workers based on analysis of behavioral data and previous accidents (Parker & Grote, 2022). In addition, systems equipped with deep learning can automatically monitor working conditions and provide early warnings of potentially dangerous situations (Zhan et al., 2022). Thus, the integration of deep learning algorithms in work safety systems not only increases the effectiveness of prevention but also helps in creating a safer and healthier work environment for workers (Hou et al., 2021). Therefore, utilizing deep learning algorithms in workplace safety is a progressive step that supports efforts to prevent and minimize risks in the workplace.

Overall, this technology offers a highly valuable and innovative approach to managing employee well-being. With advanced data analysis and predictive capabilities, deep learning algorithms can identify patterns that indicate stress levels, allowing companies to take preventive action before stress negatively impacts worker health and productivity. This marks a step forward in the effort to create a more supportive and healthy work environment and shows great potential in the use of AI to improve the quality of work life. In conclusion, deep learning algorithms are an important and effective tool in workplace stress management strategies.