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.This study aims to analyse the role of Financial Information Systems (FIS) in supporting risk management, decision-making, and organisational performance in the digital transformation era. This study employs the Systematic Literature Review (SLR) method to examine articles indexed in Scopus from 2016 to 2026. The PRISMA framework is used to ensure a systematic, transparent article selection process, resulting in the selection of 37 relevant articles for further analysis. The results of the study show that Financial Information Systems make a major contribution to improving financial transparency, operational efficiency, the quality of strategic decision-making, and organisational risk mitigation. In addition, the integration of emerging technologies such as Artificial Intelligence (AI), FinTech, big data analytics, and cloud computing further strengthens the effectiveness of financial information systems in modern organisations. This study contributes theoretically by mapping research trends and identifying research gaps, while providing practical benefits for organisations seeking to increase competitiveness through digital financial systems. For future research, it is recommended to develop a more predictive and intelligent Financial Information Systems model to address future business dynamics.

This study aims to provide a comprehensive analysis of Predictive Policing and its implications for law enforcement transformation in Indonesia, based on an extensive review of its global applications, benefits, and challenges. The study uses qualitative literature and international case study review methods to assess the impact and complexity of implementing digital technologies such as artificial intelligence (AI), machine learning, and big data analytics within a Predictive Policing framework. The results of this review highlight that while Predictive Policing offers significant potential for proactive crime prevention and increased operational efficiency, its implementation is consistently fraught with critical legal, ethical, and technical challenges, including regulatory gaps, risks of algorithmic bias, and data privacy concerns, which are particularly relevant to Indonesia. The findings underscore that public trust and police legitimacy in the context of adopting such technologies are strongly influenced by transparency, strong accountability mechanisms, and community involvement in shaping their use. This study contributes to the growing discourse on digital policing in developing countries and culminates in practical policy recommendations designed to guide the Indonesian police towards the development and implementation of Predictive Policing models that are effective, efficient, and fundamentally respectful of legal and human rights principles.

Smart cities are increasingly leveraging advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data Analytics to optimize urban management and improve the quality of life for citizens. However, managing vast and diverse datasets from numerous sources in real-time presents several challenges. This research proposes a modular framework that integrates distributed data processing engines with container-based workflow orchestration to address scalability, latency, adaptability, and fault tolerance in smart city data analytics. The framework utilizes cloud native technologies, including Apache Spark and Kubernetes, to efficiently manage resources and ensure high availability. The experimental setup tested the framework’s ability to handle dynamic data loads, demonstrating scalability through real-time resource allocation and low-latency processing. The adaptability of the framework was evident in its seamless integration with various data sources, such as environmental sensors and traffic management systems, which require different processing methods. Additionally, the framework’s modularity provided fault tolerance, enabling continued operation even if individual components failed, a crucial feature for mission-critical applications in smart cities. Compared to traditional monolithic systems, the proposed framework outperformed in flexibility, scalability, and performance, offering significant improvements in handling real-time data streams. Despite these advantages, challenges remain, particularly in integrating heterogeneous data formats and optimizing real-time processing for high-priority applications. The research highlights the importance of scalable data analytics and efficient workflow orchestration for the future of smart city platforms, offering a foundation for the development of more resilient, adaptable, and efficient cloud native infrastructures.

The increasing complexity and scale of modern cybersecurity threats necessitate the development of advanced systems capable of efficiently detecting, analyzing, and mitigating incidents in real time. This paper proposes an automated framework for digital forensics and incident response that leverages big data analytics and real time network traffic profiling. The framework integrates cutting-edge technologies, including Apache Spark for real time data processing and Hadoop for scalable data storage, combined with machine learning models like LSTM and Autoencoders to detect anomalies and threats in network traffic. By automating the process of incident detection and response, this framework significantly reduces the time required to identify threats and improves the accuracy of forensic evidence correlation across heterogeneous network environments. The study highlights the advantages of using machine learning models and big data tools to address the limitations of traditional manual and semi-automated systems, which often struggle to keep pace with large-scale data generation. Testing results demonstrate that the proposed framework can handle large data volumes efficiently, providing real time, actionable insights with significantly reduced response times. Additionally, the framework improves forensic analysis by enabling the correlation of evidence from different devices and protocols, making it more effective than traditional methods in identifying the root cause of security incidents. However, challenges related to data heterogeneity, scalability, and system integration were encountered during testing. The proposed framework holds promise for significantly enhancing the efficiency and effectiveness of cybersecurity operations, with future work focusing on further integration of advanced AI techniques and machine learning models for dynamic and adaptive incident response.

This study explores the transformative role of big data-driven Decision Support Systems (DSS) in global digital enterprises, particularly focusing on their impact on operational efficiency and corporate governance. By leveraging big data analytics, DSS offer organizations the tools to process vast amounts of real-time data, enabling executives to make more informed decisions that optimize resources, improve productivity, and reduce operational costs. The research highlights the integration of predictive analytics, machine learning, and real-time data processing within DSS, which allows businesses to gain strategic insights and anticipate market trends. Furthermore, the study emphasizes the significant role of DSS in enhancing corporate governance, improving transparency, accountability, and compliance with regulations. These systems foster better decision-making processes, which contribute to building trust among stakeholders and ensuring long-term organizational success. However, the study also identifies several challenges in implementing big data-driven DSS, including data management complexities, technological integration difficulties, and the need for skilled personnel. Despite these challenges, the findings demonstrate that big data-driven DSS are pivotal in driving competitive advantage, operational optimization, and governance improvements. The research concludes with actionable recommendations for executives to adopt and implement big data-driven DSS, emphasizing the importance of continuous support, training, and system integration. The study also suggests future research directions, including exploring the integration of emerging technologies like AI and IoT into DSS and assessing their long-term impact on sustainability and corporate governance.

Advances in digital technology have led to significant transformations in management accounting practices, particularly with the use of cloud accounting, big data analytics, artificial intelligence (AI), and digital-based management accounting information systems. These changes have resulted in a shift in the function of management accounting from merely a documentation tool to a strategic decision support system that provides information quickly, accurately, and in real time. This study aims to analyze the implementation of management accounting in the digital business era, identify the obstacles faced by organizations in the digitization process, and explain the opportunities that can be utilized to improve the efficiency of financial management systems. The research method applies a qualitative approach by conducting a literature study that reviews a number of journals, books, and scientific documents related to the topic. The research findings indicate that digitization has a positive impact on operational efficiency, clarity of information, and the quality of managerial decision-making. However, organizations still encounter various challenges, such as low human resource technological capabilities, complexity in system integration, and increased threats to data security. This study concludes that the implementation of digital management accounting is a strategic necessity for companies in the modern business era, requiring technological readiness, increased human resource capacity, and internal policies that support a complete digital transformation process.  

This study aims to analyze the strategic role of Information Technology (IT) in improving operational efficiency within e-commerce companies in Indonesia. A literature review approach was employed by examining various scholarly sources, including accredited national journals and relevant books. The findings indicate that the implementation of IT such as Enterprise Resource Planning (ERP) systems, Big Data Analytics, and Cloud Computing significantly accelerates business processes, reduces operational costs, and enhances data accuracy and service quality. E-commerce companies that integrate IT into their operations are shown to adapt more effectively to market dynamics and consumer preferences. The study concludes that the use of IT is not merely a supporting tool, but a key factor in creating competitive advantage. The implications of this research offer insights for e-commerce industry players and policymakers to continuously promote digital innovation in pursuit of efficiency and business sustainability.

The rapid development of the smart city concept encourages the need for energy management that is more efficient, sustainable and adaptive to the needs of modern urban communities. In this context, renewable energy is the main solution to reduce dependence on fossil energy sources that are limited and pollute the environment. This research aims to optimize the utilization of renewable energy in smart cities by integrating Big Data technology and Decision Support Systems (DSS). The approach used in this research is a case study and system modeling method, which involves collecting energy data from various sources such as IoT sensors, weather stations, and energy distribution systems in real-time. The data is then analyzed using Big Data Analytics techniques to identify energy consumption patterns, potential renewable energy production, and peak load predictions. Furthermore, a decision support system was designed to assist policy makers and city managers in determining optimal energy distribution and usage strategies based on the available data and simulations. The results show that the integration of Big Data and DSS is able to increase the efficiency of renewable energy utilization up to 25% compared to conventional systems. In addition, the system is also able to dynamically respond to changing conditions and provide more accurate and adaptive decision recommendations. These findings indicate that the synergy between data technology and decision support systems plays a strategic role in creating sustainable and environmentally sound smart cities.

This paper investigates how Big Data Analytics (BDA) can accelerate the transition to a low-carbon digital economy. We present a systematic literature-based research framework (2015–2025) that synthesizes applications of BDA in energy systems, transportation, industry and supply chains. The methodology combines systematic review and conceptual modelling to identify pathways through which BDA reduces emissions: (1) demand-side optimization, (2) operational efficiency, (3) predictive maintenance and (4) data-driven policy and market instruments. Results highlight concrete case examples smart grids, digital twins, and green supply-chain analytics and quantify benefits reported in recent literature. Key challenges such as data governance, carbon costs of computing, and policy integration are discussed. The paper concludes with policy recommendations and a research agenda to align digitalization with decarbonization goals.

The digital era has brought about a major transformation in the way organizations manage and utilize data. Data management is a key strategy in supporting decision-making based on accurate, fast, and relevant information. However, the rapid growth of data volume, diversity of sources, and complexity of data integration and security pose challenges in its management. These challenges include issues of data quality, inconsistency, duplication, and limitations in infrastructure and human resource capabilities. In addition, demands for compliance with regulations such as GDPR and the Personal Data Protection Act add to the complexity of ethical and responsible data management. On the other hand, technological developments such as big data analytics, artificial intelligence, the Internet of Things (IoT), and cloud computing present great opportunities to improve the efficiency and effectiveness of data management processes. Organizations that are able to adopt a data-driven approach and apply good data governance principles will gain competitive advantage, accelerate innovation, and improve customer satisfaction. This article comprehensively discusses the challenges and opportunities in data management from a data management perspective, and presents a framework for building an adaptive and sustainable data management strategy in the digital era. With a literature analysis and case study approach, this article aims to provide conceptual and practical contributions for organizations that want to optimize the potential of data as a strategic asset.

This study examines the application of big data analytics on Bhinneka.com, a leading e-commerce platform in Indonesia, to tackle the increasing in complexity of online user behavior in a swiftly changing digital environment. The primary issue is too challenges in evaluating extensive, unstructured, and heterogeneous user data, which obstructs personalization, marketing efficacy, and operational decision-making. The study seeks to assess the efficacy of big data instruments, specifically Artificial Intelligence Recommendation (AIRec) and Customer Data Platform (CDP), in improving user behavior forecasting. Service customization, and data-informed strategies. This study utilizes a qualitative case study methodology, including literature review and platform observation, to synthesis the many forms of big data analytics (descriptive, diagnostic, predictive, and prescriptive) and their implementation at Bhinneka.com. Significant findings indicate that the integration of AIRec and CDP has augmented the platform’s capacity to predict consumer preferences, improve marketing accuracy, and optimize logistics. However, obstacles stay the same, such as disjointed data systems, data quality concerns, and internal opposition to embracing a data-driven culture. The study suggests that although big data analytics substantially enhances Bhinneka.com’s digital competitiveness, ongoing investment in data infrastructure and organizational competence is crucial to fully harness its potential and preserve a competitive advantage in Indonesia’s e-commerce market.

The use of Big Data Analytics (BDA) in the financial audit process has altered the traditional audit method by improving audit quality and efficiency. The purpose of this study is to critically assess BDA's role in reducing audit delays and increasing audit quality. Using a literature review approach, this study intends to consolidate findings from national and international studies published between 2017 and 2024 that focus on the role of BDA in audit procedural transformation, fraud detection, and real-time decision support. The findings reveal that BDA not only allows auditors to review massive amounts of data in real time, but it also improves evidence collection and risk assessment. Audit delay has emerged as a significant variable in the relationship between BDA and audit quality, however its effectiveness varies depending on technical infrastructure and auditor expertise. Furthermore, this study identified implementation problems for BDA, such as data security threats, technology literacy gaps among auditors, and organizational readiness, all of which must be addressed in order for BDA to reach its full potential. This study adds to the theoretical and practical discussions about the strategic use of BDA to alter audit methods in the digital era.

The development of digital technology has encouraged organisations to adopt big data analytics in human resource management (HRM), but there are still challenges in leveraging it for effective decision-making. This study aims to investigate the effect of big data utilisation on the quality of HR decision-making and identify the supporting and inhibiting factors for its implementation. A quantitative method with purposive sampling survey was conducted on 100-150 respondents from companies that have implemented big data-based management information systems and HR analytics. Data were analysed using Structural Equation Modeling (SEM) with AMOS software. The results showed that big data analytics significantly improved the quality of HR decision-making, with HR digital competencies and organisational culture as important mediating factors. However, challenges related to cultural resistance and limited expertise were found to affect the effectiveness of implementation. The practical implications of this research emphasise the importance of HR digital capacity building and ethical data governance to support the transformation of HRM into an adaptive strategic function in the digital era. This research also contributes to the development of data-driven management theory with a holistic approach that integrates technical, human, and ethical aspects.

This study examines the importance of professional ethics of accountants and its impact on audit quality, focusing on auditor integrity and responsibility. Through a case study of PT Great River International Tbk, this study identifies ethical violations in financial statements that result in legal consequences for auditors. Qualitative methods are used to explore ethical sensitivity between early and late semester accounting students, and confirm that auditor ethics and integrity have a significant influence on audit quality, while competence does not show the same influence. In addition, this study notes that the application of technologies such as AI and Big Data Analytics can improve audit efficiency, despite the challenges of high costs and data security risks. A survey in Jakarta showed that auditor competence and ethics contribute positively to audit quality, while auditor experience has no significant effect. The conclusion of this study confirms that professional ethics of accountants is very important to maintain audit integrity and quality, and recommends more attention to factors that influence the determination of audit materiality levels.

Health risk stratification is crucial for preventive healthcare, yet existing models often rely on binary classification generalized disease prediction, neglecting personalized health indicators and graded risk levels. Many studies apply feature selection techniques like Relief and Univariate Selection without quantifying the weighted impact of features. To address these gaps, this study introduces a Big Data-driven Health Index (HI) framework using PySpark for scalable health risk stratification. The HI is computed as a weighted sum of health-related features using SHAP Analysis, XGBoost, Random Forest, and Correlation Analysis. PySpark enables efficient processing of large-scale health data, and individuals are classified into Low and High Risk. Optimal classification thresholds are determined using the Youden Index from the ROC curve to balance sensitivity and specificity. Personalized health recommendations are generated based on risk categories to guide preventive interventions. Performance evaluation reveals that Correlation Analysis achieves 100% precision and 98.90% recall, outperforming other methods. SHAP prioritizes recall but has low precision, while XGBoost and Random Forest improve precision but struggle with recall. By leveraging Big Data techniques with PySpark, this study enhances computational efficiency, scalability, and classification accuracy, addressing prior research limitations and providing a robust data-driven approach to personalized health monitoring.

This research aims to find out how personalization and big data analytics can be used as an effective marketing strategies in the era of society 5.0. This research uses a qualitative method with a descriptive approach which aims to explore, analyze and interpret phenomena related to the topic. The data in this research was obtained through literature study, by exploring information from various primary and secondary sources consisting of books, scientific journals, articles and other relevant publications. The research results show that personalization and big data analytics are effective strategies in marketing in the era of society 5.0. personalization and utilizing big data analytics technology, companies can understand consumer behavior, optimize marketing campaigns, and manage customer relationships more effectively. So it can increase customer satisfaction and loyalty.

The integration of digital technologies such as artificial intelligence (AI), blockchain, and big data analytics into financial risk management has substantially altered operational dynamics within various industries. This paper explores the dual-edged impact of these technologies, emphasizing both the opportunities they create and the challenges they present. Opportunities discussed include enhanced decision-making through advanced data processing, increased transactional transparency and security via blockchain, and improved operational efficiencies through automation. Conversely, the challenges encompass heightened cybersecurity risks, evolving regulatory compliance demands, costly technological integrations, and the emerging skill gaps in managing these digital tools. The paper further investigates the implications of these transformations for different sectors including banking, SMEs, and the construction industry. Each sector faces unique challenges and benefits from the adoption of these technologies. Future trends suggest a continued evolution influenced by technological innovation and regulatory changes. The paper underscores the necessity for ongoing research and adaptive strategies to fully leverage digital advancements in managing financial risks. By understanding these dynamics, financial institutions can better navigate the complexities of the digital age, ensuring robust risk management and a competitive edge in the global market.

This study aims to analyze factors that influence fraud disclosure . Fraud, or fraudulent acts, have become a significant problem in various sectors in Indonesia, especially with the dominance of corruption cases. This widespread corruption has a negative impact on the country's economy and public trust in institutions. Previous studies have shown that forensic audits, with an analytical approach to financial evidence, and investigative audits, which focus on thorough investigation procedures, significantly contribute to fraud disclosure. The professionalism of auditor judgment plays a role in ensuring that audit decisions are based on accurate analysis, while auditor independence is important to maintain integrity and objectivity in carrying out audits. In addition, internal control helps prevent and detect fraud early on. This study uses a qualitative method with a literature review approach sourced from trusted journals and scientific publications. The results of the study show that the integration of these factors can increase the effectiveness of fraud disclosure. These findings also emphasize the importance of implementing modern technology, such as big data analytics and artificial intelligence, to strengthen the audit system. The conclusion of this study suggests the importance of a combination of various audit elements to support better organizational oversight. The results of this study indicate that forensic audits, investigative audits, professional judgment, auditor independence, and internal control have a positive effect on fraud disclosure.

This paper discusses in depth the role of accounting in business decision-making in the digital era, as well as the challenges and opportunities faced by the accounting profession in the context of rapid and complex technological developments. In the modern business world, accounting information plays a crucial role, not only as a financial reporting tool but also as a foundation for strategic analysis that can influence the direction and policies of an organization. The issues raised pertain to the importance of timely and accurate accounting information in supporting managerial decision-making, as well as how this information can be used to enhance the performance and competitiveness of companies. The main objective of this research is to explore how digitalization and technological innovations, such as artificial intelligence (AI), big data analytics, and cloud-based information systems, affect accounting practices and their impact on business decision-making. This research also aims to identify the challenges arising from technological changes, including data security issues, the necessary technological skills, and regulatory changes that accountants must face. The methodology employed in this research is a normative approach with descriptive analysis, which includes literature studies and analysis of various relevant secondary data sources. Additionally, this research presents several case studies to demonstrate the application of theory in real practice and its impact on decision-making. The findings indicate that although digitalization brings many benefits in terms of efficiency and accuracy, there are serious challenges to be faced, such as the increased risk of data breaches and the need for higher technological skills among accounting professionals. The conclusion drawn from this research is that to optimally leverage the potential of accounting in supporting business decision-making, companies must invest in training and skill development for their accountants. Furthermore, the importance of implementing sustainable accounting practices is increasingly gaining attention, especially in the context of social and environmental responsibility. Thus, accounting not only functions as a supporting tool but also as a strategic element that adds value to companies in facing the complexities and uncertainties of the future.

Big Data Analytics has gained significant popularity in recent years, with many companies integrating it into their information technology roadmaps to enhance business performance. However, surveys indicate that Big Data Analytics demands substantial resources, including technology, costs, and talent, which often leads to failures in the initial stages of implementation. This study proposes a VGG6 architecture approach, intended to provide a framework for the initial implementation of Big Data Analytics. The study's outcomes include the implementation of the VGG6 architecture for processing images of aromatic plants using Python. Furthermore, this approach enabled the development of a Minimum Viable Product (MVP) solution that adheres to general Big Data principles, such as the 3Vs (Volume, Velocity, and Variety), and encompasses key technological components: 1) Data Storage and Analysis, 2) Knowledge Discovery and Computational Complexity, 3) Scalability and Data Visualization, and 4) Information Security.