Classification of Fatigue Levels of Tofu Industrial Workers Based on MOQS and Cardiovascular Load Variables Using Decision Tree Algorithm
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| Title: | Classification of Fatigue Levels of Tofu Industrial Workers Based on MOQS and Cardiovascular Load Variables Using Decision Tree Algorithm |
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| Authors: | null Intan Berlianty, null Miftahol Arifin |
| Source: | Green Engineering: International Journal of Engineering and Applied Science. 2:12-23 |
| Publisher Information: | International Forum of Researchers and Lecturers, 2025. |
| Publication Year: | 2025 |
| Description: | Fatigue is a critical issue in labour-intensive small industries, especially in traditional food production such as tofu manufacturing. This study aims to develop a fatigue classification model using a decision tree algorithm by integrating subjective assessments of the work system through the Macroergonomic Organizational Questionnaire Survey (MOQS) and objective physiological indicators, specifically Cardiovascular Load (CVL). The research was conducted in a tofu home industry located in Kalisari Village, Banyumas, Indonesia. Primary data were collected from 10 workers through MOQS questionnaires and heart rate measurements taken at rest and during work. CVL values were calculated and used as labels for classification into three categories: low, moderate, and high fatigue. Meanwhile, MOQS dimension scores (organization, job, personal, environment, and technology) were transformed into interval data and used as classification features. A decision tree model was built using the CART algorithm and visualized for interpretability. The results show that all workers experienced at least moderate fatigue, with 20% categorized as high fatigue. The decision tree revealed that the dimensions of organizational and personal factors were the most influential in predicting fatigue levels. The model provides a practical and interpretable tool to support decision-making in scheduling, workload balancing, and ergonomic interventions. This study demonstrates a novel approach to combining macroergonomic assessments and physiological data with machine learning for practical fatigue risk management in small-scale food production environments. |
| Document Type: | Article |
| ISSN: | 3063-6833 3063-6841 |
| DOI: | 10.70062/greenengineering.v2i3.220 |
| Accession Number: | edsair.doi...........2b46b65e31e9c1a566404e324ea2237c |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | Fatigue is a critical issue in labour-intensive small industries, especially in traditional food production such as tofu manufacturing. This study aims to develop a fatigue classification model using a decision tree algorithm by integrating subjective assessments of the work system through the Macroergonomic Organizational Questionnaire Survey (MOQS) and objective physiological indicators, specifically Cardiovascular Load (CVL). The research was conducted in a tofu home industry located in Kalisari Village, Banyumas, Indonesia. Primary data were collected from 10 workers through MOQS questionnaires and heart rate measurements taken at rest and during work. CVL values were calculated and used as labels for classification into three categories: low, moderate, and high fatigue. Meanwhile, MOQS dimension scores (organization, job, personal, environment, and technology) were transformed into interval data and used as classification features. A decision tree model was built using the CART algorithm and visualized for interpretability. The results show that all workers experienced at least moderate fatigue, with 20% categorized as high fatigue. The decision tree revealed that the dimensions of organizational and personal factors were the most influential in predicting fatigue levels. The model provides a practical and interpretable tool to support decision-making in scheduling, workload balancing, and ergonomic interventions. This study demonstrates a novel approach to combining macroergonomic assessments and physiological data with machine learning for practical fatigue risk management in small-scale food production environments. |
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| ISSN: | 30636833 30636841 |
| DOI: | 10.70062/greenengineering.v2i3.220 |