Design and Development of an Intelligent Decision Support System for HR Recruitment


  • Mohaseen Shaikh Dean Placement, Karmayogi Institute of Technology, Pandharpur, Maharashtra, India
  • Dnyaneshwar Ghanawajeer Senior Lecturer, Karmayogi Institute of Technology, Pandharpur, Maharashtra, India
  • Mahendra Sawane Assistant Professor, Department of Computer Science and Engineering, GHRCEM, Pune, Maharashtra, India



Intelligent Decision Support System, Fuzzy Inference System, Recruitment Process Improvement


In the current era of globalized business environments, the effective management of human resources (HRM) plays a critical role in attracting and retaining skilled employees as companies expand into international markets. This study aims to tackle the challenges encountered in the HR recruitment process by designing and developing an intelligent decision support system based on the Mamdani fuzzy inference system. The proposed system harnesses the capabilities of fuzzy logic and advanced analytics to enhance the efficiency, accuracy, and efficacy of candidate selection. By automating tasks, the system streamlines the recruitment process and enhances candidate-job matching through the utilization of linguistic variables such as Technical Skillset, Experience, Certifications, Qualification, and Projects completed. The Mamdani fuzzy inference system facilitates adaptable decision-making based on imprecise or uncertain inputs, capturing expert knowledge and domain-specific heuristics. This approach optimizes the candidate ranking process, contributing to the successful acquisition of talented individuals and fostering organizational growth. By adopting this technology-driven HRM approach, organizations can surmount the limitations of manual methods, elevate their recruitment processes, and make more well-informed hiring decisions. The proposed Mamdani fuzzy inference system presents a robust and efficient solution that capitalizes on the potential of information technology to augment human resource management practices and gain a competitive edge in talent acquisition.


N. Ahmad and A. N. Abd Alla, "Smart Evaluation for Job Vacancy Application System," in Second International Conference on the Applications of Digital Information and Web Technologies, IEEE, 2009.

Y. Amre, M. Ahmedabadwala, and H. Damania, "Decision Making Approach in Recruitment using Neuro-Fuzzy System," International Journal of Computer Applications, Vol. 180, pp. 29-33, 2018.

M. K. Vijaymeena and K. Kavitha, "A survey on similarity measures in text mining," Machine Learning and Applications: An International Journal, Vol. 3, No. 2, pp. 19-28, 2016.

A. Mohamed et al., "Smart talents recruiter-resume ranking and recommendation system," in IEEE International Conference on Information and Automation for Sustainability (ICIAfS), IEEE, 2018.

N. Sivaramakrishnan et al., "Validating effective resume based on employer’s interest with recommendation system," International Journal of Pure and Applied Mathematics, Vol. 119, No. 12, pp. 13261-13272, 2018.

Gopalakrishna, S. Tangadle, and V. Vijayaraghavan, "Automated tool for Resume classification using Semantic analysis," International Journal of Artificial Intelligence and Applications (IJAIA), Vol. 10, No. 1, 2019.

D. Gerard, V. Teja, and A. Santhanavijayan, "A novel firefly driven scheme for resume parsing and matching based on entity linking paradigm," Journal of Discrete Mathematical Sciences and Cryptography, Vol. 23, No. 1, pp. 157-165, 2020.




How to Cite

Shaikh, M., Ghanawajeer, D., & Sawane, M. (2023). Design and Development of an Intelligent Decision Support System for HR Recruitment. Asian Journal of Computer Science and Technology, 12(2), 24–30.