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Optimization of Gradient Boosting Method for Predicting Narcissistic Personality Disorder (NPD) in Employees

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 19 No. 2 (2025) · pp. 117–128

DOI
10.22146/ijccs.103551
ISSN
2460-7258, 1978-1520
Published
2025-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Narcissistic Personality Disorder (NPD) is a serious challenge in modern workplace environments; however, early detection and appropriate intervention remain unmet needs. This research aims to address the issue by proposing an intelligent system model based on machine learning, utilizing the Gradient Boosting method to predict NPD. The Gradient Boosting method was chosen for its ability to handle complex data and gradually improve prediction performance. This model is integrated with employee data, including a range of psychological, behavioral, and demographic variables relevant to NPD. The primary contribution of this research is the development of a predictive model that can assist organizations in identifying and providing early intervention to employees at risk of developing NPD. In doing so, it is expected to reduce the negative impact of NPD on the workplace, such as interpersonal conflicts and decreased productivity. The study shows significant results in the model's classification performance after applying Recursive Feature Elimination (RFE) to optimize the Gradient Boosting method. The accuracy rate reached 82%, an improvement from the previous 79% achieved using the Gradient Boosting Classifier. This indicates that the RFE-Gradient Boosting model has greater potential in classifying employees who genuinely have narcissistic personality disorder versus those who do not.

Keywords

Collaboration network of the authors

6 authors · 8 co-authorship links

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  • Universitas Budi Luhur
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Restu MaulunidaUniversitas Budi Luhur1
Ludi NugrohoUniversitas Budi Luhur1

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">Optimization of Gradient Boosting Method for Predicting Narcissistic Personality Disorder (NPD) in Employees</dc:title>
	<dc:creator>Solichin, Achmad</dc:creator>
	<dc:creator>Pramudita, Bagas</dc:creator>
	<dc:creator>Painem, Painem</dc:creator>
	<dc:creator>Pradiptha, Anindya Putri</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">narcissistic personality disorder; mental health; employee; machine learning; Recursive Feature Elimination; Gradient Boosting</dc:subject>
	<dc:description xml:lang="en-US">Narcissistic Personality Disorder (NPD) is a serious challenge in modern workplace environments; however, early detection and appropriate intervention remain unmet needs. This research aims to address the issue by proposing an intelligent system model based on machine learning, utilizing the Gradient Boosting method to predict NPD. The Gradient Boosting method was chosen for its ability to handle complex data and gradually improve prediction performance. This model is integrated with employee data, including a range of psychological, behavioral, and demographic variables relevant to NPD. The primary contribution of this research is the development of a predictive model that can assist organizations in identifying and providing early intervention to employees at risk of developing NPD. In doing so, it is expected to reduce the negative impact of NPD on the workplace, such as interpersonal conflicts and decreased productivity. The study shows significant results in the model's classification performance after applying Recursive Feature Elimination (RFE) to optimize the Gradient Boosting method. The accuracy rate reached 82%, an improvement from the previous 79% achieved using the Gradient Boosting Classifier. This indicates that the RFE-Gradient Boosting model has greater potential in classifying employees who genuinely have narcissistic personality disorder versus those who do not.</dc:description>
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	<dc:date>2025-04-30</dc:date>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/103551</dc:identifier>
	<dc:identifier>10.22146/ijccs.103551</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 19, No 2 (2025): April; 117-128</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
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	<dc:rights xml:lang="en-US">Copyright (c) 2025 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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