Fatigue Management: Machine Learning Application for Predicting Mining Worker Fatigue

Mining workers can experience various kinds of physical and psychological impacts that will affect the emergence of fatigue. A lot of working hours and shift work mechanisms will drain many employees' energy. Research related to fatigue gives results that this affects employee performance, and even worse. This may have an impact on the emergence of an incident at work. Even greater impact will affect the company's business activities. Many mining companies have used fatigue monitoring mechanisms, but most of them provide results that are less fast and are less able to follow the pattern produced by each individual employee. This study shows the creation of a fatigue prediction model for employees using machine learning. Machine learning can identify potential whether employees are experiencing fatigue or not, so that it can assist management in making decisions. The collected data has 2 categories, namely fit and unfit. This research also uses the smote technique to balance the model so it doesn't lean towards one classes. Based on this study, it was found that the Random Forest algorithm was able to provide the best results which gives 95.4% accuracy compared to Decision Tree and Logistic Regression. According to the findings, it was found that there were variables that will have a major impact on the prediction results, namely sleep patterns and drug consumption since this data was taken during the Covid-19 pandemic. This research can also be used as a reference for establishing a model for determining fatigue both during Covid-19 and after Covid-19.