Comparison Non-Parametric Machine Learning Algorithms for Prediction of Employee Talent
I Ketut Adi Wirayasa(1*), Arko Djajadi(2), H andri Santoso(3), Eko Indrajit(4)
(1) Department of Computer Science, Universitas Pradita, Banten
(2) Department of Computer Science, Universitas Pradita, Banten
(3) Department of Computer Science, Universitas Pradita, Banten
(4) Department of Computer Science, Universitas Pradita, Banten
(*) Corresponding Author
Abstract
Keywords
Full Text:
PDFReferences
[1] F. Fallucchi, M. Coladangelo, R. Giuliano, and E. William De Luca, “Predicting Employee Attrition Using Machine Learning Techniques,” Computers, vol. 9, no. 4, p. 86, Nov. 2020, doi: 10.3390/computers9040086.
[2] A. Dandekar, D. Basu, and S. Bressan, “Differentially Private Non-parametric Machine Learning as a Service,” Differ. Priv. Non-parametric Mach. Learn. as a Serv., vol. 11706 LNCS, pp. 189–204, 2019, doi: 10.1007/978-3-030-27615-7_14.
[3] S. Wiyono, “Perbandingan Algoritma Machine Learning SVM dan Decision Tree untuk Prediksi Keaktifan Mahasiswa,” Sinkron, vol. 3, no. 1, pp. 105–108, 2018.
[4] N. Sagala and H. Tampubolon, “Komparasi Kinerja Algoritma Data Mining pada Dataset Konsumsi Alkohol Siswa,” Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 4, no. 2, p. 98, Dec. 2018, doi: 10.23917/khif.v4i2.7061.
[5] M. G M, G. M. Mujtaba, and M. Rahmath, “Maintain and Evaluate students’ performance Using Machine Learning,” Int. J. Comput. Trends Technol., vol. 68, no. 6, pp. 57–63, 2020, doi: 10.14445/22312803/ijctt-v68i6p110.
[6] S. Jauhiainen, S. Aÿrämö, H. Forsman, and J. P. Kauppi, “Talent identification in soccer using a one-class support vector machine,” Int. J. Comput. Sci. Sport, vol. 18, no. 3, pp. 125–136, 2019, doi: 10.2478/ijcss-2019-0021.
[7] A. S. & S. Bhardwaj, “Resume Ranking And Performance Appraisal Using Predictive Mining And Machine Learning : Talent Management System,” Int. J. Manag. Appl. Sci. ISSN 2394-7926, vol. 4, no. 6, 2018.
[8] M. Moustafa Reda, M. Nassef, and A. Salah, “Factors Affecting Classification Algorithms Recommendation: A Survey,” in 8th International Conference on Soft Computing, Artificial Intelligence and Applications, Jun. 2019, pp. 83–99, doi: 10.5121/csit.2019.90707.
[9] A. Çetinkaya and Ö. K. Baykan, “Prediction of middle school students’ programming talent using artificial neural networks,” Eng. Sci. Technol. an Int. J., vol. 23, no. 6, pp. 1301–1307, 2020, doi: 10.1016/j.jestch.2020.07.005.
[10] C. N. Refugio, “An Empirical Study on Wilcoxon Signed Rank Test An Empirical Study on Wilcoxon Signed Rank Test Ana Marie Durango,” J., no. December, p. 12, 2018, doi: 10.13140/RG.2.2.13996.51840.
[11] E. A. Bedoya-Marrugo, L. E. Vargas-Ortiz, C. A. Severiche-Sierra, and D. D. Sierra-Calderon, “Kruskal-Wallis Test for the Identification of Factors that Influence the Perception of Accidents in Workers in the Construction Sector,” Int. J. Appl. Eng. Res., vol. 12, no. 17, pp. 6730–6734, 2017, [Online]. Available: http://www.ripublication.com.
[12] R. P. Sarmento and V. Costa, “Confirmatory Factor Analysis -- A Case study,” ResearchGate, p. 39, 2019, [Online]. Available: http://arxiv.org/abs/1905.05598.
[13] X. Lee, B. Yang, and W. Li, “The influence factors of job satisfaction and its relationship with turnover intention: Taking early-career employees as an example,” An. Psicol., vol. 33, no. 3, p. 697, Jul. 2017, doi: 10.6018/analesps.33.3.238551.
[14] D. A. Kristiyanti and M. Wahyudi, “Feature selection based on Genetic algorithm, particle swarm optimization and principal component analysis for opinion mining cosmetic product review,” in 2017 5th International Conference on Cyber and IT Service Management (CITSM), Aug. 2017, pp. 1–6, doi: 10.1109/CITSM.2017.8089278.
[15] S. Fernández-Salinero, Á. G. Collantes, F. R. Cifuentes, and G. Topa, “Is job involvement enough for achieving job satisfaction? The role of skills use and group identification,” Int. J. Environ. Res. Public Health, vol. 17, no. 12, pp. 1–11, 2020, doi: 10.3390/ijerph17124193.
[16] A. Yuspahruddin, A. Eliyana, A. D. Buchdadi, Hamidah, T. Sariwulan, and K. Muhaziroh, “The effect of employee involvement on job satisfaction,” Syst. Rev. Pharm., vol. 11, no. 7, pp. 490–498, 2020, doi: 10.31838/srp.2020.7.72.
[17] R. Setiawan, A. Eliyana, T. Suryani, and J. Christopher, “Creating job satisfaction in a strict organization,” Opcion, vol. 36, no. SpecialEdition27, pp. 376–385, 2020.
[18] D. R. Wickramaaratchi and G. D. N. Perera, “The Impact of Talent Management on Employee Performance: The Mediating Role of Job Satisfaction of Generation Y Management Trainees in the Selected Public Banks in Sri Lanka,” Sri Lankan J. Hum. Resour. Manag., vol. 10, no. 1, p. 21, Jun. 2020, doi: 10.4038/sljhrm.v10i1.5648.
[19] M. D. V. S. Mendis, “The Impact of Work Life Balance on Employee Performance with Reference to Telecommunication Industry in Sri Lanka: A Mediation Model,” ResearchGate, vol. 12, no. January 2017, p. 30, 2018, doi: 10.4038/kjhrm.v12i1A2.
[20] A. Kusmaningtyas and R. Nugroho, “Effect of Job Involvement on Employee Performance through Work Engagement at Bank Jatim,” Univers. J. Manag., vol. 9, no. 2, pp. 29–37, 2021, doi: 10.13189/ujm.2021.090201.
[21] M. N. El, “Talent Management , Employee Recognition And Performance In The Research Institutions,” Sciendo, vol. 14, no. 14, pp. 127–140, 2019, doi: 10.2478/sbe-2019-0010.
[22] F. Firmansyah, Rozanah Katrina Herda, Angga Damayanto, and Fajar Sidik, “Confirmatory Factor Analysis To Know the Influencing Factors of Elementary School Students’ Self-Concept in Jetis Sub District, Bantul Regency,” JISAE J. Indones. Student Assess. Eval., vol. 6, no. 2, pp. 196–202, 2020, doi: 10.21009/jisae.062.010.
[23] L. L. Chan and N. Idris, “Validity and Reliability of The Instrument Using Exploratory Factor Analysis and Cronbach ’ s a lpha,” IJARBS, vol. 7, no. 10, pp. 400–410, 2017, doi: 10.6007/IJARBSS/v7-i10/3387.
[24] A. N. Noercholis, “Comparative Analysis of 5 Algorithm Based Particle Swarm Optimization (Pso) for Prediction of Graduate Time Graduation,” Matics, vol. 12, no. 1, p. 1, 2020, doi: 10.18860/mat.v12i1.8216.
[25] T. T. Maskoen and D. Purnama, “Area Under the Curve dan Akurasi Cystatin C untuk Diagnosis Acute Kidney Injury pada Pasien Politrauma,” Maj. Kedokt. Bandung, vol. 50, no. 4, pp. 259–264, 2018, doi: 10.15395/mkb.v50n4.1342.
DOI: https://doi.org/10.22146/ijccs.69366
Article Metrics
Abstract views : 2599 | views : 2535Refbacks
- There are currently no refbacks.
Copyright (c) 2021 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1