Design and Implementation of a Human Resource System in Small and Medium Enterprises (SMEs) Using a Data Mining Approach (Case Study: Tile and Ceramic Companies in Yazd Province)
Subject Areas :
amirehsan eshaghiyeh firuzabadi
1
,
Mohammad Zarei Mahmoudabadi
2
*
1 - Postdoctoral Researcher, Department of Managementm, Meybod University, Meybod, Iran.
2 - Associate Professor, Department of Industrial Management, Meybod University, Meybod, Iran
Keywords: Human Resource System, Performance Evaluation, Data Mining, Small and Medium Enterprises (SMEs), Machine Learning,
Abstract :
Abstract Background and Objectives: This study aims to design and implement an intelligent human resource performance evaluation system for Small and Medium Enterprises (SMEs) using data mining techniques, with a focus on the tile and ceramic companies in Yazd, Iran, as a key industrial region. Methodology: In this applied research, 10 key performance indicators were identified based on theoretical frameworks and literature review. Data were collected from the HR databases of manufacturing SMEs in Yazd's tile and ceramic sector. After data preprocessing, three data mining algorithms -Decision Tree, K-Nearest Neighbors (K-NN), and Naive Bayes- were implemented in Python (Google Colab environment). The algorithms' performance was evaluated using accuracy, precision, recall, and F1-score metrics. Findings: Comparative analysis revealed that the K-NN algorithm achieved superior performance with 98.3% classification accuracy, significantly outperforming the Naive Bayes approach. The innovative findings demonstrate that the proposed K-NN-based system can accurately evaluate both existing and newly hired employees' performance with minimal error. Conclusion: The implementation of this intelligent system can substantially enhance HR management processes in SMEs, enabling more accurate and efficient workforce organization. This study provides practical solutions for industrial managers and paves the way for future research on AI applications in human resource management
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