Transaction Activities Research Articles

Suspicious Financial Activity in the Context of In-Game Asset Exchange Marketplace

In this study, we investigated the expanding problem of suspicious activity when using online in-game asset trading platforms. The decentralized structures and anonymity offered by these platforms provide a basis for suspicious actions, creating a threat to the virtual economy. By evaluating 18,157 rows of anonymized transaction data from 38 unique sellers with the help of the interquartile range approach and network analysis, we were able to identify suspicious activities. The results highlight suspicious online activities of individual transactions. This research contributes by identifying new, concerning trends and unraveling complex networks by analyzing in-game asset transaction data. It also assists in informing experts and lawmakers about new suspicious activities.

AUTOMATED INDOOR ACTIVITY MONITORING FOR ELDERLY AND VISUALLY IMPAIRED PEOPLE USING QUANTUM SALP SWARM ALGORITHM WITH MACHINE LEARNING

Elderly and vision-impaired indoor activity monitoring uses sensor technology to monitor interaction and movement in the living area. This system can identify deviations from regular patterns, provide alerts, and ensure safety in case of emergencies or potential risks. These solutions enhance quality of life by promoting independent living while offering peace of mind to loved ones and caregivers. Visual impairments have become the chief part of these problems over the past few years. Usually, visually impaired people seek help from others to keep doing their daily tasks. An automated human activity recognition (HAR) technique could enhance a vision-impaired person’s transaction activity and safe movement. HAR using deep learning (DL) includes training neural networks to automatically detect and classify various activities carried out by humans based on sensor information. By leveraging DL approaches, the HAR system could accurately identify and classify activities, including sitting, standing, walking, or running, from the information captured by sensors or wearable devices. This system is integral to smart home automation, healthcare, and sports analytics. This study develops an Automated Fractal Quantum Salp Swarm Algorithm with Machine Learning based HAR (QSSA-MLHAR) for assisting elderly and visually impaired people. The QSSA-MLHAR technique uses sensory input data to identify human activities in diverse classes. In the QSSA-MLHAR technique, the min–max scalar is primarily used to scale the input data. Besides, the QSSA-MLHAR technique applies a mixed extreme learning machine (M-ELM) model to classify various human actions. The QSSA-based hyperparameter tuning process is used to improve the detection results of the M-ELM system. A wide range of simulations was performed to ensure the better performance of the QSSA-MLHAR method. The comprehensive outcomes assured that the QSSA-MLHAR technique performs well compared to other models.

Pengendalian Intern Manajemen Keuangan pada Perusahaan Kontraktor

This research aims to determine and analyze the project financial management system at CV. Diana Concern. The method used is descriptive qualitative with the type of research being a case study. The data collection technique uses a triangulation process which is carried out by conducting interviews, observation and documentation. The research results show that CV. Diana Concern, which is the object of this research, carries out financial management system activities, especially in project operations, not yet fully using financial system procedures which are the basis for internal control, so it requires improvements to the existing financial system. Where all financial activities are completely handed over to representatives from the subcontractors in each project. Transaction activities that are not recorded on time by ignoring forms or documents accompanying the transaction, as well as implementation orders that are not professionally organized, can cause financial reporting to be inaccurate, not timely and accountable

Analysis of Market Diversification Trends and Network Characteristics Based on M&A Transactions in North America

Despite the increasing investment opportunities in emerging technologies, strategic alliance and dynamic investment strategy suffer from a limited understanding of the market investment trends and industry convergence. Therefore, this study aims to develop a structured framework to examine the market diversification trend and the industry-to-industry influential degree. The proposed framework utilizes M&A transaction activities from the interests of buyer and target industries from 2009 to 2018 in North America. The M&A network is then examined for the difference in the structural characteristics between the buyer and target industries. This study identifies market irregularity and diversification trends and applies the initial findings as market-level evidence to elucidate industry convergence potentials. Degrees of a specific industry’s influence on other industries are also presented and discussed from the perspectives of buyer and target industries. The findings of this study contribute to the development of industry convergence conceptual model and advances knowledge regarding market diversification, collaboration-driven industry convergence, and investment strategy.

PERBANDINGAN AKURASI ALGORITMA K-NEAREST NEIGHBOR DAN LOGISTIC REGRESSION UNTUK PREDIKSI CUSTOMER CHURN

In this highly developed era, many transaction activities have shifted from offline to online, which is commonly called E-commerce. This study provides an overview of customer churn prediction using two algorithms that are often used, namely K-Nearest Neighbor and Logistic Regression. In this study, a comparison was made of the two algorithms in determining significant accuracy in predicting customer churn or unsubscribing customers. This research was conducted with three tests to compare the performance of the two algorithms from the distribution of test data by 30 percent, 25 percent, and 20 percent. The algorithms have a good performance on the distribution of test data by 20 percent, and Logistic Regression algorithm has a better performance at 82.89% than K-Nearest Neighbor algorithm which is only 81.84%.

Transformative Dynamics in Private Equity: Exploring Co-investment, ESG Integration and Digitalization Strategies

This paper delves into the evolving landscape of private equity (PE) financing, particularly in the context of the real estate and project development sectors amidst global economic fluctuations and geopolitical uncertainties. With traditional debt financing losing its sheen, the study focuses on the burgeoning role of PE, especially co-investment funds, as a viable financial alternative. Utilizing a rich dataset from reputable sources such as Morningstar, Pitchbook, and the European Central Bank, along with other pertinent studies, the paper presents a nuanced analysis of the declining PE transaction activity in 2023. It highlights how this downturn has catalyzed the adoption of innovative strategies, including the rise of “continuation vehicles” and the increasing significance of co-investments. Moreover, the research offers an insightful examination of the German SME sector, showcasing how PE can be a transformative force. It particularly emphasizes the incorporation of ESG (environmental, social, and governance) considerations and the advancement of digitalization within these enterprises. The study employs a mixed-methods approach, blending qualitative insights with a robust collection of both primary and secondary data. It argues that PE is not merely a financial instrument but a catalyst for meaningful business evolution, enabling German SMEs to effectively respond to current market challenges and undergo essential transformations. This paper aims to contribute to the broader understanding of PE’s role in contemporary business environments, offering valuable perspectives for academics, industry practitioners, and policymakers.

Penerapan Algoritma Clustering untuk Segmentasi Pelanggan E-commerce berdasarkan Data Pembelian dan Aktivitas

In the current digital era, e-commerce has become one of the main pillars of global trade. With the ever-increasing amount of transaction and user activity data, e-commerce companies are faced with the challenge of understanding and managing diverse customer segments more effectively. This paper discusses the application of clustering algorithms for e-commerce customer segmentation based on purchasing data and user activity. The aim of this research is to identify homogeneous customer groups to support more targeted marketing strategies and increase customer retention. The problem faced is how to process big data originating from user transactions and activities on e-commerce platforms, as well as how to identify patterns that are useful for customer segmentation. The data used in this research includes purchase history, frequency of visits, length of time spent on the site, and interactions with certain products. The solution method applied in this research is the clustering algorithm, especially K-Means and DBSCAN. K-Means is used to group data into a predetermined number of clusters based on the Euclidean distance between data points. Meanwhile, DBSCAN is used to identify clusters with high density and separate data that is considered noise or outliers. Data preprocessing is carried out to clean and normalize the data before being applied to the clustering algorithm. Validation of clustering results is carried out using metrics such as Silhouette Score and Davies-Bouldin Index. The research results show that by applying the clustering algorithm, customers can be grouped into several segments that have similar characteristics. For example, we found groups of customers with high purchase frequency but low transaction value, as well as other groups with high transaction value but low purchase frequency. This information is very useful for companies to design more effective marketing strategies, such as special offers for customers with high transaction values ​​or loyalty programs for customers with high purchasing frequency. The conclusion of this research is that clustering algorithms can be a very effective tool in e-commerce customer segmentation, allowing companies to understand customer behavior patterns and develop more targeted and effective marketing strategies. Thus, implementing this method is expected to improve business performance and overall customer satisfaction.

The Influence of Social Media, SEO (Search Engine Optimization), and Mobile Applications on Consumer Purchasing Decisions at PT. Udayana Bali Ambassador

Consumer behavior is changing significantly due to the development of technology and the internet, which has impacted transaction activities from conventional to online. In Indonesia, the increase in internet users has made it easier for consumers to access information and compare products before purchasing. This requires companies, including PT Udayana Bali Ambassador, to understand the influence of digital elements such as social media, SEO, and mobile applications on consumer purchasing decisions. The three digital marketing elements are essential in building consumer relationships, increasing visibility, and facilitating buying. The combination of these three strategies creates an effective digital marketing ecosystem. This research aims to analyze the influence of social media, SEO, and mobile applications on consumer purchasing decisions at PT Udayana Bali Ambassador. The research method used is quantitative and descriptive, with data collection through questionnaires distributed online to 97 respondents. Instrument tests include validity, reliability, and classical assumption tests such as normality, multicollinearity, and heteroscedasticity. Data analysis was conducted using multiple linear regression to test the effect of independent variables on the dependent variable. The results showed that social media, SEO, and mobile applications significantly influence consumer purchasing decisions. The calculated F value in this study has exceeded the F table value, namely, 407.078> 3.092, which means that social media (X1), SEO (Search Engine Optimization) (X2), and Mobile Application (X3) simultaneously affect consumer purchasing decisions (Y). Based on this, it can be concluded that social media, SEO, and mobile applications influence consumer purchasing decisions.

The Influence of Project Management Office, Education and Training, and Budget on the Implementation of Enterprise Resource Planning-Sap at PT Danareksa (Persero)

The application of information system technology in the company's business operations has the potential to have a major impact on the company's performance. In the last year, the development of information system technology has developed very rapidly by presenting various latest and cutting-edge innovations. The rapid development of information system technology encourages companies to be able to adapt to these changes, but not all types of information technology available are useful or in accordance with the needs of the company. In connection with the appointment as a Holding based on Government Regulation No. 113 of 2021. The company needs a reliable, practical and good information system to support overall operational transaction activities and also as a holding company and the desire to achieve a well-integrated and automated system. The phenomena that occur include: The implementation of Enterprise Resource Planning ("ERP") has not been maximized in carrying out the company's operational transactions and financial consolidation with holding members, The research approach used in this study is a quantitative approach. The results of hypothesis testing in this study are the first hypothesis to be accepted in this study. Based on the results of data processing, the t-statistical value of 3.194 is greater than the t-table of 1.96. The p-value of 0.001 is less than the alpha value of 0.05. That is, the first hypothesis shows that the Project Management Office (H1) has a positive effect, and Education and Training (H2) has no effect. It can be said that the Project Management Office has an effect on the implementation of ERP-SAP. The second hypothesis in this study, according to the findings of the study, the second hypothesis states that education and training on ERP- SAP implementation have a P-Value of 0.481 > 0.0. It can be concluded that Education and Training have no effect on ERP-SAP Implementation. ERP-SAP while the Budget has a positive effect on the implementation of ERP-SAP because it has an estimated value of 0.493. The Effect of Budget on ERP-SAP Implementation is significant because it has a p-value of 0.000 < 0.05. It can be concluded that the Budget has a positive effect on the implementation of ERP-SAP.

Pengaruh Kemudahan, Kepercayaan dan Keamanan terhadap Keputusan Penggunaan Mobile Banking BCA di Jakarta

With the increasingly rapid development of information technology, people need to do things quickly, effectively and efficiently, especially when carrying out transaction activities. Banking companies are improving services with the introduction of mobile banking. Mobile banking is an innovation from development, science, information technology and communications used by banking companies to facilitate customer transactions and meet needs, which can be accessed by installing an application on the phone. BCA Bank is a private bank that offers digital banking services, one of which is a mobile banking product known as BCA mobile, launched in 2011. By providing banking services via smartphone. BCA bank hopes that customers can enjoy the convenience and speed of BCA Mobile. The aim of this research is to determine the influence of convenience, trust and security on decisions to use mobile banking in Jakarta. The method used in this research is non-probability sampling, namely purposive sampling. The data collection technique was through distributing questionnaires to 100 respondents and processed using SPSS version 25. The analysis method used was multiple linear regression. The results of the hypothesis test show that the variables of ease, trust and security have a significant effect on usage decisions.

Fund transfer fraud detection: Analyzing irregular transactions and customer relationships with self-attention and graph neural networks

This paper presents a method for identifying fraudulent fund transfers using real bank data, analyzing customer information, transactional activities, and customer relationships. The preprocessing step transforms high-dimensional, irregular transaction time series into regular time series, then further compresses them in a latent space using a self-attention-based autoencoder. To address the scarcity of fraudulent data samples and mitigate training issues caused by data imbalance, various deep generative models, including the conditional variational autoencoder and Wasserstein generative adversarial network, are applied to generate additional fraudulent raw data and augment fraud samples in latent space. The reparameterization trick is integrated into the encoder–decoder structure to boost the model’s generative capabilities. Additionally, a Graph Neural Network (GNN) is used to model customer relationships. The proposed approach utilizes end-to-end learning, integrating the autoencoder’s reconstruction loss, KL divergence loss (when reparameterization trick is applied), and classification loss for fraud detection. To optimize computational resources, neighborhood sampling for GNN is combined with mini-batch training for the autoencoder, improving both training efficiency and model reliability. Comprehensive experiments demonstrate the effectiveness of the proposed fusion network, highlighting the importance of each component and preprocessing step. For example, the areas under the precision–recall curves for fraud detection show notable improvements in our model. For suspicious transactions identified by the bank’s rules, other models range from 0.66% to 22.15%, while our model reached 27%. For non-suspicious transactions, other models range from 2.53% to 22.00%, with our model achieving 22.90%. This model has potential for wider applications in anomaly detection, particularly in datasets with irregular time series and complex customer relationships.

Digital Economy, Green Dual Innovation and Carbon Emissions

The digital economy serves as a pivotal catalyst for sustainable and eco-friendly development. This study employs a suite of advanced econometric models, including the fixed effects, mediation, threshold and moderation model, to elucidate the intricate dynamics by which the digital economy influences carbon emissions through the lens of green innovation. Building on the existing research on digital economy, green technology innovation and carbon emissions, this paper takes a dual-innovation perspective and divides green technology innovation into disruptive green technology innovation and incremental green technology innovation. And from the government and the public level, it explores how social concerns affect the effect of digital economy on carbon emissions. The analysis is grounded in a comprehensive dataset encompassing a decade of provincial-level data from 2011 to 2021 across China’s 30 provinces. The benchmark regression outcomes indicate the digital economy’s ability to substantially cut down carbon emissions; the threshold effect and mediating effect models demonstrate that a single-threshold effect exists and that disruptive and progressive green technological innovations mediate such ability. Additional research reveals that the digital economy’s impact on carbon emissions could be positively moderated by public and governmental attention. Eastern and western regions in China, as well as those with high levels of foreign investment and low levels of technological transaction activity, are more affected by the digital economy in terms of carbon emission reduction. Our conclusions offer practical recommendations for digital economy’s coordinated advancement and carbon emissions mitigation, and guide local governments to achieve sustainable development goals (SDGs).

Pengaruh E-Service Quality Dan Consumer Trust Terhadap Repurchase Intention Pada Pengguna E-Commerce Lazada Di Kota Bandung

The rapid development of technology in the current era of digitalization is driving increasingly tighter business competition. Producers are required to be more critical in innovating and developing their business following trends occurring in society. Online stores are one of the impacts of the rapid development of technology in the current era of digitalization. Various products can be purchased online via e-commerce, ranging from food, clothing, household necessities, to vehicles. This trend has led to the continued increase in e-commerce users in Indonesia and is even projected to continue to increase until 2029. However, this is not in accordance with what is happening with Lazada e-commerce which continues to experience a negative trend from 2019-2022 with a decreasing index. in the top brand award released by the top brand index, the increase in 2023 is considered not significant so it is necessary to analyze the factors that could influence the decline in the Lazada e-commerce index. One of the indicators in assessing the index for the top brand award is future intention, where in this indicator there is repurchase intention, so it can be interpreted that the decline in the index is due to a decrease in interest in repurchasing Lazada customers after carrying out transaction activities on Lazada e-commerce. Therefore, it is indicated that the e-service quality and consumer trust factors can influence the repurchase intention of Lazada e-commerce customers, so these factors need to be tested to find out the true influence on repurchase intention. The population in this study is respondents aged 17-45 years who have made transactions at least twice a year on Lazada e-commerce. With judgment sampling as a sampling technique, 110 respondents were obtained. Descriptive analysis techniques were used to describe the data that had been collected and after that multiple regression analysis was carried out with the help of SPSS version 26 software to answer the problems in the research. The results of statistical tests found that there is a positive influence of both variables on repurchase intention and they also have a simultaneous influence, which indicates that these two factors need attention to obtain good repurchase intention.

Grid Density Algorithm-Based Second-Hand Housing Transaction Activity and Spatio-Temporal Characterization: The Case of Shenyang City, China

Grid Density Algorithm-Based Second-Hand Housing Transaction Activity and Spatio-Temporal Characterization: The Case of Shenyang City, China

Sharia Economic Law Criticism on Muhammad Syahrûr’s Hudûd Theory Regarding Bank Interes

n modern economics applied by conventional financial institutions, the interest system is inseparable. Economic system based on sharia principles emerged as a solution to riba-based (usury-based) transactional activities, which in sharia economic law is in the khilafiyyah (dissent) territory amongst scholars regarding whether the conventional bank interest is riba which is prohibited by Islamic law. A figure named Muhammad Syahrûr came up and offered his theory of limits (nazhariyyah al-hudûd) in providing riba limitations in the Quran. This research aims to describe M. Syahrûr's thoughts on bank interest based on his theory of limits, and to analyze it based on the review of sharia economic law (fiqh muamalah mâliyyah). this reseach is understood as a literary study research which is a research on secondary materials. The type of data used in this research is secondary data, which is literature focusing on the thoughts of a figure, which is Muhammad Syahrûr.The result of the research shows that Muhammad Syahrûr's opininion that allows the practice of bank interest based on his theory of limits (nazhariyyah al-hudûd) is a marjûh (weak) opinion, since it is not supported by valid arguments and it also contradicts the opinions of the jumhur ulama (the majority of the scholars).

Library System Development Using Design Thinking Method

The library information system that is currently being used is still inadequate in providing information and various problems occur on the website which disrupt various transaction activities in the library. The aim of creating this website is to provide facilities for all users, from librarian, and students to lecturers, to obtain library information easily. The website will be built with various features in the hope of making it easier for users to get information from the library. Using the design thinking method, research is carried out to identify problems that occur and solutions are found according to the needs of library users. Based on the results of the usability test, the score was above average and in the good category and this system can be generally accepted by all users. Even though the usability results were unsatisfactory, the majority of users, 82.41%, were satisfied with this library system. The performance test results also show that the website has very good performance with an average score of 92.68%.

PERLINDUNGAN KONSUMEN TERHADAP TRANSAKSI DALAM SOCIAL-COMMERCE DI INDONESIA

The purpose of this research is to analyze consumer protection against transactions that occur on social-commerce platforms in Indonesia, which are reviewed from the legal relationship between business actors, consumers, and social-commerce platforms (TikTok Shop and Instagram Shopping) and to analyze electronic transactions on social-commerce electronic systems. whether it has fulfilled the principles of consumer safety and security. The type of research used by the author is a normative legal research type. The approach in this research uses the Statute Approach and a Conceptual Approach. The results of the research obtained that the legal relationship that exists on TikTok Shop is TikTok Shop as a third party that bridges the buying and selling transactions carried out by business actors and consumers. Meanwhile, in Instagram Shopping, the legal relationship that exists is between business actors and consumers, the role of Instagram Shopping is only as a page for business actors and consumers to meet. TikTok Shop and Instagram Shopping have several weaknesses in protecting consumer safety and security in carrying out transaction activities in their applications.

Legal Protection for Directors Related to Agreements with Third Parties that Cause Losses to the Company

The Board of Directors is obliged to take responsibility for its actions on the employment contract agreement of any responsibility born of any transaction or employment contract activity carried out by it in accordance with the duties and responsibilities given by the board of commissioners to it as a director of the company, with the consideration that the activity has been carried out with due regard to prudential standards and good faith from the duties and functions as a director. The research method used is normative juridical research using primary, secondary, and tertiary legal materials and research places, data collection techniques in this research are literature studies and document studies and interviews, data analysis is carried out by collecting, classifying, and analysing based on the results of literature study research and studies in several companies, the results of the study show that the legal protection of the Board of Directors on agreements with third parties that harm the company so that if something undesirable happens, the sanctions for violations of the Board of Directors in their position as budget users will face criminal, civil and administrative sanctions. So that directors who get authority based on applicable laws and regulations in carrying out a work contract activity either obtained through delegation of authority, then have fulfilled the criteria in each agreement.

Does personal innovativeness and personal anxiety matter? The predictors of continuance use intention of an e-wallet system

PurposeElectronic wallets (e-wallet) systems have revolutionized transactional activities, prompting individuals to adopt digital payment methods. This study investigates the determinants of the continuance intention of the e-wallet system Touch 'n Go in Malaysia. We propose a framework integrating the diffusion of innovation theory (DOI) and mental accounting theory, thereby assessing the impacts of product-related factors (convenience, compatibility, perceived risk and perceived compatibility) and the moderating role of personal-related factors [personal innovativeness (PI) and personal anxiety].Design/methodology/approachData were collected involving 381 Malaysian Touch 'n Go users. The partial least squares structural equation modeling (PLS-SEM) was applied as a data analysis technique.FindingsAs demonstrated by the results, convenience serves as a facilitator, while perceived risk acts as an inhibitor, influencing the continuance intention of the application. Compatibility and perceived complexity are found to be insignificant antecedents. However, neither PI nor personal anxiety was found as moderators.Originality/valueThis research brings contributions to the electronic commerce literature by extending the understanding of the predictors and moderators of the continuance intention of Touch 'n Go users in Malaysia. We discuss how these findings implicate the two theories and offer insights for service providers and policymakers to promote the post-adoption intentions of Touch 'n Go.

Cashless Payment System: A Study on Perception of Small and Medium Retailers

The cashless payment system has been prevailing strongly in every business field because of its unique and convenient nature. All the financial transactions have been made easier through this system. It has reduced the hassle of carrying physical cash and also the trepidation of losing money. Innovative technologies such as UPI, E-wallets, Mobile banking, Smart cards, etc., have encouraged digital transactions in businesses. Cashless payment system plays a crucial role in retail business due to the higher activity of transactions in the retail business. It helps the retailers in sustaining transparency and accountability of their business transactions. Due to digital illiteracy and security issues, many retailers and customers are still preferring the traditional cash payment system. Many initiatives have been taken up by the Indian government to promote digital transactions and subside the flow of black money to some extent which leads to the economic development of the country. The research study aims to understand the perception of the retailers of medium and small businesses in Andhra Pradesh. The study focuses on the awareness among the retailers towards cashless payment system and also the problems faced by them through this system. The primary data was collected from 100 respondents who were small and medium business retailers in the Kadapa district of Andhra Pradesh. The study concluded that most of the retailers have adopted and are using cashless payment system in their business.