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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.

The development of digital technology has led to an increase in the use of short video-based entertainment applications, including the Melolo application. However, the free version still has various complaints, such as inconsistent subtitles, unintuitive navigation, force close glitches, and unstable advertisements, so user satisfaction analysis is needed. This study aims to measure the level of satisfaction of users of the free version of the Melolo application using the End User Computing Satisfaction (EUCS) method, which covers five variables, namely content, accuracy, format, ease of use, and timeliness. Data was collected through an online questionnaire of 385 Melolo app users in Jambi City and analyzed using Structural Equation Modeling (SEM) with the help of SmartPLS 4. The results showed an R-Square value of 0.546, indicating that the model was able to explain 54.6% of the changes in user satisfaction levels. The variables of content and timeliness were found to have a significant effect on user satisfaction, while accuracy, format, and ease of use had no significant effect. These results indicate that content quality and system timeliness are the main factors in increasing user satisfaction. Therefore, Melolo app developers are advised to maintain content quality and improve system performance and stability to optimize the user experience.

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.

This study focuses on analyzing the welfare index in the ASEAN region (covering six major countries) by comparing two perspectives: objective welfare (Human Development Index/HDI) and subjective welfare (World Happiness Index). Using a balanced panel dataset from 2015–2023, the research applies different econometric approaches for each model, namely the Random Effect Model (REM) for HDI analysis and the Common Effect Model (CEM) for happiness analysis. Empirical findings indicate a striking welfare paradox across the six sample countries. In the objective dimension (HDI), economic stability (GDP) and governance free from corruption (CPI) are proven to be the main positive and significant drivers, while government expenditure (GovExp) shows no meaningful impact, suggesting budget inefficiency. Conversely, in the subjective welfare model, the Easterlin Paradox emerges, as GDP and the corruption index have no significant effect on the happiness index. The happiness levels in these six countries tend to be more influenced by government expenditure. This study concludes that strong economic fundamentals and clean governance free from corruption are essential to building a high quality of human life, whereas citizens’ life satisfaction is more determined by the direct presence of the state through public spending.

This study discusses the progress of science in Andalusia during the rule of the Umayyad Daulah II, which served as an essential bridge between the Islamic world and the West. The purpose of this research is to examine the factors that stimulated scientific advancement, the fields of knowledge that developed, and the intellectual values inherited for modern life. This study employs a qualitative approach with a library research method by analyzing various historical sources, academic journals, and scholarly works related to Islamic civilization in Andalusia. The results show that scientific progress in Andalusia under the Umayyad Daulah II emerged from political stability, social tolerance, religious enthusiasm, and government support for education and research. Knowledge flourished in diverse disciplines such as medicine, astronomy, mathematics, philosophy, art, and architecture. Andalusia became a leading center for education and translation, transmitting Islamic knowledge to Europe and contributing greatly to the rise of the Renaissance. In conclusion, the intellectual achievements of Andalusia demonstrate that the harmony between faith, justice, and openness produces a just and progressive civilization. These scientific and moral values remain relevant for guiding the development of a modern civilization grounded in ethics and humanity.

Over the past 20 years, developments in digital technology have led to the emergence of financial innovation in the form of cryptocurrencies, with Bitcoin being the main pioneer. Bitcoin is a decentralized, blockchain-based electronic payment system that is not controlled by a single financial institution. Although its presence facilitates quick and straightforward cross-border transactions, it also raises ethical and legal issues, especially when taking into account Islamic law, which strongly emphasizes justice, certainty, and the welfare of society. This paper aims to investigate the usage of Bitcoin in modern economic transactions from the standpoint of Islamic and international law. This study investigates Islamic legal sources, the views of Islamic scholars, fatwas (religious decrees), and international laws and regulations pertaining to cryptocurrency assets using a qualitative, normative-empirical methodology. The results show that, although opinions among scholars differ, the usage of Bitcoin is subject to ijtihadiyah (Islamic ijtihad) in Islamic law. Some reject it because of its great volatility and speculative potential, while others allow it as long as it provides advantages and does not include riba, gharar, or maysir (the risks associated with gambling). In terms of international law, Bitcoin is typically seen as a digital asset that has to be closely watched in order to preserve economic stability and deter financial crime. Therefore, balanced legislation is required to guarantee that the usage of Bitcoin is in line with the principles of sharia maqasid and global economic fairness.

Amidst the current changing global conditions, it is important for a country to achieve the Sustainable Development Goals (SDGs) to face challenges in sustainable development, social inequality, and strengthen economic and environmental resilience. This study aims to analyze the influence of environmental performance and political stability on the SDG scores of ASEAN countries for the 2020-2024 period, moderated by economic growth. Researchers used a quantitative method, processed using multiple linear regression with SPSS. The regression process was conducted twice, before and after using moderating variables. The findings suggest that economic growth can alter the influence of environmental performance and political stability on SDG scores. Political stability has a positive impact on the SDGs after economic growth has moderated. While environmental performance has a negative impact after being moderated by economic growth. Economic growth promotes political stability and sustainable growth. Conversely, with high growth, improvements in environmental performance are indicated to shift priorities from sustainability to exploitation.

This research offers an in-depth examination of three primary Sharia monetary tools—Sukuk, the Sharia Interbank Money Market (PUAS), and Sharia Repo—aimed at enhancing the resilience of Islamic financial systems in Indonesia. Through a descriptive review of existing literature, the paper details Sukuk as asset-supported securities for medium- to long-term funding, PUAS operations grounded in mudharabah and wakalah agreements for brief interbank dealings, and Sharia Repo via SBSN sell-and-buyback arrangements to streamline Sharia bank liquidity. Results indicate these tools work in tandem to handle surplus funds, curb inflation, and bolster Bank Indonesia's monetary framework absent any speculative practices. Policy recommendations emphasize advancing education efforts, regulatory innovations, and infrastructural upgrades to promote equitable expansion within Sharia finance.

Economic forecasting is becoming increasingly important year after year, especially during crises such as the pandemic of COVID-19 and the Russia-Ukraine war. Its development can be seen from the use of basic statistical models to the increasingly widespread use of machine learning technology. Economic forecasting plays an important role in helping to formulate policies and is also a reliable tool for researchers in dealing with uncertainty. Global crises, such as inflationary pressures due to the pandemic and supply chain disruptions from the Russia-Ukraine conflict, have prompted increased research in this field in an effort to anticipate economic shocks and emphasize the urgency of forecasting to prepare strategies for dealing with future uncertainty. This literature review uses the Scopus database with 2561 publications from 2020 to 2025, analyzed using R Studio with a bibliometrix approach (specifically biblioshiny) and VOSviewer to map relevant thematic connections. This analysis shows that economic forecasting is greatly influenced by market uncertainty and geopolitical factors, and at the same time influences public policy formulation and financial stability. Research contributions from Indonesia are still limited, with only 40 documents, thus emphasizing the need to strengthen economic forecasting studies in Indonesia to support monetary policy and national financial stability.

This study aims to analyze the influence of macroeconomic variables in the form of the dollar exchange rate, inflation, and Gross Domestic Product (GDP) on the Composite Stock Price Index (JCI) in Indonesia for the period 2010–2024. The research method used is a quantitative approach with multiple linear regression analysis using time series data obtained from Bank Indonesia, the Central Statistics Agency (BPS), and the Indonesia Stock Exchange (IDX). The data analysis technique was carried out through classical assumption tests and hypothesis testing to determine the relationship between variables. The results of the study show that partially GDP has a significant effect on the JCI, while inflation and the dollar exchange rate tend not to have a significant effect. However, simultaneously these three variables have a significant influence on the JCI. These findings show that macroeconomic stability is very important in maintaining the performance of the capital market in Indonesia and can be a reference for investors in making investment decisions. In addition, the results of the study confirm that national economic growth is the main indicator that market participants pay attention to in assessing investment prospects. Therefore, the government needs to maintain economic stability through effective and sustainable fiscal and monetary policies.

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.

Queueing systems play an important role in evaluating service performance, especially in small-scale businesses such as barbershops, where fluctuating customer arrival patterns and limited service capacity often lead to long waiting times. This study aims to analyze the performance of barbershop services using the M/M/1 queueing model and an analytical approach based on experimentally tested arrival (λ) and service (μ) rates. The model was selected because it represents a single-server system with Poisson arrivals and exponentially distributed service times, closely matching real barbershop operational characteristics. Using assumed realistic parameters, the analysis shows that when λ = 12 customers per hour and μ = 6 customers per hour, the system becomes unstable with a utilization rate (ρ) exceeding 1, indicating continuous queue growth. Further simulations with increased service rates demonstrate significant improvements: at μ = 15, the system achieves ρ = 0.8 with an average waiting time of 16 minutes, while at μ = 13, the system remains stable but experiences a long waiting time of approximately 55 minutes. These findings emphasize that barbershop performance is highly sensitive to service speed and that even small increases in μ can produce substantial improvements in queue stability and customer waiting times. The study concludes that barbershops must ensure adequate service capacity—either through optimizing service duration, improving worker efficiency, or adding servers—to maintain service quality and enhance customer satisfaction.

Traffic congestion is a persistent challenge in urban areas in Indonesia, where increasing vehicle density creates the need for intelligent traffic monitoring systems. This study aims to develop a real-time vehicle parking system using the YOLOv8 object detection model to provide efficient traffic analysis from live CCTV broadcasts and recorded videos. This study uses a quantitative experimental approach with the implementation of the YOLOv8m model using the Ultralytics library in Python, tested on data collected from CCTV cameras A TCS Dishub Medan and additional footage from mobile devices. Vehicles are detected and counted in two directions up (Up) and down (Down) using virtual detection lines on the video frame. The system performance is evaluated by automatic detection counting with manually recorded ground truth data. The results show that on live CCTV broadcasts, the YOLOv8m model achieves an average precision of 98.96%, a recall of 96.59%, and an F1 score of 97.74% for upstream traffic, while for downstream traffic it achieves 100% precision, 95.64% recall, and an F1 score of 97.730/0. On the other hand, on high-quality recorded videos, all performance metrics achieve 100%, indicating perfect detection accuracy. These findings confirm the effectiveness of YOLOv8 in real-time traffic monitoring, but also indicate that video quality and stream stability affect detection performance. In conclusion, the developed system shows strong potential to support smart city traffic management solutions. Future research should focus on performance optimization under low-resolution live streaming conditions to improve accuracy in practical applications.  

This study examines the factors that cause fraud in financial reporting. The study analyzed 195 data points from 39 financial institutions listed on the Indonesia Stock Exchange (IDX) during the period 2019 to 2023 using a purposive sampling technique. The research applied multiple linear regression analysis to analyze the impact of governance independence and performance variables on the likelihood of fraudulent financial reporting. The independent variables include financial targets assessed by profitability (return on assets [ROA]), financial stability measured by changes in assets, external pressure measured by the debt-to-equity ratio (DER), and the proportion of independent commissioners as a measure of good corporate governance. The study proves that financial targets affect fraudulent financial reporting, while financial stability, external pressure, and independent commissioners do not influence fraudulent financial reporting. The findings of this study provide valuable insights for regulators, investors, and management to enhance oversight and reduce the risk of fraud in the banking sector.

The increasing demand for interactive web applications has encouraged the adoption of server-driven approaches such as Livewire as an alternative to building Single Page Applications (SPAs) without complex client-side JavaScript. However, the performance implications of this approach compared to conventional methods remain insufficiently explored. This study presents an empirical comparison between Laravel Blade with AJAX and Livewire in an academic attendance system scenario. Performance evaluation was conducted using k6 on the same web server, complemented by manual browser-based testing to observe actual communication patterns. The results indicate that Livewire exhibits approximately 2.7× higher average response time and up to 6× greater bandwidth consumption than Laravel Blade, primarily due to its snapshot mechanism and state synchronization process. Conversely, Livewire demonstrates better stability, reflected by lower maximum response times and a 0% error rate. These findings highlight a clear trade-off between resource efficiency and development convenience, where Livewire favors stability and developer productivity, while Laravel Blade provides superior efficiency in terms of latency and bandwidth usage.

Soil contamination by hydrocarbons, pesticides, heavy metals, and complex pollutants is rapidly increasing and degrading essential ecosystem functions. Physical or chemical treatments offer faster results, yet they are often costly, energy-intensive, and risk disrupting soil biological integrity without fully eliminating pollution sources. Microorganism-based bioremediation provides a more sustainable alternative by utilizing microbial metabolism to degrade or immobilize pollutants into less toxic and less mobile forms. This article presents a structured literature review on the roles and applications of microorganisms for bioremediation of contaminated soils, covering comparisons between single isolates and microbial consortia, dominant biological mechanisms, and ecological challenges in field application. A Systematic Literature Review approach was applied, using narrative synthesis and thematic clustering of national and international journals published between 2020 and 2025. The review indicates that single microbial isolates are commonly selected for specific pollutant targets, whereas microbial consortia are preferred for mixed or persistent contaminants due to metabolic synergy that enhances microbial adaptability and stepwise pollutant breakdown in highly polluted soils. Adaptive mechanisms such as EPS production and biofilm formation contribute to microbial resilience under stress and help retain contaminants within the soil matrix. Key challenges identified include inoculum stability under extreme conditions and limited microbial access to pollutants trapped in micro-soil pores. The findings highlight that microbial selection strategies must be tailored to pollutant characteristics and soil environmental conditions, while also emphasizing the potential of biofilm-based systems and organic carriers to support broader field implementation of microbial bioremediation.

This study aims to analyze the application of the franchising system on business performance in the beverage franchise business through a case study of Mitra Minuman Siap Saji. The approach used is qualitative with a case study design, with data collection techniques in the form of in-depth interviews, operational observations, and supporting documentation. The results show that the implementation of standardized Standard Operating Procedures (SOPs), franchisor support in the form of training, raw material supplies, and periodic monitoring contribute significantly to improving business stability, product quality consistency, and customer satisfaction. However, there are limitations in flexibility and several communication obstacles that have the potential to affect the effectiveness of the partnership. The relatively strict contract structure also impacts partners' perceptions of local innovation space, although it is generally still viewed as providing business security and business model clarity. These findings emphasize that a balance between franchisor control and partner autonomy, accompanied by open communication and fair contract design, is a key factor in creating sustainable business performance in a franchising system.

The production sector at both the district and city levels is crucial for fostering structural change and boosting economic growth in specific areas. Still, many regions struggle with issues such as linking supply chains, readiness for technology, quality of labor, and efficient policies. This research intends to examine the strategies of the manufacturing sector at the district and city levels to enhance regional competitiveness and promote sustainable economic growth. The study utilizes a descriptive qualitative method based on a review of literature from academic journals, policy papers, and official statistics related to manufacturing progress. Results reveal that several important factors strongly affect regional manufacturing growth. These include the connection of local supply chains, industry strategies focused on the market, the implementation of digital and smart manufacturing methods, innovation encouraged by educational institutions and organizations, and the influence of local governments in developing an effective industrial policy atmosphere. Furthermore, creating designated industrial areas and managing operations efficiently significantly helps attract investments and boost the manufacturing output of regions. The research concludes that a cohesive and tailored manufacturing strategy for each region is vital for improving local productivity, generating jobs, and enhancing economic stability at both district and city scales.

A wall follower robot is a type of autonomous robot that is designed to move by following a wall at a certain distance. This research aims to design and build a Wall follower robot equipped with a Fuzzy-PID control system to improve navigation performance. The robot uses five HC-SR04 ultrasonic sensors to detect the distance to the wall and the surrounding obstacles. The data from the sensor is then processed by a Fuzzy-PID algorithm that combines the advantages of conventional PID control with fuzzy logic, resulting in a more adaptive response to environmental conditions. The test results showed that the robot with Fuzzy-PID control was able to maintain the stability of the distance to the wall more consistently compared to the pure PID control. In addition, the system exhibits better adaptability to complex environmental conditions, such as sharp turns, uneven wall surfaces, and the presence of resistance variations. The application of Fuzzy-PID control has been shown to improve the stability, response speed, and accuracy of the robot's navigation. These findings are expected to contribute to the development of robotic navigation systems for a wide range of practical applications, including automated cleaning robots, environmental exploration, and industrial systems that require reliable autonomous mobility.

Diseases in primary health services exhibit complex spatial-temporal dynamics due to urbanization and population mobility. Conventional surveillance approaches are difficult to capture these patterns adaptively. Machine learning (ML) based on spatio-temporal modeling offers a solution with the ability to detect disease clusters automatically and with high precision. Research Objectives: This research aims to develop a machine learning model to detect disease hotspots from primary service data in Indonesia, with a focus on improving prediction accuracy, interpretability, and relevance of health policies. Methodology: The primary service dataset for 2024 (5,343 entries) was analyzed using three ML models Gradient Boosting Machine (GBM), Temporal Random Forest (TRF), and Multi-EigenSpot with spatial (village) and temporal (week, month) features. Performance evaluation includes predictive (AUC, F1-score) and spatial (Moran's I, Spatio-Temporal Correlation Index) metrics. Results: The results showed that Multi-EigenSpot achieved the best performance (AUC=0.91; F1=0.86), with the detection of dominant hotspots in Sungai Asam and Beringin Villages. Moran's I value of 0.63 indicates a strong spatial autocorrelation, while STCI=0.57 indicates moderate temporal stability. Conclusions: ML-based spatio-temporal models are effective in identifying hidden disease patterns and have the potential to be integrated into national digital surveillance systems. This approach supports precision public health by providing a scientific basis for real-time location- and time-based intervention policies.