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All Journal Diponegoro Journal of Accounting Jurnal Teknik Informatika (JUTIF)
Bayu Indra Kusuma
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Computer Science & IT Economics, Econometrics & Finance

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ANALISIS MEKANISME CORPORATE GOVERNANCE TERHADAP PROBABILITAS TERJADINYA EARNINGS RESTATEMENT Bayu Indra Kusuma; Abdul Rohman
Diponegoro Journal of Accounting Volume 3, Nomor 2, Tahun 2014
Publisher : Diponegoro Journal of Accounting

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (416.802 KB)

Abstract

This paper seeks to examine the impacts of corporate governance on earnings restatement are indicated as a form of financial reporting failures on non-financial listed companies in Indonesia, with a view to providing reference to strengthen the corporate governance and improve the quality of financial information.            Data for this paper were obtained from the annual reports of non-financial listed companies from the period of 2004 to 2010 with a total population of 2.146, which includes 34 restatements especially earnings restatement by 34 companies. A control sample comprising non-restating companies is formed using match-pair procedures where restated and non-restated companies are matched by fiscal year, industry sector, and company size. Logistic regression model was used to measure the restatements dummy variables. Moreover, dummy variables are also used not only on the composition of the board and the concentration of ownership, but also the quality of the independent auditor. Earnings restatement on this paper focused on accounting misstatements and changes in accounting policies.            The results show that occurence of restatements especially earnings restatement can be prevented by strong internal governance, such as the proportion of independent directors and the ownership of large shareholders are higher. They have better control than others to monitoring and finding acts of fraud committed by management quickly and accurately. Surprisingly, the independence of the audit committee actually found a positive but not significant effect on the likelihood of higher restatements. The audit that have done by Big 4 found a negative but not significant effect on the likelihood of restatements is lower. While the government ownership found a positive but not significant effect on high possibility of restatements.
CYBERBULLYING DETECTION ON TWITTER USES THE SUPPORT VECTOR MACHINE METHOD Kusuma, Bayu Indra; Aryo Nugroho
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.809

Abstract

Social media is a platform that provides facilities for users to engage in various social activities. However, the increasing popularity of social media in the modern era also cannot be separated from the occurrence of several negative impacts, one of which is cyberbullying. Cyberbullying is an action that is done online that can harm the mental and emotional condition of an individual. To reduce this problem, this research aims to investigate the performance of the C-SVC and Nu-SVC algorithms from the Support Vector Machine method in classifying cyberbullying sentences. The data used is comments data from the @puanmaharani_ri account on Twitter, which was collected from September 25, 2020, to September 29, 2022, totaling 5,000 data. After the data is collected, it is labeled and preprocessed, and then the data will be weighted using the TF-IDF method. The result of the TF-IDF will be displayed in the form of a word cloud. Next, the Support Vector Machine method will classify cyberbullying sentences using several percentages split combinations such as 60%, 70%, 80%, and 90%. The test results show that the C-SVC method has the highest accuracy of 79.6% at a 70% percentage split, while Nu-SVC has the highest accuracy of 78.9% at a 60% percentage split. From these results, it can be concluded that the Support Vector Machine method with the C-SVC algorithm provides better results than Nu-SVC in classifying cyberbullying sentences.
Co-Authors Aryo Nugroho SATRIYAS ILYAS