Comparisons of automated machine learning (AutoML) in predicting whistleblowing of academic dishonesty with demographic and theory of planned behavior
Machine learning has been very promising in solving real problems, but the implementation involved difficulties mainly for the inexpert data scientists. Therefore, this paper presents an automated machine learning (AutoML) to simplify and accelerate the modeling tasks. Focused on Python and RapidMin...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | MethodsX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016123003606 |
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| author | Rahayu Abdul Rahman Suraya Masrom Masurah Mohamad Eka Nurmala Sari Fitriani Saragih Abdullah Sani Abd Rahman |
| author_facet | Rahayu Abdul Rahman Suraya Masrom Masurah Mohamad Eka Nurmala Sari Fitriani Saragih Abdullah Sani Abd Rahman |
| author_sort | Rahayu Abdul Rahman |
| collection | DOAJ |
| description | Machine learning has been very promising in solving real problems, but the implementation involved difficulties mainly for the inexpert data scientists. Therefore, this paper presents an automated machine learning (AutoML) to simplify and accelerate the modeling tasks. Focused on Python and RapidMiner rapid modeling tools, Tree-based Pipeline Optimization Tool (TPOT) and AutoModel were used. This paper presents a comprehensive comparison between these tools with regard to the prediction accuracy and Area Under Curve (AUC) in classifying real cases of whistleblowing academic dishonesty among undergraduate students of two universities in Indonesia. Additionally, the correlations weight from demographic and Theory of Planned Behavior (TOB) attributes in the different machine learning models are also discussed. All the machine learning algorithms from TPOT and AutoModel are considerable powerful to generate good accuracy level (between 70–93% of AUC) in classifying both cases of whistleblowing and non-whistleblowing on the hold-out samples from the testing process. Generally, based on the validation results of the prediction models, demographic attributes presented more importance than the TBP attributes. The findings of this study will be a great interest of many research scholars to conduct a more in-depth analysis on AutoML for many domains mainly in education and academic misconduct fields. • AutoML is the first of its kind to be empirically compared between TPOT and AutoModel in an application to predict academic dishonesty whistleblowing. • Besides accuracy performances of the AutoML, the proportion of the variance of each attribute from demographic and Theory of Planned Behavior (TPB) is also presented in the prediction models of academic dishonesty whistleblowing. • AutoML is a convenient and reproducible rapid modeling method of machine learning to be used in many kinds of prediction problem. |
| first_indexed | 2024-03-09T03:09:39Z |
| format | Article |
| id | doaj.art-20d2394bdc614c4cbd8a2a44acf7ffcc |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-03-09T03:09:39Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-20d2394bdc614c4cbd8a2a44acf7ffcc2023-12-04T05:22:28ZengElsevierMethodsX2215-01612023-12-0111102364Comparisons of automated machine learning (AutoML) in predicting whistleblowing of academic dishonesty with demographic and theory of planned behaviorRahayu Abdul Rahman0Suraya Masrom1Masurah Mohamad2Eka Nurmala Sari3Fitriani Saragih4Abdullah Sani Abd Rahman5Faculty of Accountancy, Univesiti Teknologi MARA, Perak Branch, Tapah Campus, MalaysiaCollege of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Perak Branch, Tapah Campus, Malaysia; Corresponding author.College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Perak Branch, Tapah Campus, MalaysiaDepartment of Accounting, Universitias Muhammadiyah Sumatera Utara, IndonesiaDepartment of Accounting, Universitias Muhammadiyah Sumatera Utara, IndonesiaFaculty of Science and Information Technology, Universiti Teknologi PETRONAS, Perak, MalaysiaMachine learning has been very promising in solving real problems, but the implementation involved difficulties mainly for the inexpert data scientists. Therefore, this paper presents an automated machine learning (AutoML) to simplify and accelerate the modeling tasks. Focused on Python and RapidMiner rapid modeling tools, Tree-based Pipeline Optimization Tool (TPOT) and AutoModel were used. This paper presents a comprehensive comparison between these tools with regard to the prediction accuracy and Area Under Curve (AUC) in classifying real cases of whistleblowing academic dishonesty among undergraduate students of two universities in Indonesia. Additionally, the correlations weight from demographic and Theory of Planned Behavior (TOB) attributes in the different machine learning models are also discussed. All the machine learning algorithms from TPOT and AutoModel are considerable powerful to generate good accuracy level (between 70–93% of AUC) in classifying both cases of whistleblowing and non-whistleblowing on the hold-out samples from the testing process. Generally, based on the validation results of the prediction models, demographic attributes presented more importance than the TBP attributes. The findings of this study will be a great interest of many research scholars to conduct a more in-depth analysis on AutoML for many domains mainly in education and academic misconduct fields. • AutoML is the first of its kind to be empirically compared between TPOT and AutoModel in an application to predict academic dishonesty whistleblowing. • Besides accuracy performances of the AutoML, the proportion of the variance of each attribute from demographic and Theory of Planned Behavior (TPB) is also presented in the prediction models of academic dishonesty whistleblowing. • AutoML is a convenient and reproducible rapid modeling method of machine learning to be used in many kinds of prediction problem.http://www.sciencedirect.com/science/article/pii/S2215016123003606Automated Machine Learning (AutoML) |
| spellingShingle | Rahayu Abdul Rahman Suraya Masrom Masurah Mohamad Eka Nurmala Sari Fitriani Saragih Abdullah Sani Abd Rahman Comparisons of automated machine learning (AutoML) in predicting whistleblowing of academic dishonesty with demographic and theory of planned behavior MethodsX Automated Machine Learning (AutoML) |
| title | Comparisons of automated machine learning (AutoML) in predicting whistleblowing of academic dishonesty with demographic and theory of planned behavior |
| title_full | Comparisons of automated machine learning (AutoML) in predicting whistleblowing of academic dishonesty with demographic and theory of planned behavior |
| title_fullStr | Comparisons of automated machine learning (AutoML) in predicting whistleblowing of academic dishonesty with demographic and theory of planned behavior |
| title_full_unstemmed | Comparisons of automated machine learning (AutoML) in predicting whistleblowing of academic dishonesty with demographic and theory of planned behavior |
| title_short | Comparisons of automated machine learning (AutoML) in predicting whistleblowing of academic dishonesty with demographic and theory of planned behavior |
| title_sort | comparisons of automated machine learning automl in predicting whistleblowing of academic dishonesty with demographic and theory of planned behavior |
| topic | Automated Machine Learning (AutoML) |
| url | http://www.sciencedirect.com/science/article/pii/S2215016123003606 |
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