Cracking Overtime: Unleashing Machine Learning at PT XYZ with Linear Regression, Neural Networks, and Random Forests

Excessive overtime at PT XYZ is a significant issue for the organization. Besides the significant financial repercussions, they may also affect employee health and productivity. This research aims to forecast future overtime hours, facilitating strategic planning, mitigating excessive overtime, and developing more effective overtime policies. This study employs an overtime realization dataset encompassing many characteristics that influence overtime determinations. The dataset is partitioned into training and testing data to serve as inputs for the three predictive algorithm models: linear regression, artificial neural network, and random forest. The random forest model demonstrates superior performance, evidenced by a mean squared error (MSE) of 158.78, which is proximate to the actual value. The root mean squared error (RMSE) of 12.601 is lower than that of the other two models, indicating a reduced average prediction error. The mean absolute error (MAE) of 8.931 reflects the average deviation from the actual value, while the mean absolute percentage error (MAPE) of 0.336 indicates a prediction error of 34%. Furthermore, the coefficient of determination (R²) of 0.914 signifies that approximately 91.4% of the variation in overtime hours is accounted for, in contrast to the other models, which accounted for 78.8% and 79.6%, respectively. The results indicate that the random forest model demonstrates superior predictive accuracy compared to the other two algorithms, owing to its capacity to handle non-linear data and outliers. Consequently, the random forest model is advocated as the most efficacious method for forecasting the amount of supplementary working hours in the future.