Pelanggaran Prinsip Etika Audit dalam Dysfunctional Audit Behavior Violation of Audit Ethics Principles in Dysfunctional Audit Behavior

Dysfunctional Audit Behavior (DAB) poses a significant threat to the integrity of audit practices and ethical standards. This research delves into the intricate web of ethical violations within DAB, examining the deviations from established ethical principles in the field of auditing. The study employs a comprehensive qualitative research methodology, incorporating interviews, case analyses, and ethical frameworks to unravel the underlying factors contributing to these violations. The research uncovers the multifaceted nature of ethical breaches within DAB, focusing on issues related to impartiality, integrity, objectivity, professionalism, and confidentiality. Through in-depth analyses of real-world cases and interviews with auditors, clients, and regulatory bodies, this study aims to identify patterns and motivations driving auditors towards unethical practices. Moreover, the research explores the impact of these violations on the credibility of audit reports and the overall trust in the auditing profession. The findings of this research not only shed light on the root causes of ethical misconduct but also offer valuable insights for regulatory bodies, audit firms, and educators. By understanding the complexities of DAB, stakeholders can develop targeted strategies to prevent and mitigate these violations effectively. Strengthening ethical education, enhancing regulatory oversight, and fostering a culture of integrity within audit organizations emerge as key recommendations from this study.

Feature Selection Using Rough Set Based Heuristic Dependency Calculation

The amount of data to be processed is significantly increasing day by day. The increase in data size is not only due to more number of records but also due to substantial number of attributes added to space. The phenomenon is leading to the dilemma called curse of dimensionality i.e. datasets with exponential number of attributes. The ideal approach is to reduce the number of dimensions such that resulted reduced set contains the same information as present in the entire set of attributes. There are various approaches to perform this task of dimensionality reduction. Recently, rough set-based approaches, which use attribute dependency to carry out feature selection, have been prominent. However, this dependency measure requires the calculation of the positive region, which is a computationally expensive task. In this research, we have proposed a new concept called the “Dependency Classes”, which calculates the attribute dependency without using the positive region. Dependency classes define the change in attribute dependency as we move from one record to another. By avoiding the positive region, they can be an ideal replacement for the conventional dependency measure in feature selection algorithms, especially for large datasets. A comparison framework was devised to measure the efficiency and effectiveness of the proposed measure. Experiments on various publically available datasets show that the proposed approaches provide significant computational performance with same accuracy as provided by conventional approach. We have also recommended seven feature selection algorithms using this measure. The experimental results have shown that algorithms using the classes were more effective than their counterparts using the positive region-based approach in terms of accuracy, execution time and required runtime memory.