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The integration of big data in the financial sector has increasingly attracted scholarly attention, particularly in areas such as risk management, fraud detection, algorithmic trading, and investment optimization. Given the rapid development of this field, it is essential to map research trends and identify emerging directions that shape the future of financial innovation. This study applies a bibliometric approach using 3,829 articles retrieved from the Scopus database from 1981 to 2025, with data processed through R Studio and the Bibliometrix-Biblioshiny application. The objective is to explore the intellectual landscape of big data finance and reveal research frontiers as well as thematic evolution. The results show a sharp increase in publications after 2015, alongside the growth of fintech and artificial intelligence applications, with dominant themes including blockchain integration, risk analytics, and predictive modelling. Cross-disciplinary and cross-regional collaborations continue to expand. These findings provide a comprehensive overview of how big data has shaped financial studies and offer insights for potential future research directions.

This study investigates the relationship between corporate governance characteristics, financial structure, and Enterprise Risk Management (ERM) disclosure in Indonesian non-financial firms. Focusing on manufacturing companies listed on the Indonesia Stock Exchange in 2023, the analysis examines whether board size, the proportion of independent commissioners, and leverage influence the extent of ERM disclosure. Using a quantitative approach, multiple linear regression is applied to secondary data obtained from firms’ annual reports. The findings indicate that board size and the proportion of independent commissioners do not have a significant effect on ERM disclosure, while leverage exhibits a positive and significant relationship. This result suggests that firms with higher debt levels are more inclined to enhance risk disclosure as a mechanism to address information asymmetry and demonstrate accountability to investors and creditors. The study contributes to the ERM and corporate governance literature by providing evidence from an emerging market setting and highlighting the practical importance of financial structure in shaping risk transparency, offering relevant insights for corporate decision-makers and regulators to strengthen sustainable risk management practices.

Hypertension is a major global health risk that requires accurate early detection, yet conventional methods struggle with complex and imbalanced health datasets. This study aims to optimize hypertension prediction using a Logistic Regression model integrated with Borderline-SMOTE to enhance recall and provide model transparency through SHAP (Shapley Additive Explanations). The method utilizes the BRFSS dataset, applying Borderline-SMOTE to address class imbalance at the decision boundary and XAI techniques for global and local interpretation. The findings show that the model achieved an accuracy of 0.719, an AUC of 0.800, and a significantly improved recall of 0.756. SHAP analysis identified age, high cholesterol, and BMI as the most influential risk factors, while waterfall plots successfully clarified individual risk extremes, ranging from 1.72% to 99.43% probability. These results imply that the proposed approach provides a sensitive and transparent screening tool for public health practitioners, effectively balancing statistical efficiency with clinical accountability.

The dynamics of student organization structure have significant implications for talent retention and student career development. This study aims to comparatively analyze the influence of centralization and decentralization structures in the Information Technology Study Program Student Association on the effectiveness of talent retention strategies and career development capacity. The research method uses a quantitative approach with a comparative design involving 120 respondents selected through purposive sampling technique with criteria of at least one period of organizational experience. Data collection instruments use structured Likert scale questionnaires that have been tested for validity and reliability with Cronbach's Alpha values above 0.80. Data analysis uses Structural Equation Modeling and independent sample t-test to compare both structural models. The results show that the decentralization structure has a strong significant effect on talent retention with a path coefficient of 0.628 compared to centralization of 0.312. Comparative analysis identifies substantial differentiation in all dimensions of career development with the decentralization structure consistently outperforming centralization, especially in the aspect of decision-making autonomy. The findings confirm that the distribution of authority in decentralization creates a learning ecosystem that facilitates diversification of leadership experiences and strengthens students' organizational commitment through participatory empowerment mechanisms.

A research about study of sandstone slope stability using the Bishop Simplified method in Uu Samarinda has been conducted. This study was conducted to analyze the rebound number values of sandstone slopes, evaluate their stability level, and calculate the safety factor using the Bishop method. The results showed that the rebound number values were 22.34 at point 1, 19.83 at point 2, and 18.07 at point 3. The Uniaxial Compressive Strength (UCS) values at the observation points were 1.90 MPa, 1.62 MPa, and 2.21 MPa, respectively. Geological Strength Index (GSI) evaluation indicated a rating of 80–85, demonstrating intact/massive rock structure, fresh and unweathered rock surfaces, and very good rock quality. Based on the Bishop method analysis, the slope factor of safety in 6.525  with a probability of failure is 0.000%, indicating that the sandstone slope in Ulu Samarinda is highly stable even under external pressures such as heavy rainfall or minor earthquakes. This study provides crucial information on the mechanical characteristics and stability of sandstone slopes in ulu Samarinda, which can serve as a reference for technical planning, geotechnical risk mitigation, and the sustainable development of safe areas.

Abstract: The rapid diffusion of Generative Artificial Intelligence (AI) has intensified public debate regarding its benefits, risks, and societal implications. This study investigates public sentiment and thematic structures surrounding Generative AI by analyzing Twitter discourse as a representation of large-scale, real-time public perception. The research addresses two main problems: how public sentiment toward Generative AI is distributed and what dominant themes shape this perception. Accordingly, the objective is to map both emotional polarity and thematic narratives embedded in social media conversations. A computational mixed-methods approach was employed using a dataset of 12,470 tweets collected on 17 December 2024. Sentiment classification was conducted using a transformer-based DistilBERT model, while semantic representations were generated with Sentence-BERT. Topic modeling was performed using BERTopic, integrating HDBSCAN clustering and class-based TF-IDF to extract coherent and interpretable topics. Human-in-the-loop validation supported the interpretive robustness of topic labeling. The findings reveal that public sentiment toward Generative AI is predominantly positive (41.8%), particularly in relation to productivity enhancement, education, and creative applications. Neutral sentiment (31.4%) reflects informational discourse, while negative sentiment (26.8%) centers on ethical concerns, privacy risks, misinformation, and AI hallucinations. Seven dominant topics were identified, with clear topic–sentiment alignment showing optimism in utility-driven themes and skepticism in ethics- and risk-related discussions. In conclusion, public perception of Generative AI is dualistic—characterized by strong enthusiasm alongside persistent caution. These results provide empirical insights for AI governance, responsible innovation, and future research on socio-technical impacts of Generative AI. *    

Internal control is a crucial aspect for SMEs in maintaining the reliability of revenue recording and preventing the risk of misappropriation. This study aims to analyze the implementation of internal control in the revenue cycle at Exmo Tea Café and to evaluate its effectiveness through internal audit. The research uses a qualitative descriptive approach with data collection through interviews with management personnel directly involved in transactions and financial recording. The analysis is conducted using the COSO framework, which includes five main components: control environment, risk assessment, control activities, information and communication, and monitoring. The research results indicate that Exmo Tea Café has implemented several basic elements of internal control, such as recording transactions through a cashier application, daily cash reconciliation, and reporting to the owner. However, the effectiveness of these controls is still limited because the segregation of duties between receiving and recording is not optimal, risk assessment is reactive, documentation and report archiving are not systematic, and monitoring is conducted informally. In addition, there are no formal policies regarding operational standards (SOPs) or internal audit procedures that could serve as guidelines for continuous control implementation. These conditions have the potential to lead to risks of fraud, recording errors, and delays in financial reporting. Therefore, these findings underscore the need for a comprehensive enhancement of the internal control system, including strengthening the separation of duties, conducting preventive risk assessments, providing employee training related to financial governance, as well as implementing more formal monitoring and documentation. These improvements are expected to increase reporting accuracy, operational effectiveness, and minimize the potential for errors or fraud in the company's financial activities.  

This study aims to analyze the effectiveness of technical personnel management in handling main engine failures during extreme weather conditions at sea. The main focus of this study is to assess the extent to which technical competence, communication, coordination, and preparedness of technical personnel contribute to the effectiveness of damage management. The method used is a descriptive quantitative approach with data collection through a closed-ended questionnaire based on a Likert scale. A total of 100 respondents who are ship engineering officers currently studying at a maritime campus were sampled. The results of the analysis show that the four independent variables (technical competence, communication, coordination, and preparedness) simultaneously have a significant effect on the effectiveness of handling main engine failures. From the results of the multiple linear regression test, the coefficient of determination (R²) value of 0.897 indicates that 89.7% of the variation in damage management effectiveness can be explained by these four variables. This finding indicates that good technical personnel management plays a significant role in reducing the risk of engine system failure during extreme weather.

This study critically reassesses Islamic economic law in response to the accelerating digital transformation reshaping global financial ecosystems. Drawing on classical jurisprudential sources, international Sharia standards, and contemporary literature on Islamic fintech, the research employs a qualitative library-based methodology to evaluate how digital innovations—particularly AI, blockchain, digital assets, and Islamic fintech platforms—challenge existing regulatory structures across Muslim jurisdictions. The findings reveal substantial fragmentation in Sharia governance, inconsistencies in regulatory interpretation, and limited technical capacity, which collectively hinder the development of a cohesive global framework. The study argues that the integration of Maqasid al-Shariah offers a robust ethical and legal foundation for constructing a global Sharia-compliant regulatory architecture capable of addressing cybersecurity risks, algorithmic bias, consumer protection gaps, cross-border inconsistencies, and the complexities of emerging technologies. The analysis highlights the need for harmonized standards, AI ethics protocols, enhanced RegTech adoption, and dynamic regulatory sandboxes to balance innovation with Sharia compliance. Ultimately, the research proposes a forward-looking model that embeds Islamic ethical principles within contemporary digital governance, ensuring that Islamic finance remains resilient, transparent, and socially responsible in the digital age.

This study discusses the phenomenon of digital entrepreneurship in the era of global climate awareness, focusing on the integration of artificial intelligence (AI) ethics, sustainable technology, and green innovation. The main issues raised are the fragmentation of analysis between digital business ethics, green economic opportunities, and technological challenges such as greenwashing, high AI energy consumption, and the digital divide. The purpose of this study is to formulate an interdisciplinary framework that combines ethical, technological, and sustainability dimensions to strengthen the role of digital entrepreneurs in achieving low-carbon development. The methods used include critical literature analysis, bibliometrics of 200 publications (2018-2025) using VOSviewer, and fuzzy logic-based simulations using the UNESCO AI ethics framework (2021) and the sustainable business model of Bocken et al. (2014). The results show four main research clusters: AI for Sustainable Innovation, Ethical Digital Business, Blockchain for Green Supply Chain, and Circular Digital Economy. The application of AI ethics increases the efficiency of green business decisions by up to 20%, consumer trust by 17%, and MSME participation by 14%. The synthesis of findings confirms that AI ethics acts as a conceptual mediator that strengthens the link between technological innovation and sustainability. In conclusion, ethical digital entrepreneurship has great potential as a driving force for Indonesia's green economy, but it requires digital ethics audit policies and the adoption of low-carbon technologies to address ethical and environmental risks in the AI era.

The rapid advancement of digital technology has significantly transformed the economic landscape, particularly in payment systems that are shifting from conventional cash transactions to the use of Electronic Money (E-Money). E-Money has become increasingly popular due to the convenience it offers, allowing users to conduct transactions anytime and anywhere without the need to carry physical cash. As this payment innovation continues to expand, it is essential to examine whether its mechanisms comply with Islamic principles, given that the use of E-Money is closely related to the values of muamalah in Islam. This study aims to identify the underlying contractual structure (akad) governing Mandiri E-Money transactions and to assess its conformity with sharia principles. It further analyzes the potential presence of gharar, riba, or maisir within the top-up and transaction processes, as well as the sharia mitigation mechanisms that may be applied. The research employs a normative approach based on classical and contemporary Islamic legal theory, supported by observational analysis of Mandiri E-Money practices. Data were analyzed qualitatively using a descriptive method and maqashid al-shariah reasoning. The findings indicate that the use of Mandiri E-Money does not involve elements of riba, gharar, or maisir, and therefore does not deviate from sharia principles. These potential risks were examined through fiqh legal maxims and DSN-MUI fatwas to ensure comprehensive sharia compliance.

This study aims to analyze the effect of profitability, leverage, and liquidity on firm value in food and beverage sub-sector companies listed on the Indonesia Stock Exchange (IDX) for the 2020–2024 period. Profitability is measured by Return On Assets (ROA), leverage by Debt to Equity Ratio (DER), and liquidity by Current Ratio (CR), while firm value is proxied by Price to Book Value (PBV). The study employs a quantitative approach using multiple linear regression analysis. The sample consists of 25 companies selected through purposive sampling, with a total of 125 secondary data observations obtained from annual financial statements. The results indicate that, partially, profitability, financial risk, and liquidity have a positive and significant effect on firm value. Simultaneously, the three independent variables also significantly affect firm value, with an adjusted R² of 43.4%, meaning that 56.6% of the variation in firm value is explained by other factors outside the model. These findings support agency theory and signaling theory, which suggest that strong financial performance, optimal debt management, and adequate liquidity provide positive signals to investors, thereby enhancing firm value.

This study aims to evaluate the effects of profitability, leverage, liquidity, and cash-flow shocks on the financial distress of companies in the hotel, restaurant, and tourism subsector listed on the Indonesia Stock Exchange during the period 2021 to 2024. The research approach employed is quantitative, using logistic regression analysis. The data analyzed are secondary data obtained from the annual financial statements of the respective companies. The results of the study indicate that, simultaneously, the four independent variables significantly influence financial distress. However, based on partial testing, each variable, namely Return on Assets (ROA), Debt to Equity Ratio (DER), Current Ratio (CR), and cash flow shock, does not show a significant relationship with financial distress. These findings imply that the risk of financial distress in this industry cannot be explained solely through a single financial indicator; instead, a more holistic approach is required. This study provides essential contributions to both management and investors in assessing companies' financial condition and formulating appropriate strategic decisions.

The revision of the State-Owned Enterprises (SOE) Law fundamentally changes the concept of SOE losses by emphasizing that losses incurred in SOE operations constitute corporate losses, not state financial losses. This change has a direct impact on the construction of directors' accountability, which has often been associated with corruption when companies experience losses. This study aims to analyze the provisions of SOE directors' responsibilities based on Good Corporate Governance (GCG) principles within the new regulatory framework, as well as to examine the application of sanctions against directors who violate these principles and cause corporate losses. The study uses normative legal methods with statutory, conceptual, and case-based approaches. The analysis was conducted by examining the provisions of the Limited Liability Company Law, the revised SOE Law, related implementing regulations, and several important decisions, such as those concerning Jiwasraya, Asabri, Garuda Indonesia, and Pertamina-TPPI. The results show that the principles of GCG, fiduciary duty, and the Business Judgment Rule are the primary instruments in assessing directors' actions. Civil and administrative sanctions are the first line of defense for assessing directors' accountability, while criminal sanctions can only be imposed if there is an element of abuse of authority, conflict of interest, or other fraudulent acts. This research emphasizes the need for a clear distinction between business risks and unlawful acts to prevent directors from being criminalized for business decisions made in good faith and in accordance with good corporate governance principles. These findings are expected to serve as a reference in formulating state-owned enterprise policies and promoting more proportionate law enforcement against directors.

This article examines the evolving discourse on granting legal personhood to Artificial Intelligence (AI) by analyzing jurisprudential foundations, global regulatory frameworks, and emerging challenges in liability attribution. As AI systems acquire higher autonomy, opacity, and decision-making independence, traditional human-centered legal structures struggle to assign responsibility for AI-generated harms. Through a qualitative methodological approach, involving library research and content analysis, this study evaluates whether limited or functional legal personhood can serve as a viable solution to accountability gaps created by advanced AI systems. The discussion explores key themes including AI autonomy, black-box decision processes, digital identities in virtual environments, metaverse avatars, and the boundaries of existing tort and contract law. Comparative insights from the European Union, the United States, and India highlight significant divergences in regulatory approaches, particularly regarding “electronic personhood,” strict liability models, and AI-specific safeguards. Findings indicate that while full personhood is premature, a hybrid framework—combining functional personhood, risk-based regulation, and AI-focused accountability mechanisms—could enhance legal clarity, promote responsible innovation, and strengthen public trust. This study contributes to the ongoing global effort to conceptualize AI legal personhood within modern socio-digital ecosystems.

This study examines the emerging paradigm of federated intelligence architectures as a secure, privacy-preserving, and scalable foundation for data-driven innovation across AI, IoT, and cloud ecosystems. With billions of interconnected devices generating massive heterogeneous data, traditional centralized machine-learning models face critical limitations, including privacy risks, regulatory constraints, latency, and single points of failure. Through a qualitative content-analysis approach, this paper synthesizes contemporary research on federated learning, blockchain integration, zero-trust governance, and edge intelligence to formulate a comprehensive understanding of distributed AI infrastructures. The findings highlight that federated learning enables collaborative model training without exposing raw data, significantly enhancing privacy, security, and compliance. Moreover, combining blockchain with federated learning strengthens auditability, model integrity, and trust, while zero-trust principles provide continuous verification and adaptive security enforcement across devices. Edge-AI integration further reduces latency and bandwidth consumption, enabling real-time analytics in resource-constrained IoT environments. Collectively, these elements contribute to the formation of cognitive ecosystems capable of autonomous, interoperable, and context-aware operations. The study underscores the transformative potential of federated intelligence while identifying critical gaps that inform future research trajectories.

Cooperatives play a significant role in Indonesia’s economic system because they aim to enhance member welfare through collective ownership and cooperative principles. To maintain accountability, cooperatives are required to prepare financial reports following the Financial Accounting Standards for Entities Without Public Accountability (SAK ETAP). This study examines how SAK ETAP is applied in the financial reporting practices of Koperasi Simpan Pinjam (KSP) Mandiri Sejahtera, Comal Branch. Using a qualitative descriptive method with a case study approach, data were gathered through interviews and an analysis of the 2022 financial statements. The results indicate that although the cooperative has implemented several elements of SAK ETAP, full compliance has not been achieved due to limited human resources and the absence of an integrated reporting system. Nevertheless, the preparation of PPAP reports reflects prudence in managing credit risk and highlights the need for digital systems and improved accounting skills to strengthen transparent and accountable financial management.

The rolling process is a material forming method that is greatly influenced by the roll rotation speed parameter. Inappropriate rotation variations can cause various defects such as waves, tears, thickness irregularities, and ovalization. This review aims to compare the effect of variations in the roll machine rotation speed on the quality of rolling results on various types of materials, including Al6061-O aluminum plate, rubber sheets, and hollow and pipe-shaped materials. The method used is a literature study by collecting and comparing data on roll speed, defects that appear, deformation results, and process efficiency from several relevant journals. The results of the analysis show that high rotation speeds increase productivity but also increase the risk of defects because the material does not have time to adapt to deformation. Conversely, low rotation speeds produce more stable shapes and minimize defects, but are less efficient for mass production. Each material has a different optimal rotation range, including 23–36 rpm for Al6061-O plate, 45–72 rpm for rubber sheet formation, 24.4 rpm for hollow and pipe rolling, and around 21 rpm for corrugated plate. Overall, this study confirms that rotational speed control is a critical factor in achieving a balance between deformation quality and machining efficiency.

This study aims to analyze the level of flood hazard in Baturaja Barat District, Ogan Komering Ulu Regency, by applying a spatial approach using Geographic Information Systems (GIS) through the overlay method. The analysis was carried out by integrating six main physical parameters, namely rainfall, land use, elevation, slope, river density, and soil type. Each parameter was assigned a score and weight according to its influence on flood potential and subsequently processed spatially to produce a flood hazard map. The results indicate that the Baturaja Barat District area is classified into three hazard levels: low (3.25%), moderate (70.64%), and high (25.84%). Areas with high hazard levels are predominantly characterized by densely populated settlements situated at low elevations and in close proximity to river networks, particularly in Tanjung Karang, Air Gading, Talang Jawa, Karang Agung, and surrounding villages. These findings highlight that land-use changes, topographic conditions, and the distribution of river networks play significant roles in increasing flood risk. The results of this study are expected to serve as a foundation for spatial planning, the strengthening of disaster mitigation policies, and the enhancement of community preparedness against flood hazards in the region.

This study aims to analyze inventory control of catfish seeds at ABC Company by comparing the conventional inventory method represented by the Economic Order Quantity (EOQ) approach and the Just In Time (JIT) system in order to improve cost efficiency. This research employed a descriptive quantitative approach using a case study design. Data were collected through direct observation, semi-structured interviews with company management, and documentation of inventory and cost records for the 2024 operational period. The analysis method involved calculating optimal order quantities, ordering frequency, delivery frequency, and total inventory costs using EOQ and JIT formulas, followed by a comparative cost efficiency analysis. The results show that the conventional method resulted in a total inventory cost of Rp 75,050,000 per year with high ordering frequency. In contrast, the implementation of the JIT system reduced inventory costs to Rp 18,762,500 per year, achieving a cost efficiency of 72%. These findings indicate that the JIT system is more cost-efficient than the conventional method; however, its implementation requires careful consideration of supplier capacity, logistics readiness, and biological risks associated with live inventory. This study contributes empirical evidence on the applicability of JIT in the aquaculture sector, which has different characteristics from manufacturing industries.