Machine learning in predicting whistle-blowing intention of academic dishonesty with theory of planned behaviour

The COVID-19 pandemic and its aftermath have caused most higher educations to choose implement remote learning as a new method of instruction assessment. Nevertheless, has been criticized by having adverse impact on academic integrity. Whistle-blowing regarded an effective mechanism in limiting such unethical behavior. Thus, the main objective this study is identify influence attributes whistle-blowing intention among university students. effectiveness was observed prediction models based machine technique. This paper presents fundamental knowledge evaluations tree-based algorithms namely decision tree, random forest, be compared with logistics regression gradient linear model. A rigorous evaluation reports are provided that includes area under curve (AUC) supplementary metric measure model accuracy. Additionally, provide clearer insight models, pattern influences from adoption theory planned behavior (TPB) demography presented. findings revealed both TPB contain some degree impressive for generate good result.