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Abstrak
This study elaborates the application of various machine learning (ML) models to measure competency portfolio assessments for job grade conversion needed of employees. The purpose is choose the best ML models to enhance the accuracy, scalability, and fairness. Logistic regression and support vector machines is two traditional methods were evaluated together with random forest and gradient boosting as ensemble models and neural network as deep learning models. This study taken data of 117 employees invited to join on the competency portfolio assessment event on November 2024, all models were measured through cross-validation on parameters such as accuracy, precision and recall by Orange Data Mining. The best performance model in this study is Random Forest, achieving the highest score on Precision and Recall parameters. While Neural Networks demonstrated potential performance that almost has the same result with logistic regression. Based on this research, Random Forest can be prioritized and implemented to help the company to enhance the accuracy of competency portfolio results that needed to develop employees career, eligible competencies, and help decision making of job grade conversion assessment.
Keywords: Comparative Analysis, Competency Portfolio Assessment, Machine Learning
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Referensi
- AlShourbaji, I., Helian, N., Sun, Y., Hussien, A. G., Abualigah, L., & Elnaim, B. (2023). An efficient churn prediction model using gradient boosting machine and metaheuristic optimization. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-41093-6
- Aruna, P., Sujatha D, C., Surya Prakash, S., & Prince, R. (2022). An Automated Self Rating Questionnaire Assessment Tool for the evaluation of Entrepreneurial Competencies using SVM Algorithm. 5th International Conference on Inventive Computation Technologies, ICICT 2022 - Proceedings, 1230–1234. https://doi.org/10.1109/ICICT54344.2022.9850442
- Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.
- https://link.springer.com/article/10.1023/A:1010933404324
- Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20(3), 273-297.https://link.springer.com/article/10.1007/BF00994018
- Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29(5), 1189-1232. https://projecteuclid.org/euclid.aos/1013203451
- He, Q., Chen, L., Tang, W., He, G., Tan, S., Shu, Y., & Jiang, L. (2022). Application and Comparative Analysis of Traditional Machine Learning and Deep Learning in Transmission Line Fault Classification. IMCEC 2022 - IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference, 1715–1719. https://doi.org/10.1109/IMCEC55388.2022.10020121
- Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. https://direct.mit.edu/neco/article/9/8/1735/6109/Long-Short-Term-Memory
- Kamper, H., Niehaus, C., & Wolff, K. (2023). Using Machine Learning to Understand Assessment Practices of Capstone Projects in Engineering. 2023 IEEE IFEES World Engineering Education Forum and Global Engineering Deans Council: Convergence for a Better World: A Call to Action, WEEF-GEDC 2023 - Proceedings. https://doi.org/10.1109/WEEF-GEDC59520.2023.10344177
- Lee, J., & Song, J. H. (2024). How does algorithm-based HR predict employees’ sentiment? Developing an employee experience model through sentiment analysis. Industrial and Commercial Training, 56(4), 273–289. https://doi.org/10.1108/ICT-08-2023-0060
- Patil, A., Sarda, T., Shetye, S., Bachchan, S., & Gutte, V. S. (2023). Comparing Logistic Regression and Tree Models on HR Data. Proceedings - 2023 International Conference on Advanced Computing and Communication Technologies, ICACCTech 2023, 671–677. https://doi.org/10.1109/ICACCTech61146.2023.00113
- Sharma, R., & Sohal, J. (2024). Data-Driven Talent Management: The Impact of Machine Learning on HR Efficiency and Effectiveness. 2024 2nd World Conference on Communication and Computing, WCONF 2024. https://doi.org/10.1109/WCONF61366.2024.10691955
- Tikhonova, M. (2020). Text mining for evaluation of candidates based on their CVs. Communications in Computer and Information Science, 1086CCIS, 184–189. https://doi.org/10.1007/978-3-030-39575-9_19
Referensi
AlShourbaji, I., Helian, N., Sun, Y., Hussien, A. G., Abualigah, L., & Elnaim, B. (2023). An efficient churn prediction model using gradient boosting machine and metaheuristic optimization. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-41093-6
Aruna, P., Sujatha D, C., Surya Prakash, S., & Prince, R. (2022). An Automated Self Rating Questionnaire Assessment Tool for the evaluation of Entrepreneurial Competencies using SVM Algorithm. 5th International Conference on Inventive Computation Technologies, ICICT 2022 - Proceedings, 1230–1234. https://doi.org/10.1109/ICICT54344.2022.9850442
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.
https://link.springer.com/article/10.1023/A:1010933404324
Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20(3), 273-297.https://link.springer.com/article/10.1007/BF00994018
Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29(5), 1189-1232. https://projecteuclid.org/euclid.aos/1013203451
He, Q., Chen, L., Tang, W., He, G., Tan, S., Shu, Y., & Jiang, L. (2022). Application and Comparative Analysis of Traditional Machine Learning and Deep Learning in Transmission Line Fault Classification. IMCEC 2022 - IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference, 1715–1719. https://doi.org/10.1109/IMCEC55388.2022.10020121
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. https://direct.mit.edu/neco/article/9/8/1735/6109/Long-Short-Term-Memory
Kamper, H., Niehaus, C., & Wolff, K. (2023). Using Machine Learning to Understand Assessment Practices of Capstone Projects in Engineering. 2023 IEEE IFEES World Engineering Education Forum and Global Engineering Deans Council: Convergence for a Better World: A Call to Action, WEEF-GEDC 2023 - Proceedings. https://doi.org/10.1109/WEEF-GEDC59520.2023.10344177
Lee, J., & Song, J. H. (2024). How does algorithm-based HR predict employees’ sentiment? Developing an employee experience model through sentiment analysis. Industrial and Commercial Training, 56(4), 273–289. https://doi.org/10.1108/ICT-08-2023-0060
Patil, A., Sarda, T., Shetye, S., Bachchan, S., & Gutte, V. S. (2023). Comparing Logistic Regression and Tree Models on HR Data. Proceedings - 2023 International Conference on Advanced Computing and Communication Technologies, ICACCTech 2023, 671–677. https://doi.org/10.1109/ICACCTech61146.2023.00113
Sharma, R., & Sohal, J. (2024). Data-Driven Talent Management: The Impact of Machine Learning on HR Efficiency and Effectiveness. 2024 2nd World Conference on Communication and Computing, WCONF 2024. https://doi.org/10.1109/WCONF61366.2024.10691955
Tikhonova, M. (2020). Text mining for evaluation of candidates based on their CVs. Communications in Computer and Information Science, 1086CCIS, 184–189. https://doi.org/10.1007/978-3-030-39575-9_19