Abstract: This study applies the Random forest algorithm to classify and evaluate the effectiveness of business human resources (HR) data, focusing on its potential in supporting strategic decision-making and enhancing organizational efficiency. The research introduces a model that automates the categorization of HR data, including employee records, performance evaluations, and training activities, using the Random Forest method. By constructing both classification and effectiveness assessment models, the study aims to provide businesses with a robust tool for managing and evaluating employee contributions. Key HR metrics were analyzed and categorized, leading to the creation of an effectiveness evaluation model that offers objective insights into employee performance. The Random forest algorithm’s accuracy and stability were validated through cross-validation techniques, proving it to be effective in categorizing employee data and identifying different workforce groups. The models developed in this study are designed to support HR managers in optimizing human resource allocation, improving employee satisfaction, and driving overall business performance. The paper also discusses how the model can be optimized further by expanding data sources and applying it to practical business scenarios.

Fangfang Dong, “Random Forest Algorithm for HR Data Classification and Performance Analysis in Cloud Environments” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151147

@article{Dong2024,
title = {Random Forest Algorithm for HR Data Classification and Performance Analysis in Cloud Environments},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151147},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151147},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Fangfang Dong}
}