Indonesian Journal of Electrical Engineering and Computer Science Vol. 31, No. 2, August 2023, pp. 909~916 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v31.i2.pp909-916  909 Journal homepage: http://ijeecs.iaescore.com Machine learning in predicting whistle-blowing intention of academic dishonesty with theory of planned behaviour Suraya Masrom 1 , Nor Hafiza Abdul Samad 2 , Rahayu Abdul Rahman 3 , Farah Husna Mohd Fatzel 3 , Siti Marlia Shamsudin 3 1 College of Computing, Informatics and Media, Universiti Teknologi MARA, Perak Branch, Malaysia 2 Faculty of Computing and Multimedia, Universiti Poly-Tech Malaysia, Kuala Lumpur, Malaysia 3 Faculty of Accountancy, Universiti Teknologi MARA, Perak Branch, Malaysia Article Info ABSTRACT Article history: Received Jan 18, 2023 Revised Apr 10, 2023 Accepted Apr 16, 2023 The COVID-19 pandemic and its aftermath have caused most higher educations to choose to implement remote learning as a new method of instruction and assessment. Nevertheless, remote learning has been criticized by having adverse impact on academic integrity. Whistle-blowing has been regarded as an effective mechanism in limiting such unethical behavior. Thus, the main objective of this study is to identify the influence attributes of whistle-blowing intention among university students. The effectiveness of the whistle-blowing attributes was observed in prediction models based on machine learning technique. This paper presents the fundamental knowledge on evaluations of tree-based machine learning algorithms namely decision tree, random forest, to be compared with logistics regression and gradient linear model. A rigorous evaluation reports are provided that includes the area under curve (AUC) as a supplementary metric to measure the model accuracy. Additionally, to provide a clearer insight on the whistle-blowing prediction models, the pattern of influences from the whistle-blowing attributes based on the adoption of theory of planned behavior (TPB) and demography are presented. The findings revealed that both TPB and demography attributes contain some degree of impressive knowledge for the machine learning to generate a good prediction result. Keywords: Academic dishonesty Area under curve Machine learning prediction Theory of planned behavior Whistle-blowing This is an open access article under the CC BY-SA license. Corresponding Author: Rahayu Abdul Rahman Faculty of Accountancy, Universiti Teknologi MARA Perak Branch, Tapah Campus, Malaysia Email: rahay916@uitm.edu.my 1. INTRODUCTION Remote learning has been implemented by higher education institutions globally in response to COVID-19 pandemic and its social confinement enforcement [1], [2]. Although remote learning provides some beneficial impact to the learning [3]-[5], there are some drawbacks that educators face [3], [6]. Prior studies in [7], [8] stressed that although remote learning is regarded as an effective strategy especially during COVID-19 pandemic to mitigate health risks for both educators and students, it has adverse impact on academic integrity. Using electronic examination as a student’s assessment tool gives more opportunities for students to engage in academic dishonesty [9] and good for fostering their self-regulated learning [5]. Achmada et al. [10] define academic misconduct or dishonesty as an intentional act of fraud, in which a student seeks to claim credit for the work or efforts of another without authorization, or uses unauthorized materials or fabricated information in any academic exercise. Academic dishonesty includes forgery of academic documents, intentionally impeding or damaging the academic work of others, or assisting other students in acts of dishonesty.