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

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 15 No. 4 (2021) · pp. 403–414

DOI
10.22146/ijccs.69366
ISSN
2460-7258, 1978-1520
Published
2021-10-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

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

Collaboration network of the authors

5 authors · 7 co-authorship links · 4 across different organisations

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  • Universitas Pradita
  • Multimedia Nusantara University
  • Nasional University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Djarot HindartoNasional University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Comparison Non-Parametric Machine Learning Algorithms for Prediction of Employee Talent</dc:title>
	<dc:creator>Wirayasa, I Ketut Adi</dc:creator>
	<dc:creator>Djajadi, Arko</dc:creator>
	<dc:creator>Santoso, H andri</dc:creator>
	<dc:creator>Indrajit, Eko</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">non-parametric; machine learning; ordinal data; employee talent</dc:subject>
	<dc:description xml:lang="en-US">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.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2021-10-31</dc:date>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/69366</dc:identifier>
	<dc:identifier>10.22146/ijccs.69366</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 15, No 4 (2021): October; 403-414</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/69366/32410</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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