KLASIFIKASI RESIKO ERGONOMI MENGGUNAKAN ALGORITMA NAIVE BAYES: BERDASARKAN METODOLOGI QUICK EXPOSURE CHECK (QEC)
Keywords:
ergonomics, machine learning, naïve bayes, QEC.Abstract
This research examines the application of machine learning in forecasting and categorizing ergonomic risk levels. Nonetheless, recent research on the integration of Naïve Bayes machine learning with ergonomics remains limited, particularly concerning the Quick Exposure Check (QEC) technique. This study aims to categorize ergonomic risk levels and evaluate the accuracy of classification through machine learning techniques. The employed model is the Naïve Bayes algorithm, grounded in the Quick Exposure Check (QEC) methodology. Data were gathered from evaluations of body posture and occupational characteristics, including strength and duration, and subsequently classified by risk level. The findings of this investigation indicated a total accuracy of 99.00% ± 1.41%, with a micro-average of 99.01%. This degree of accuracy is within the high category. The model exhibits flawless precision and recall for the Medium and High-risk categories, and a recall rate of 93.33% for the Low risk. Misclassification occurred just in a limited number of low-risk instances that were inaccurately classified as medium, suggesting a conservative bias in the evaluation. These results suggest that the model may serve as a dependable tool for ergonomic risk classification, particularly in reliably identifying high riskReferences
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