Supply Chain Analysis in the Health Sector Using Gradient Boosting Regression Algorithm - Wijaya, Bayu Angga; Halawa, Nestina (Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia, 2025)
Supply chain analysis in healthcare is a crucial aspect in ensuring efficient and optimized resource distribution. This study uses the Gradient Boosting Regression algorithm to predict demand in healthcare supply chains to improve the accuracy of stock planning and management trained using supply datasets from hospitals. The model evaluation results show that most of the predictions are close to the actual values, as seen from the points clustered around the reference line. Despite the slight deviations, the Mean Absolute Error (MAE) value of 157.16 indicates that the average prediction error is relatively small compared to the demand scale which ranges from 0 to 14,000. This indicates that the Gradient Boosting Regression model performs reasonably well in estimating supply chain demand in the healthcare sector. Thus, this approach has the potential to be used in more accurate decision-making, in order to improve the efficiency of distribution and availability of health resources