Development of Artificial Neural Networks Model to Determine Labor Rest Period Based on Environmental Ergonomics
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 need...
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Indonesia
2023-07-01
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| Series: | International Journal of Technology |
| Subjects: | |
| Online Access: | https://ijtech.eng.ui.ac.id/article/view/3854 |
| Summary: | 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. |
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| ISSN: | 2086-9614 2087-2100 |