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Climate risk finance has emerged as an increasingly important field of research along with the growing urgency to address climate change and its impacts on the global financial system. Climate change poses real risks to the stability of the international economy and financial systems. Climate risk finance represents an approach that encompasses various financial instruments in supporting climate change mitigation and adaptation. Although the term climate risk finance has not been widely used explicitly as a single keyword, the concept that integrates climate risk and financing is reflected in related keywords such as climate risk, climate finance, and climate change. This study employs a bibliometric analysis method using the Scopus database, supported by analytical tools such as VOSviewer and R Studio, to explore the development of research on climate risk finance. The study identifies publication patterns, international collaborations, and emerging themes within the related literature. The findings show that the publication rate on climate risk finance is relatively moderate each year, but has experienced growth in the last decade. The evolving understanding in this field is expected to strengthen the resilience of financial systems and support sustainable strategies to address long-term climate risks.

This study examines the impact of Pentagon Fraud factors on FSF, with WBS as a moderation variable, focusing on Indonesian State-Owned Enterprises (SOEs) from 2021 to 2024. The Pentagon’s Fraud Theory encompasses five key elements: pressure, opportunity, rationalization, competence, and arrogance, each of which is represented by financial stability, ineffective monitoring, the quality of auditors, the experience of directors, and CEO pictures. This study aims to determine how these factors affect financial reporting that contains fraud, and whether WBS can strengthen or weaken the relationship between the two. Using a quantitative approach with secondary data from the annual reports of 104 SOEs, thisi study applied panel data regression method. FSF was measured using the Beneish M-Score, while the effect of moderation was tested through moderated regression analysis. The results of this study are expected to provide deeper insights into the dynamics of fraud in the public sector and highlight the importance of WBS as a governance tool in reducing the risk of fraud. The study contributes to the previous literature by integrating a comprehensive fraud framework and testing it with moderation mechanisms, while also focusing on specific institutional contexts (SOEs), which have not been explicity explored in previous studies.

Artificial Intelligence (AI) has become a significant driver of innovation in the banking sector, especially in the context of post-pandemic digital transformation. AI is widely utilized in areas such as fraud detection, credit evaluation, risk management, and customer interaction, attracting considerable interest from both academics and industry professionals. This research explores the recent advancements in AI within the banking industry, focusing on studies published between 2020 and 2025. A bibliometric approach is employed, using data from the Scopus database and bibliometric tools like VOSviewer and R Studio to visualize keyword networks and track emerging trends. The study aims to identify influential authors, journals, and countries contributing to AI research in banking. By analyzing these developments, the research highlights the contributions of AI to improving operational efficiency, data security, and financial inclusion, particularly in the Indonesian context. This work offers valuable insights into the ongoing integration of AI in the banking sector and its potential to shape future financial services, emphasizing its relevance to both global and regional markets.

ABSTRACT Background: Tourist areas are regions with a high potential risk of disasters, so tourism actors are required to have basic knowledge of Basic Life Support as part of disaster preparedness. The Tourism Health Nursing approach emphasizes the importance of the health capacity of the tourist community to prevent and respond to emergency conditions. Objective: This study aims to understand the effect of BLS training on improving the knowledge of tourism actors in managing disaster preparedness based on Tourism Health Nursing. Method: The research design was a quasi-experiment with a one group pretest–posttest design. The sample consisted of 30 tourism actors, selected using purposive sampling. Analysis was conducted using the paired t-test. Result: The average score after receiving Basic Life Support (BLS) training showed that the average BLS knowledge score of respondents before training was 58.9, with a minimum-maximum range of 60-73. with an average score difference of 6.4. Conclusion: It can be concluded that there is an effect of Basic Life Support (BLS) training on public knowledge. BLS training has a significant impact on increasing the knowledge of tourism actors in disaster preparedness.

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.

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.

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 develops an interpretable machine learning model to predict the settlement status of Hajj fees in Jambi Province, Indonesia. Utilizing the XGBoost algorithm on a dataset of 4,332 prospective pilgrims from 2025, the research addresses the critical challenge of class imbalance where only 28.5% of samples are labeled "Unsettled". The baseline XGBoost model achieved a ROC-AUC of 0.7778, with a recall of 0.3482 for the minority class. SHAP (SHapley Additive exPlanations) analysis was employed to interpret model predictions, revealing that financial features specifically NILAI_VA (Virtual Account Value), JML_SETORAN (Deposit Amount), and JML_PELUNASAN (Settlement Amount) are the most significant factors influencing repayment risk, with negative SHAP values indicating increased default probability. The findings demonstrate that an interpretable XGBoost framework can provide both predictive accuracy and actionable insights for policymakers, enabling targeted interventions such as flexible payment schemes and enhanced financial monitoring for high-risk pilgrims..

Global concern on climate change has encouraged policymakers and central banks to adopt green financial instruments such as green bonds within sustainable monetary frameworks. Research on the integration of green bonds and monetary policy has grown rapidly in recent years, reflecting wider trends in sustainable finance, climate risk management, and central bank policy innovation. Green bonds play an important role in supporting low-carbon transitions and can influence monetary operations through asset purchases and collateral policies. This study applies a bibliometric analysis of publications on green bonds and monetary policy indexed in Scopus from 2021 to 2025. Using bibliometric methods with VOSviewer and R Studio, the analysis maps dominant themes, co-authorship networks, and the evolution of green monetary studies. The results show strong growth in research output, high levels of international collaboration, and a concentration on sustainable development and green finance. However, fewer studies address climate policy uncertainty and geopolitical risk, even though these factors are highly relevant to financial stability and the effectiveness of monetary policy. Future research in these underexplored areas could provide stronger scientific foundations for building more adaptive and resilient monetary systems in both developed and emerging economies.

The development of information technology has encouraged various organizations, including cooperatives, to digitize their service systems. The Credit Cooperative (Kopdit) CU Mera Ndi Ate is one of the cooperatives that still uses a manual system in managing savings and loans, which causes the service process to be slow, inaccurate, and has a high risk of recording errors. This study aims to design and build a web-based savings and loans system that can be used by members of Kopdit CU Mera Ndi Ate. This system allows members to conduct transactions online, view transaction history, and monitor savings or loan balances. The research methods used are observation, interviews, and literature studies. The system development process uses a waterfall model with stages of analysis, design, implementation, and testing. The result of this study is a prototype of a web-based savings and loans information system that has main features such as member registration, transaction recording, financial data management, and automatic financial report generation. With the implementation of this system, it is hoped that the cooperative can improve work efficiency, speed up services, and provide easy access to information to all members.

ABSTRACT Background: Low birth weight babies are the age group that has the highest risk of health problems, so low birth weight babies need optimal care. Kangaroo method care is one of the interventions to increase weight in lowbirth weight babies. Objective: The purpose of this study is to examine the difference in the weight of low birth weight babies before and after being given kangaroo care. Method: The research design used was preexperimental pretest posttest without control group on 16 respondents from 50 pupulation with tehnique sampling purposive sampling. Normality test using the Shapiro Wilk test obtained normally distributed data. Result: The results of the study showed that the average weight of lowbirth weight babies before kangaroo care was 1843.12 grams and the average weight of lowbirth weight babies after kangaroo care was 1995 grams. The results of data analysis using the paired t test with a significance of p < 0.05 found p = 0.000, which means that there is a significant difference between the weight of lowbirth weight babies before and after being given kangaroo care. Conclusion: Providing kangaroo care can increase the weight of LBW babies.

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.

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.  

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. *    

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.

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 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.