(Universitas Negeri Surabaya, Indonesia)(2) Danang Ariyanto
(Universitas Negeri Surabaya, Indonesia)(3) Junaidi Budi Prihanto
(Universitas Negeri Surabaya, Indonesia)(4) Dimas Avian Maulana
(Universitas Negeri Surabaya, Indonesia)(5) Riska Wahyu Romadhonia
(Universitas Negeri Surabaya, Indonesia)(6) Asri Maharani
(Manchester Metropolitan University, United Kingdom)(7) Affi Oktaviarina
(Universitas Negeri Surabaya, Surabaya, Indonesia)(8) Ibnu Febry Kurniawan
(Universitas Negeri Surabaya, Indonesia)(9) Khusnia Nurul Khikmah
(IPB University, Indonesia)(10) Muhammad Mahdy Al Akbar
(Universitas Negeri Surabaya, Indonesia)*corresponding author
AbstractThis research aims to develop an analytical approach to classification statistics. The proposed approach combines machine learning with optimization. Considering the urgency of research related to exploring the best methods to apply to sports data. This study proposes a novel framework that combines the k-means clustering results with the bat algorithm to optimize performance prediction for athletes in Indonesia. The proposed method aims to explore the data by comparing the classification performance of random forests, extremely randomized trees, and support vector machines. We conducted a case study using primary data from 200 respondents at Surabaya State University and the East Java National Sports Committee. The accuracy results in this study indicate that, based on the performance evaluation metric, the best approach is random forest clustering using k-means with bat algorithm optimization, achieving 81.25% accuracy, compared with other machine learning approaches. This research contributes to the field of classification statistics by introducing a novel hybrid framework that integrates machine learning, clustering, and optimization techniques to improve predictive accuracy, particularly in sports analytics. Beyond sports science, the proposed approach can be adapted to other domains that require robust performance prediction and decision support, such as health analytics, educational assessment, and human resource selection.
KeywordsBat algorithm; Classification; Clustering; Machine learning; Optimization
|
DOIhttps://doi.org/10.26555/ijain.v11i4.1816 |
Article metricsAbstract views : 513 | PDF views : 30 |
Cite |
Full Text Download
|
References
[1] J. Cervantes, F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez, “A comprehensive survey on support vector machine classification: Applications, challenges and trends,” Neurocomputing, vol. 408, no. September, pp. 189–215, Sep. 2020, doi: 10.1016/j.neucom.2019.10.118.
[2] G. Tutz, “Ordinal regression: A review and a taxonomy of models,” WIREs Comput. Stat., vol. 14, no. 2, pp. 1–28, Mar. 2022, doi: 10.1002/wics.1545.
[3] A. Rajabi, M. Eskandari, M. J. Ghadi, L. Li, J. Zhang, and P. Siano, “A comparative study of clustering techniques for electrical load pattern segmentation,” Renew. Sustain. Energy Rev., vol. 120, no. March, p. 109628, Mar. 2020, doi: 10.1016/j.rser.2019.109628.
[4] A. M. Ikotun, M. S. Almutari, and A. E. Ezugwu, “K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions,” Appl. Sci., vol. 11, no. 23, p. 11246, Nov. 2021, doi: 10.3390/app112311246.
[5] T. Agarwal and V. Kumar, “A Systematic Review on Bat Algorithm: Theoretical Foundation, Variants, and Applications,” Arch. Comput. Methods Eng., vol. 29, no. 5, pp. 2707–2736, Aug. 2022, doi: 10.1007/s11831-021-09673-9.
[6] I. Karakonstantis and A. Vlachos, “Bat algorithm applied to continuous constrained optimization problems,” J. Inf. Optim. Sci., vol. 42, no. 1, pp. 57–75, Jan. 2021, doi: 10.1080/02522667.2019.1694740.
[7] A. Alharbi, W. Alosaimi, H. Alyami, H. T. Rauf, and R. Damaševičius, “Botnet Attack Detection Using Local Global Best Bat Algorithm for Industrial Internet of Things,” Electronics, vol. 10, no. 11, p. 1341, Jun. 2021, doi: 10.3390/electronics10111341.
[8] B. Charbuty and A. Abdulazeez, “Classification Based on Decision Tree Algorithm for Machine Learning,” J. Appl. Sci. Technol. Trends, vol. 2, no. 01, pp. 20–28, Mar. 2021, doi: 10.38094/jastt20165.
[9] R. Genuer and J.-M. Poggi, Random Forests with R, 1st ed. in Use R! Cham: Springer International Publishing, 2020. doi: 10.1007/978-3-030-56485-8.
[10] U. Saeed, S. U. Jan, Y.-D. Lee, and I. Koo, “Fault diagnosis based on extremely randomized trees in wireless sensor networks,” Reliab. Eng. Syst. Saf., vol. 205, no. January, p. 107284, Jan. 2021, doi: 10.1016/j.ress.2020.107284.
[11] D. A. Pisner and D. M. Schnyer, “Support vector machine,” in Machine Learning, Elsevier, 2020, pp. 101–121. doi: 10.1016/B978-0-12-815739-8.00006-7.
[12] D. Patel, D. Shah, and M. Shah, “The Intertwine of Brain and Body: A Quantitative Analysis on How Big Data Influences the System of Sports,” Ann. Data Sci., vol. 7, no. 1, pp. 1–16, Mar. 2020, doi: 10.1007/s40745-019-00239-y.
[13] K. Till and J. Baker, “Challenges and [Possible] Solutions to Optimizing Talent Identification and Development in Sport,” Front. Psychol., vol. 11, no. April, p. 525518, Apr. 2020, doi: 10.3389/fpsyg.2020.00664.
[14] M. Lopes Dos Santos et al., “Stress in Academic and Athletic Performance in Collegiate Athletes: A Narrative Review of Sources and Monitoring Strategies,” Front. Sport. Act. Living, vol. 2, p. 502979, May 2020, doi: 10.3389/fspor.2020.00042.
[15] V. Schweiger, D. Niederseer, C. Schmied, C. Attenhofer-Jost, and S. Caselli, “Athletes and Hypertension,” Curr. Cardiol. Rep., vol. 23, no. 12, p. 176, Dec. 2021, doi: 10.1007/s11886-021-01608-x.
[16] C. McHugh, K. Hind, J. Cunningham, D. Davey, and F. Wilson, “A career in sport does not eliminate risk of cardiovascular disease: A systematic review and meta-analysis of the cardiovascular health of field-based athletes,” J. Sci. Med. Sport, vol. 23, no. 9, pp. 792–799, Sep. 2020, doi: 10.1016/j.jsams.2020.02.009.
[17] C. M. Baker-Smith and T. Tsuda, “Exercise Testing in Hypertension and Hypertension in Athletes,” in Pediatric Hypertension, Cham: Springer International Publishing, 2023, pp. 827–842. doi: 10.1007/978-3-031-06231-5_12.
[18] T. Trojian et al., “American Medical Society for Sports Medicine Position Statement on the Care of the Athlete and Athletic Person With Diabetes,” Clin. J. Sport Med., vol. 32, no. 1, pp. 8–20, Jan. 2022, doi: 10.1097/JSM.0000000000000906.
[19] C. J. Holmes and M. K. Hastings, “The Application of Exercise Training for Diabetic Peripheral Neuropathy,” J. Clin. Med., vol. 10, no. 21, p. 5042, Oct. 2021, doi: 10.3390/jcm10215042.
[20] W. Xu, M. Tang, and Y. Li, “A new method for assessment of regional drought risk: information diffusion and interval mapping adjustment based on k-means cluster points,” J. Water Clim. Chang., vol. 13, no. 12, pp. 4302–4316, Dec. 2022, doi: 10.2166/wcc.2022.345.
[21] K. Zhou and S. Yang, “Effect of cluster size distribution on clustering: a comparative study of k-means and fuzzy c-means clustering,” Pattern Anal. Appl., vol. 23, no. 1, pp. 455–466, Feb. 2020, doi: 10.1007/s10044-019-00783-6.
[22] B. Alsalibi, L. Abualigah, and A. T. Khader, “A novel bat algorithm with dynamic membrane structure for optimization problems,” Appl. Intell., vol. 51, no. 4, pp. 1992–2017, Apr. 2021, doi: 10.1007/s10489-020-01898-8.
[23] L. F. Zhu, J. S. Wang, H. Y. Wang, S. S. Guo, M. W. Guo, and W. Xie, “Data Clustering Method Based on Improved Bat Algorithm With Six Convergence Factors and Local Search Operators,” IEEE Access, vol. 8, no. April, pp. 80536–80560, 2020, doi: 10.1109/ACCESS.2020.2991091.
[24] L. Breiman, “Random Forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, Oct. 2001, doi: 10.1023/A:1010933404324.
[25] A. Cutler, D. R. Cutler, and J. R. Stevens, “Random Forests,” in Ensemble Machine Learning, New York, NY: Springer New York, 2012, pp. 157–175. doi: 10.1007/978-1-4419-9326-7_5.
[26] S. Park, S.-Y. Hamm, and J. Kim, “Performance Evaluation of the GIS-Based Data-Mining Techniques Decision Tree, Random Forest, and Rotation Forest for Landslide Susceptibility Modeling,” Sustainability, vol. 11, no. 20, p. 5659, Oct. 2019, doi: 10.3390/su11205659.
[27] A. Arfiani and Z. Rustam, “Ovarian cancer data classification using bagging and random forest,” in AIP Conference Proceedings, American Institute of Physics Inc., Nov. 2019, p. 020046. doi: 10.1063/1.5132473.
[28] P. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Mach. Learn., vol. 63, no. 1, pp. 3–42, Apr. 2006, doi: 10.1007/s10994-006-6226-1.
[29] T. V Rampisela and Z. Rustam, “Classification of Schizophrenia Data Using Support Vector Machine (SVM),” J. Phys. Conf. Ser., vol. 1108, no. 1, p. 012044, Nov. 2018, doi: 10.1088/1742-6596/1108/1/012044.
[30] C. AVCI, M. BUDAK, N. YAĞMUR, and F. BALÇIK, “Comparison between random forest and support vector machine algorithms for LULC classification,” Int. J. Eng. Geosci., vol. 8, no. 1, pp. 1–10, Feb. 2023, doi: 10.26833/ijeg.987605.
[31] A. Sofro, D. Ariyanto, J. Budi Prihanto, D. A. Maulana, R. W. Romadhonia, and A. Maharani, “Integration of Bivariate Logistic Regression Models and Decision Trees to Explore the Relationship between Socio-Demographic and Anthropometric Factors with the Incidence of Hypertension and Diabetes in Prospective Athletes,” Sport Mont, vol. 22, no. 1, pp. 71–78, Feb. 2024, doi: 10.26773/smj.240210.
[32] P. Probst, M. N. Wright, and A. Boulesteix, “Hyperparameters and tuning strategies for random forest,” WIREs Data Min. Knowl. Discov., vol. 9, no. 3, p. e1301, May 2019, doi: 10.1002/widm.1301.
[33] G. M. Foody, “Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification,” Remote Sens. Environ., vol. 239, no. March, p. 111630, Mar. 2020, doi: 10.1016/j.rse.2019.111630.
[34] S. Md Mujeeb, R. Praveen Sam, and K. Madhavi, “Adaptive Exponential Bat algorithm and deep learning for big data classification,” Sādhanā, vol. 46, no. 1, p. 15, Dec. 2021, doi: 10.1007/s12046-020-01521-z.
[35] A. K. Heather et al., “Biological and Socio-Cultural Factors Have the Potential to Influence the Health and Performance of Elite Female Athletes: A Cross Sectional Survey of 219 Elite Female Athletes in Aotearoa New Zealand,” Front. Sport. Act. Living, vol. 3, p. 601420, Feb. 2021, doi: 10.3389/fspor.2021.601420.
[36] N. Leite, J. Arede, X. Shang, J. Calleja-González, and A. Lorenzo, “The Influence of Contextual Aspects in Talent Development: Interaction Between Relative Age and Birthplace Effects in NBA-Drafted Players,” Front. Sport. Act. Living, vol. 3, no. March, p. 642707, Mar. 2021, doi: 10.3389/fspor.2021.642707.
[37] A. Lisinskiene and M. Lochbaum, “The Coach–Athlete–Parent Relationship: The Importance of the Sex, Sport Type, and Family Composition,” Int. J. Environ. Res. Public Health, vol. 19, no. 8, p. 4821, Apr. 2022, doi: 10.3390/ijerph19084821.
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