<![CDATA[Background : Due to increasing work pressures triggered by industrialization, high workloads, and unsafe environmental conditions, occupational stress has now become a major issue impacting worker health and productivity. Physical exposures such as noise, heat, poor air quality, and inadequate ergonomics further exacerbate stress responses, particularly in the manufacturing sectors of developing countries with limited occupational health and safety (OHS) systems. Objective : This study seeks to (1) identify significant environmental factors that affect work stress, (2) develop a discriminative function to group workers into high and low stress categories, and (3) evaluate the accuracy and interpretability of the model. This research is expected to make an empirical contribution to the study of K3 with a data-based multivariate approach, as well as provide a scientific basis for improving occupational health policy and management in the manufacturing industry sector. Methods : This study used an explanatory quantitative approach to examine the relationships between variables and to differentiate groups of workers with different stress levels based on the physical conditions of the work environment and ergonomics. Using a sample of 200 workers in a labor-intensive manufacturing industry, this study developed a predictive model using discriminant analysis to classify workers into high- and low-stress categories. The reliability test was carried out using Cronbach's Alpha coefficient, with a value of ≥0.7 as the minimum limit of internal consistency. Data analysis was performed using JASP 0.18.0.0 (Intel) software. Results : The Tests for Multivariate Normality table shows the results of multivariate normality tests based on two parameters, namely skewness (distribution skewness) and kurtosis (distribution skewness). The p-value for skewness is 0.762 and for kurtosis is 0.547, both greater than the threshold of 0.05. Thus, it can be concluded that the data in this model meets the assumption of multivariate normality, since there is no significant difference between the actual data distribution and the multivariate normal distribution. Conclusion : This study shows that physical environmental factors have a significant effect on the stress level of workers in labour-intensive industries. Through linear discriminant analysis, models with one discriminant function achieved 60% accuracy, demonstrating moderate predictive ability. Ergonomics, air quality (PM2.5), and noise variables had the largest contribution to work stress, while temperature, lighting, and age had a smaller influence. These results confirm the importance of controlling physical factors in creating a healthy and productive work environment. The discriminant model used has met the assumption of multivariate normality, so the results are valid for further analysis. Keywords: Occupational Stress; Environmental; Ergonomic; Discriminant Analysis]]>
