International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 10, Issue 1 (January 2023), Pages: 48-54

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 Original Research Paper

 Machine learning prediction of law enforcement officers’ misconduct with general strain theory

 Author(s): Rahayu Abdul Rahman 1, Suraya Masrom 2, *, Jihadah Ahmad 3, Lilis Maryasih 4, Nor Balkish Zakaria 5, Mohd Auzan Md Nor 6

 Affiliation(s):

 1Faculty of Accountancy, Universiti Teknologi MARA, Perak Branch, Tapah Campus, Shah Alam, Malaysia
 2Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perak Branch, Tapah Campus, Shah Alam, Malaysia
 3Faculty of Computing and Multimedia, Kolej Universiti Poly-Tech MARA, Kuala Lumpur, Malaysia
 4Faculty of Economics and Business, Universitas Syiah Kuala, Acheh, Indonesia
 5Accounting Research Institute, Universiti Teknologi MARA, Selangor, Shah Alam, Malaysia
 6Commercial Crime Investigation Division, Royal Malaysia Police, Perak, Malaysia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0001-7957-8697

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2023.01.007

 Abstract:

The main objective of this study is to develop a machine learning prediction model on employee misconduct that signals the failure of the integrity of law enforcement officers in performing their duties and responsibilities. Using a questionnaire survey of two hundred eighty-six participants, from senior officers to rank and file police officers, this study presents the fundamental knowledge on the design and implementation of a machine learning model based on four selected algorithms; generalized linear model, random forest, decision tree and support vector machine. In addition to demographic attributes, the performance of each machine learning algorithm on the employee's misconduct has been observed based on the attributes of general strain theory namely financial stress, work stress, leadership exposure, and peer pressure. The findings indicated that peer pressure was the most influencer in the prediction models of all machine learning algorithms. However, random forest is the most outperformed algorithm in terms of prediction accuracy.

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Employee misconduct, Machine learning, Prediction, Police forces

 Article History: Received 16 June 2022, Received in revised form 21 September 2022, Accepted 22 September 2022

 Acknowledgment 

We acknowledge the financial support granted by the Accounting Research Institute, Universiti Teknologi MARA, and Kolej Universiti Poly-Tech MARA for this project.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Rahman RA, Masrom S, Ahmad J, Maryasih L, Zakaria NB, and Nor MAM (2023). Machine learning prediction of law enforcement officers’ misconduct with general strain theory. International Journal of Advanced and Applied Sciences, 10(1): 48-54

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7

 Tables

 Table 1 Table 2 Table 3 Table 4

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