Suppressing the Level of Corruption in Kampar District: A Study of The Impact of Accountability, Audit Opinions, Publication of Financial Statements, Audit Results And Follow-Up Of Audit Results

This study aims to evaluate the effects of accountability, audit opinion, financial statement disclosure, audit findings, and follow-up on audit results in reducing corruption levels within the Regional Apparatus Organization (OPD) of Kampar Regency. Conducted through a survey method, the study utilized a purposive sample of 50 accounting and finance professionals to achieve its objectives. Consistent with the research objectives, this study adheres to a conventional academic structure, utilizing clear and objective language, precise technical terminology, and a logical progression of ideas presented in a balanced manner. Primary data was collected via questionnaire distribution. Multiple linear regression tests were utilized to analyze the data. Results indicate that accountability, audit opinion, financial statement disclosure, audit findings, and follow-up of audit results significantly suppress the level of corruption in the Kampar district. It is imperative to follow up on audits to maintain transparency and accountability in the district. Based on simultaneous testing, the evidence suggests that factors such as accountability, audit opinion, disclosure of financial statements, audit findings, and follow-up on audit results play a role in reducing the incidence of corruption in Kampar district, in 2023.

Comparative Analysis of Sentiment Analysis Techniques for Social Media

Nowadays the excessive use of internet produces a huge amount of data due to the social networks such as Twitter, Facebook, Orkut and Tumbler. These are microblogging sites and are used to share the people opinions and suggestions on daily basis relevant to the certain topic. These are beneficial for decision making or extracting conclusions. Analysis of these feeds aims to assess the thinking and comments of people about some personality or topic. Sentiment analysis is a type of text classification and is performed by various techniques such as Machine Learning Techniques and shows that the text is negative, positive or neutral. In this work, we provide a comparison of most recent sentiment analysis techniques such as Na?ve Bayes, Bagging, Random Forest, Decision Tree, Support Vector Machine and Maximum entropy. The purpose of the study is to provide an empirical analysis of existing classification techniques for social media for analyzing the good performance and better information retrieval. A comprehensive comparative framework is designed to compare these techniques. Various benchmark datasets (UCI, KAGGLE) available in different repositories are used for comparison purpose. We presented an empirical analysis of six classifiers. The analysis results that Random Forest performs much better as compared to other. Efforts are made to provide a conclusion about different algorithms based on numerical and graphical metrics to conclude that which algorithm is optimal.