Sebuah Pendekatan Hybrid Metodologi SVM dan NLP Mampu Mengukur Perilaku Sumber Daya Manusia Dikorelasikan Kecerdasan Emosional dalam Perspektif Kerja: Sebuah Studi Kasus di Sulawesi Selatan - Indonesia
DOI:
https://doi.org/10.47747/snfmi.v3i1.3123Abstract
This study introduces a new paradigm that combines Natural Language Processing (NLP) and Support Vector Machine (SVM) methods to measure human resource behavior correlated with emotional intelligence. This approach not only improves employee performance and satisfaction but also has practical applications in optimizing decision-making processes and enhancing regional operations. The framework addresses high turnover, low team performance, and recruitment challenges, aiming to support strategic decision-making in government agencies and companies. The model uses 9 factors and 20 criteria in NLP and analyzes 600 respondent comments using sentiment analysis, including 9 text criteria in SVM, with 3 indicators from 7 companies and 3 government agencies. The results obtained show a value of 0.93. This study shows that the use of hybrids to optimize human resource behavior, emotional intelligence in a work perspective, technical criteria, and public sentiment produces inclusive data. This study lays a strong foundation for future research with practical applications and makes a significant contribution to the field of management science, especially human resource behavior and beyond.
References
Ahsaan, S. U., Ahmed, A., Hussain, S., & Khan, R. (2022). A hybrid support vector machine algorithm for big data heterogeneity using machine learning. Symmetry, 14(11), 2344. https://doi.org/10.3390/sym14112344
Aldino, A. A., Satria, R., Handoko, E., & Wibowo, A. (2021). Application of support vector machine (SVM) algorithm in classification of low-cape communities in Lampung Timur. Building of Informatics, Technology and Science (BITS), 3(3), 325–330. https://ieomsociety.org/india2021/proceedings/
Alzoubi, H. M., & Aziz, R. (2021). Does emotional intelligence contribute to quality of strategic decisions? The mediating role of open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 130. https://doi.org/10.3390/joitmc7020130
Banmairuroy, W., Kritjaroen, T., & Homsombat, W. (2022). The effect of knowledge-oriented leadership and human resource development on sustainable competitive advantage through organizational innovation’s component factors: Evidence from Thailand’s new S-curve industries. Asia Pacific Management Review, 27(3), 200–209. https://doi.org/10.1016/j.apmrv.2021.09.004
Caucheteux, C., & King, J.-R. (2022). Brains and algorithms partially converge in natural language processing. Communications Biology, 5(1), 134. https://doi.org/10.1038/s42003-022-03036-1
Chigeda, F., Ndofirepi, T. M., & Steyn, R. (2022). Continuance in organizational commitment: The role of emotional intelligence, work‐life balance support, and work‐related stress. Global Business and Organizational Excellence, 42(1), 22–38. https://doi.org/10.1002/joe.22171
Chin, S. T. S. (2021). Influence of emotional intelligence on the workforce for Industry 5.0. Journal of Human Resources Management Research, 2021(2021), 882278. https://doi.org/10.5171/2021.882278
Ebrahimi, P., Gharleghi, B., Shafiei, N., & Tabatabaei, M. (2022). Startups and consumer purchase behavior: Application of support vector machine algorithm. Big Data and Cognitive Computing, 6(2), 34. https://doi.org/10.3390/bdcc6020034
Ferreira, B., Curado, C., & Oliveira, M. (2022). The contribution of knowledge management to human resource development: A systematic and integrative literature review. Journal of the Knowledge Economy, 13(3), 2319–2347. https://doi.org/10.1007/s13132-021-00791-8
Gaye, B., Zhang, D., & Wulamu, A. (2021). Improvement of support vector machine algorithm in big data background. Mathematical Problems in Engineering, 2021(1), 5594899. https://doi.org/10.1155/2021/5594899
Harrison, C. J., & Sidey-Gibbons, C. J. (2021). Machine learning in medicine: A practical introduction to natural language processing. BMC Medical Research Methodology, 21(1), 158. https://doi.org/10.1186/s12874-021-01364-5
Jaffu, R., & Changalima, I. A. (2023). Human resource development practices and procurement effectiveness: Implications from public procurement professionals in Tanzania. European Journal of Management Studies, 28(2), 149–169. https://doi.org/10.1108/EJMS-05-2022-0032
Jena, L. K., & Goyal, S. (2022). Emotional intelligence and employee innovation: Sequential mediating effect of person-group fit and adaptive performance. European Review of Applied Psychology, 72(1), 100729. https://doi.org/10.1016/j.erap.2021.100729
Kersloot, M. G., Eijkemans, M. J. C., Kalkman, C. J., & Peelen, L. M. (2020). Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: A systematic review and recommendations for future studies. Journal of Biomedical Semantics, 11(1), 1–21. https://doi.org/10.1186/s13326-020-00227-6
Khyani, D., Qureshi, S., Hussain, M., & Ali, S. (2021). An interpretation of lemmatization and stemming in natural language processing. Journal of University of Shanghai for Science and Technology, 22(10), 350–357.
Kuchinke, K. P. (2023). Phenomenology and human resource development: Philosophical foundations and implication for research. Human Resource Development Review, 22(1), 36–58. https://doi.org/10.1177/15344843221106763
Maulud, D. H., Razak, M. A. M., Azmi, M. N., & Hamid, M. A. (2021). State of art for semantic analysis of natural language processing. Qubahan Academic Journal, 1(2), 21–28. https://doi.org/10.48161/qaj.v1n2a39
Pillai, A. S. (2022). A natural language processing approach to grouping students by shared interests. Journal of Empirical Social Science Studies, 6(1), 1–16.
Piwowar-Sulej, K. (2021). Human resources development as an element of sustainable HRM – With the focus on production engineers. Journal of Cleaner Production, 278, 124008. https://doi.org/10.1016/j.jclepro.2020.124008
Saha, S., Dutta, S., Banerjee, S., & Das, A. (2023). Emotional intelligence and leadership: Insights for leading by feeling in the future of work. International Journal of Manpower, 44(4), 671–701. https://doi.org/10.1108/IJM-09-2022-0429
Swanson, R. A. (2022). Foundations of human resource development (3rd ed.). Berrett-Koehler Publishers.
Votto, A. M., Moreira, M. F., Oliveira, L. P., & Dias, R. (2021). Artificial intelligence in tactical human resource management: A systematic literature review. International Journal of Information Management Data Insights, 1(2), 100047. https://doi.org/10.1016/j.jjimei.2021.100047
Zeng, W., & Coecke, B. (2016). Quantum algorithms for compositional natural language processing. arXiv Preprint, arXiv:1608.01406. https://arxiv.org/abs/1608.01406
