Development of Artificial Neural Networks Model to Determine Labor Rest Period Based on Environmental Ergonomics

Abstract

Food SMEs (Small and Medium Enterprises) were examples of labor-intensive industry, which involved laborers in pursuing production activities. Food SMEs require complex processes in production activities. Support to increase work productivity and reduce ergonomic risks of the activities was needed. The study was conducted at Tofu SMEs. The determination of the rest period could be developed to give some recovery times to laborers. WBGT (Wet Bulb Globe Temperature) was estimated to determine the rest period. The rest period was determined by the workstation environment and workload labor. ANN (Artificial Neural Networks) model was carried out due to a nonlinear relationship. ANN was used to process the information from the data set and predict the amount of rest period and WBGT. ANN was trained using backpropagation. The backpropagation algorithm used the error value to change the weight with forward and backward propagation. The result showed that dry bulb temperature, heart rate, wet bulb temperature, and gender significantly impacted the rest period and WBGT. A total of 180 data sets from tofu SMEs were divided into training data (80%) and validation data (20%). The optimal ANN structure was determined by four input, four hidden, and two output neurons. The activation function was sigmoid for both layers. SSE (Sum of Squared Errors) was used to obtain the best structure. The value of R2 was equal to above 0.900, which indicated that ANN could model the labor rest period based on environmental ergonomics.