Jurnal Riset Akuntansi Dan Bisnis Airlangga

Back Matter

Admin JRABA — Mon, 13 Nov 2023 00:00:00 +0700

Back Matter

STRATEGY FOR INCREASE OF ORIGINAL REGIONAL INCOME THROUGH CHANGES IN WASTE RETRIBUTION IN MOJOKERTO CITY

Puji Handayati Handoyo, Budi Eko Soetjipto, Etty Soesilowati, Ratna Dwi Nastiti — Mon, 20 Nov 2023 00:00:00 +0700

This research aims to determine the potential for increasing revenue from waste retribution in Mojokerto City as a means of increasing PAD thus it can maximize the potential of the APBD for the benefit of regional social and economic development. This is qualitative research using a descriptive case study approach. The data used is primary and secondary data collected using interview or FGD, documentation, and questionnaire methods. Data analysis was carried out using Miles and Huberman, starting from data collection, data reduction, data presentation, and conclusion. Data shows that the total revenue from waste retributions is only able to cover 5% of the total waste management costs thus the remaining 95% of these costs are charged to the APBD. Strategies are needed so that the potential for retribution revenue can be increased. The potential calculation is carried out by increasing the rates of 2x and adding household retribution data. The results show that increasing retribution rates and adding household data can increase the percentage of costs that can be covered by up to 50%. So, it can be concluded that this strategy contributes to increasing PAD. The research results are expected to have a theoretical contribution to contribution theory related to research on strategies for increasing Regional Original Income (PAD). Apart from that, it is hoped that the results of this research will have a practical contribution to the Mojokerto City Government in developing strategies to increase waste levy revenues.

FINANCIAL FRAUD DETECTION AND MACHINE LEARNING ALGORITHM (UNSUPERVISED LEARNING): SYSTEMATIC LITERATURE REVIEW

Nadia Husnaningtyas, Totok Dewayanto — Mon, 20 Nov 2023 00:00:00 +0700

This research aims to assess the usage of unsupervised learning in detecting financial fraud across various financial industries by identifying cognitive constructs, benefits, economic optimization, and challenges associated with fraud detection necessitating innovative approaches for effective detection. This study conducts Systematic Literature Review following PRISMA protocol for article selection of 27 journal articles published between 2010 and 2023, sourced from Scopus database. The analysis discloses that unsupervised learning has been implemented across diverse financial sectors, including online payments, insurance, and prominently in banking, especially for identifying anomalies in credit card transactions. K-Means is the most popular method used in unsupervised learning. Nevertheless, there are ongoing challenges that require solutions to ensure the efficacy of machine learning implementation, encompassing issues like class imbalance and the complexity of fraudulent activities. In theoretical terms, this research provides an understanding of cognitive concepts, benefits and applications, challenges, and practical recommendations in the use of unsupervised learning for financial fraud detection. This is useful for practical implementation, benefiting industry practitioners in selecting appropriate models with datasets that have the potential to enhance detection system accuracy and reduce financial losses due to fraud.

ENVIRONMENTAL UNCERTAINTY, AND EARNING MANAGEMENT: THE ROLE OF MANAGER ABILITY

Iswajuni, Bryan Luke — Mon, 20 Nov 2023 00:00:00 +0700

This study aims to examine the effect of environmental uncertainty on earnings management, with managerial ability as moderation. The population of this study are manufacturing companies listed on the Indonesia Stock Exchange (IDX) in 2017 - 2021. The sampling technique used purposive sampling, resulting in 344 annual reports of manufacturing companies. The data analysis technique uses moderated regression analysis (MRA). The results showed that environmental uncertainty had a positive effect on earnings management and indicated that managerial ability weakened the effect of environmental uncertainty on earnings management. The theoretical contribution of this research is to strengthen contingency theory in describing earnings management practices that occur due to environmental uncertainty, and can be weakened when supported by adequate managerial ability. Contributions for investors, can be used as a strategy in managing investment portfolios by considering internal factors such as managerial capabilities that can reduce earnings management practices, and external factors such as environmental uncertainty that can increase earnings management practices.

MACHINE LEARNING FOR E-COMMERCE FRAUD DETECTION

Rahayu Damayanti, Zaldy Adrianto — Mon, 20 Nov 2023 00:00:00 +0700

The study examines the effectiveness, challenges, and best machine learning algorithms for detecting e-commerce fraud. This study uses a systematic literature review to evaluate the effectiveness of machine learning-based e-commerce fraud detection, identify challenges, and identify the most effective techniques. The study examinedliterature extracted from the ScienceDirect, Emeralds, Wiley, and Springer databases, identifying 29 publications from recognized journals from 2012–2022, filtered using limitations and quality assessment criteria, and assessing paper eligibility. This study reveals that machine learning significantly enhances the accuracy of detecting e-commerce fraud. Yet, there are a number of issues that need to be resolved before machine learning can be utilized to detect e-commerce fraud. Poorer-quality data distribution is the biggest challenge in detecting e-commerce fraud. In order to determine the best machine learning strategy, the model's accuracy was also evaluated, and it was discovered that random forests performed the best in terms of accuracy. This study increases theoretical contributions as a continuation of previous research relevant to the concept of machine learning in detecting fraud in e-commerce. Then, based on the random forest's greater precision, it provides practical advice to e-commerce firms as a basis for decision-makers to find a suitable machine learning technique for fraud detection.

THE MODERATING ROLE OF ESG DISCLOSURE ON FIRM STRATEGY AND STOCK PRICE CRASH RISK

Gregorius Jeandry , Nurdin — Mon, 20 Nov 2023 00:00:00 +0700

This study aims to examine the effect of the company's business strategy on the stock price crash risk and examine the role of ESG disclosure as moderation in the relationship between business strategy and the stock price crash risk. The study population includes all companies listed on the Indonesia Stock Exchange (IDX) for the period 2017–2022. The sampling technique used was saturation sampling, with a total of 609 observations. Data collection techniques use content analysis. The data analysis technique uses ordinary least squares. This study found that firm strategy with the prospector typology affect the stock price crash risk, while the defenders and analyzers typologies have no effect on the stock price crash risk. ESG disclosure can weaken the effect of the business strategies of the prospector, defender, and analyzer typologies on stock price crash risk. Additional analysis found that ESG can reduce the risk of future stock price crashes. The contribution to signal theory is that there is consistently a strategic motivation for management to control good and bad information for the public. Bad information can cause the risk of price crashes to be higher than good information. Through ESG disclosure, it can provide positive signals to the public so that the risk of stock price crashes is low. The practical contribution for investors is as an investment strategy by paying more attention to the tendency of stock price crashes. For the government as a basis for making economic policies to create a well-maintained business climate.

AUDIT COMMITTEE EFFECTIVENESS AS FRAUD PREVENTION MECHANISMS

Larassanti Kusumosari, Shiddiq Nur Rahardjo — Mon, 20 Nov 2023 00:00:00 +0700

This study aims to examine the effect of audit committee effectiveness on financial statement fraud. This research type is explanatory research. The research population is property and real estate sector companies listed on the Indonesia Stock Exchange (IDX) during 2020–2022. The sampling method used is a purposive sampling with a total sample of 141 annual reports. The data collection method used content analysis on annual report. This study used logistic regression analysis assisted by EViews13 software. The results showed that the effectiveness of the audit committee, proxied by financial expertise, accounting expertise, and the number of audit committee meetings had negative and significant effect on financial statement fraud. This study contributes by strengthening agency theory, where the findings prove that the effectiveness of the audit committee can overcome agency conflicts and minimize financial statement fraud. Practically, this research contributes to regulators to develop policies regarding the work guidelines of the audit committee as a supervisory body for the financial reporting process and to investors to be able to improve investment decision strategies by analyzing the audit committee structure as a party that ensures the reliability of financial statements which are the basis for corporate decision making.

Front Matter

Admin JRABA — Mon, 20 Nov 2023 00:00:00 +0700

Front Matter