Comparison Non-Parametric Machine Learning Algorithms for Prediction of Employee Talent

https://doi.org/10.22146/ijccs.69366

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


Classification of ordinal data is part of categorical data. Ordinal data consists of features with values based on order or ranking. The use of machine learning methods in Human Resources Management is intended to support decision-making based on objective data analysis, and not on subjective aspects. The purpose of this study is to analyze the relationship between features, and whether the features used as objective factors can classify, and predict certain talented employees or not. This study uses a public dataset provided by IBM analytics. Analysis of the dataset using statistical tests, and confirmatory factor analysis validity tests, intended to determine the relationship or correlation between features in formulating hypothesis testing before building a model by using a comparison of four algorithms, namely Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Artificial Neural Networks. The test results are expressed in the Confusion Matrix, and report classification of each model. The best evaluation is produced by the SVM algorithm with the same Accuracy, Precision, and Recall values, which are 94.00%, Sensitivity 93.28%, False Positive rate 4.62%, False Negative rate 6.72%,  and AUC-ROC curve value 0.97 with an excellent category in performing classification of the employee talent prediction model.

Keywords


non-parametric; machine learning; ordinal data; employee talent

Full Text:

PDF


References

[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 : 2664 | views : 2551

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2021 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


Copyright of :
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 1978-1520 (print); ISSN 2460-7258 (online)
is a scientific journal the results of Computing
and Cybernetics Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijccs.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijccs


View My Stats1
View My Stats2