The Effect of the Proportion of the Board of Commissioners, Audit Committee, Asymmetric Information and Company Size on Earnings Management Practices

The objective of this study was to empirically determine whether there is a correlation between the proportion of the board of commissioners, audit committee, information asymmetry, and company size with earnings management in the consumer goods manufacturing companies sector listed on the Indonesia Stock Exchange during the years 2010-2012. The study aims to clarify any potential links between the identified variables. Data was extracted from the financial statements of each sample company, which were publicly available on the websites www.idx.co.id and ICMD. The study employed purposive sampling, gathering data from 15 companies over a period of three years, resulting in a total of 45 observations. The independent variables include the proportion of the board of commissioners, audit committee, information asymmetry, and company size, while earnings management serves as the dependent variable. The findings from our study utilizing panel data and regression models demonstrate that neither the proportion of the board of commissioners, audit committee, information asymmetry, nor company size have any impact on earnings management either partially or concurrently.

A Comparativestudy of State-Of-The-Artmachine Learning Classificationmethods

In this era of information and technology data mining has gained much fame. Millions of versatile data records in various forms such as text, digits and images are going to store in databases and online data repositories. Machine learning techniques are playing vital role in analyzing such bulk of data in better way. Health department is considered as one of the most significant domain of generating huge collection of data associated to patient?s care, diagnostics, analysis and recommendations in various contexts based on disease and medical situations. The analysis of health care data can be very helpful for diagnosis of patients and decision making. A number of comparative researches in machine learning techniques have been performed in the literature on health data; however most of these approaches have been limited to a single dataset analysis, focused on a small number of parameters evaluation such as accuracy measurement and lack of graphical representation of statistical performance metrics. There is need to use more parameters and multiple data sets in order to evaluate machine learning algorithms for their maximum performance. The purpose of this research work was to propose and conduct empirical analysis of multiple machine learning classifiers through accuracy, precision, sensitivity, specificity and F-measure parameters to measure their maximum performance on health data. In this regard Diabetes, Kidney, Liver, Lungs and Heart datasets have been analyzed using Na?ve Bayes, LMT, SMO, JRip and J48 Decision Tree classifiers. It has been concluded from analysis that J48 classifier has shown optimal functionality on health datasets having large number of attributes. It has shown high accuracy and F-measure value on CKD (Chronic Kidney Dataset) dataset that is the highest ratio among other classifiers. While in case of small datasets (Lung cancer) Na?ve Bayes and SMO has beaten other classifiers. In graphical representation ROC curve has proved that Na?ve Bayes classifiers presented maximum performance. Precision-Recall curve proved that J48 has beaten other classifiers. Graphical representation of the results of different statistical performance metrics of machine learning Algorithms have also been provided.