Supply Chain Analysis in the Health Sector Using Gradient Boosting Regression Algorithm

Authors

  • Bayu Angga Wijaya Universitas Prima Indonesia
  • Nestina Halawa Univeristas Prima Indonesia

DOI:

https://doi.org/10.34012/jutikomp.v8i1.6822

Keywords:

Gradient Boosting Regression, Healthcare Supply Chain, Demand Prediction, Stock Management

Abstract

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

Author Biography

Nestina Halawa, Univeristas Prima Indonesia

Program Studi Tekinik Informatika

Fakultas Sains dan Teknologi

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Published

2025-04-30

How to Cite

Wijaya, B. A., & Halawa, N. (2025). Supply Chain Analysis in the Health Sector Using Gradient Boosting Regression Algorithm. JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP), 8(1), 81-90. https://doi.org/10.34012/jutikomp.v8i1.6822