(Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)(2) Aida Mustapha
(Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)(3) Siti Solehah Tenah
(Research Management Centre, University Tun Hussein Onn Malaysia, Malaysia)(4) * Salama A Mostafa
(Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)(5) Nazim Razali
(Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)*corresponding author
AbstractAmong the challenges in industrial revolutions, 4.0 is managing organizations’ talents, especially to ensure the right person for the position can be selected. This study is set to introduce a predictive approach for talent identification in the sport of netball using individual player qualities in terms of physical fitness, mental capacity, and technical skills. A data mining approach is proposed using three data mining algorithms, which are Decision Tree (DT), Neural Network (NN), and Linear Regressions (LR). All the models are then compared based on the Relative Absolute Error (RAE), Mean Absolute Error (MAE), Relative Square Error (RSE), Root Mean Square Error (RMSE), Coefficient of Determination (R2), and Relative Square Error (RSE). The findings are presented and discussed in light of early talent spotting and selection. Generally, LR has the best performance in terms of MAE and RMSE as it has the lowest values among the three models.
|
DOIhttps://doi.org/10.26555/ijain.v8i1.707 |
Article metricsAbstract views : 1380 | PDF views : 216 |
Cite |
Full Text Download
|
References
[1] N. Razali, A. Mustapha, F. A. Yatim, and R. Ab Aziz, “Predicting Player Position for Talent Identification in Association Football,” IOP Conf. Ser. Mater. Sci. Eng., vol. 226, p. 012087, Aug. 2017, doi: 10.1088/1757-899X/226/1/012087.
[2] R. M. Malina, “Early Sport Specialization: Roots, Effectiveness, Risks,” Curr. Sports Med. Rep., vol. 9, no. 6, pp. 364–371, Nov. 2010, doi: 10.1249/JSR.0b013e3181fe3166.
[3] Y. Galily, “Artificial intelligence and sports journalism: Is it a sweeping change?,” Technol. Soc., vol. 54, pp. 47–51, Aug. 2018, doi: 10.1016/j.techsoc.2018.03.001.
[4] R. R. Nadikattu, “Implementation of New Ways of Artificial Intelligence in Sports,” J. Xidian Univ., vol. 14, no. 5, pp. 5983–5997, 2020, doi: 10.2139/ssrn.3620017.
[5] U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “The KDD process for extracting useful knowledge from volumes of data,” Commun. ACM, vol. 39, no. 11, pp. 27–34, Nov. 1996, doi: 10.1145/240455.240464.
[6] A. Ochoa, A. Hernández, J. Sánchez, A. Muñoz-Zavala, and J. Ponce, “Determining the Ranking of a New Participant in Eurovision Using Cultural Algorithms and Data Mining,” 18th Int. Conf. Electron. Commun. Comput. (conielecomp 2008), pp. 47–52, Mar. 2008, doi: 10.1109/CONIELECOMP.2008.27.
[7] C. Downs, S. J. Snodgrass, I. Weerasekara, S. R. Valkenborghs, and R. Callister, “Injuries in Netball-A Systematic Review,” Sport. Med. - Open, vol. 7, no. 1, p. 3, Dec. 2021, doi: 10.1186/s40798-020-00290-7.
[8] S. Whitehead et al., “The Applied Sports Science and Medicine of Netball: A Systematic Scoping Review,” Sport. Med., vol. 51, no. 8, pp. 1715–1731, Aug. 2021, doi: 10.1007/s40279-021-01461-6.
[9] A. H. Mohamad, R. Ramli, and A. F. Ramli, “A Software Engineering Approach in Netball Performance Analysis: Training and Activities Features for Automatic Players Position Selection,” 2020 8th Int. Conf. Inf. Technol. Multimed., pp. 371–377, Aug. 2020, doi: 10.1109/ICIMU49871.2020.9243476.
[10] E. R. Brooks, A. C. Benson, A. S. Fox, and L. M. Bruce, “Physical movement demands of elite-level netball match-play as measured by an indoor positioning system,” J. Sports Sci., vol. 38, no. 13, pp. 1488–1495, Jul. 2020, doi: 10.1080/02640414.2020.1745504.
[11] S. Bae, S. H. Ha, and S. C. Park, “Identifying gifted students and their learning paths using data mining techniques,” Data Min. E-Learning, pp. 191–206, Jun. 2006, doi: 10.2495/1-84564-152-3/11.
[12] B. D. Jones et al., “The Identification of ‘Game Changers’ in England Cricket’s Developmental Pathway for Elite Spin Bowling: A Machine Learning Approach,” J. Expert., vol. 2, no. 2, pp. 92–120, 2019. Available: Google Scholar.
[13] H. Jantan, A. R. Hamdan, and Z. A. Othman, “Human Talent Prediction in HRM using C4.5 Classification Algorithm,” Int. J. Comput. Sci. Eng., vol. 2, no. 8, pp. 2526–2534, 2010. Available: Google Scholar.
[14] M. Nasr, E. Shaaban, and A. Samir, “A proposed Model for Predicting Employees’ Performance Using Data Mining Techniques: Egyptian Case Study,” Int. J. Comput. Sci. Inf. Secur., vol. 17, no. 1, pp. 31–40, 2019. Available: Google Scholar.
[15] C. Combes, N. Meskens, C. Rivat, and J.-P. Vandamme, “Using a KDD process to forecast the duration of surgery,” Int. J. Prod. Econ., vol. 112, no. 1, pp. 279–293, Mar. 2008, doi: 10.1016/j.ijpe.2006.12.068.
[16] L. Subramainan, M. Z. M. Yusoff, and M. A. Mahmoud, “A classification of emotions study in software agent and robotics applications research,” 2015 Int. Symp. Agents, Multi-Agent Syst. Robot., pp. 41–46, Aug. 2015, doi: 10.1109/ISAMSR.2015.7379128.
[17] U. Shafique and H. Qaiser, “A Comparative Study of Data Mining Process Models (KDD, CRISP-DM and SEMMA),” Int. J. Innov. Sci. Res., vol. 12, no. 1, pp. 217–222, 2014. Available: Google Scholar.
[18] Y. Ben-Haim and E. Tom-Tov, “A Streaming Parallel Decision Tree Algorithm,” J. Mach. Learn. Res., vol. 11, no. 28, pp. 849–872, 2010, [Online]. Available: http://jmlr.org/papers/v11/ben-haim10a.html.
[19] A. Azevedo and M. F. Santos, “KDD, semma and CRISP-DM: A parallel overview,” IADIS Eur. Conf. Data Min., 2008. Available: Google Scholar.
[20] S. Bagga and D. G. N. Singh, “Conceptual Three Phase Iterative Model of KDD,” Int. J. Comput. Technol., vol. 2, no. 1, pp. 6–8, Feb. 2012, doi: 10.24297/ijct.v2i1.2605.
[21] S. J. Delany, P. Cunningham, D. Doyle, and A. Zamolotskikh, “Generating Estimates of Classification Confidence for a Case-Based Spam Filter,” Muñoz-Ávila, H., Ricci, F. Case-Based Reason. Res. Dev. ICCBR 2005. Lect. Notes Comput. Sci., vol. 3620, pp. 177–190, 2005, doi: 10.1007/11536406_16.
[22] J. A. Lara, D. Lizcano, M. A. Martínez, and J. Pazos, “Data preparation for KDD through automatic reasoning based on description logic,” Inf. Syst., vol. 44, pp. 54–72, Aug. 2014, doi: 10.1016/j.is.2014.03.002.
[23] S. Lotfi and M. Rebbouj, “Machine Learning for sport results prediction using algorithms,” Int. J. Inf. Technol. Appl. Sci., vol. 3, no. 3, pp. 148–155, Aug. 2021, doi: 10.52502/ijitas.v3i3.114.
[24] M. Mandorino, A. J. Figueiredo, G. Cima, and A. Tessitore, “A Data Mining Approach to Predict Non-Contact Injuries in Young Soccer Players,” Int. J. Comput. Sci. Sport, vol. 20, no. 2, pp. 147–163, Dec. 2021, doi: 10.2478/ijcss-2021-0009.
[25] L. Rokach and O. Maimon, “Decision Trees,” Maimon, O., Rokach, L. Data Min. Knowl. Discov. Handbook. Springer, Boston, MA, pp. 165–192, 2005, doi: 10.1007/0-387-25465-X_9.
[26] K. P. Sudheer, A. K. Gosain, D. Mohana Rangan, and S. M. Saheb, “Modelling evaporation using an artificial neural network algorithm,” Hydrol. Process., vol. 16, no. 16, pp. 3189–3202, Nov. 2002, doi: 10.1002/hyp.1096.
[27] S. A. Mostafa et al., “Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson’s disease,” Cogn. Syst. Res., vol. 54, pp. 90–99, May 2019, doi: 10.1016/j.cogsys.2018.12.004.
[28] R. R. Hocking, “A Biometrics Invited Paper. The Analysis and Selection of Variables in Linear Regression,” Biometrics, vol. 32, no. 1, p. 1, Mar. 1976, doi: 10.2307/2529336.
[29] T. Horvat and J. Job, “The use of machine learning in sport outcome prediction: A review,” WIREs Data Min. Knowl. Discov., vol. 10, no. 5, Sep. 2020, doi: 10.1002/widm.1380.
[30] P. Chainok et al., “Modeling and predicting the backstroke to breaststroke turns performance in age-group swimmers,” sport. Biomech., pp. 1–22, Dec. 2021, doi: 10.1080/14763141.2021.2005127.
[31] J. Stübinger, B. Mangold, and J. Knoll, “Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics,” Appl. Sci., vol. 10, no. 1, p. 46, Dec. 2019, doi: 10.3390/app10010046.
[32] W. Chen, D. Sharifrazi, G. Liang, S. S. Band, K. W. Chau, and A. Mosavi, “Accurate discharge coefficient prediction of streamlined weirs by coupling linear regression and deep convolutional gated recurrent unit,” Eng. Appl. Comput. Fluid Mech., vol. 16, no. 1, pp. 965–976, Dec. 2022, doi: 10.1080/19942060.2022.2053786.
[33] N. . Saravana Kumar, K. Hariprasath, N. Kaviyavarshini, and A. Kavinya, “A study on forecasting bigmart sales using optimized machine learning techniques,” Sci. Inf. Technol. Lett., vol. 1, no. 2, pp. 52–59, Nov. 2020, doi: 10.31763/sitech.v1i2.167.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571 (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
andri.pranolo.id@ieee.org (publication issues)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0