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In the contemporary digital marketplace, consumers increasingly face diverse product choices and brand communications. Understanding the mechanisms through which product quality and brand perception influence customer loyalty remains critical for competitive advantage. The mediating role of customer trust in this relationship has received limited empirical attention within Indonesian market contexts. This research analyzes the direct and indirect effects of product quality and brand perception on customer loyalty, with customer trust as a mediating variable, using Partial Least Squares Structural Equation Modeling (PLS-SEM) methodology on 103 respondents. A quantitative cross-sectional survey design was employed, collecting data via Likert-scale questionnaires (1-5) with 15 measurement items across four latent constructs: Product Quality (5 items), Brand Perception (4 items), Customer Trust (3 items), and Customer Loyalty (3 items). Data analysis utilized PLS-SEM via SmartPLS 3.0, including assessment of measurement model validity (outer model), structural relationships (inner model), and mediation effects through bootstrapping (5000 iterations). The outer model demonstrated adequate validity with 12 of 15 indicators loading above 0.7, and all constructs meeting composite reliability (CR > 0.7) and average variance extracted (AVE > 0.5) criteria. The inner model revealed that product quality significantly influenced customer trust (β = 0.624, p < 0.001), while brand perception showed no significant direct effect (β = 0.045, p = 0.767). Customer trust strongly predicted loyalty (β = 0.650, p < 0.001). Product quality demonstrated a significant indirect effect on loyalty through trust (β = 0.405, p < 0.001), indicating full mediation. The model explained 43.5% of trust variance and 42.2% of loyalty variance. Product quality emerged as the dominant antecedent of customer trust and loyalty, while brand perception did not significantly contribute. Trust served as the critical mechanism translating quality into loyalty. These findings suggest that companies should prioritize quality assurance and consistent delivery over brand marketing campaigns for sustainable loyalty development. The research contributes to mediation theory in consumer behavior and provides actionable strategic guidance for practitioners in emerging markets.

This study aims to analyze the effect of welfare inequality on poverty levels on the island of Sumatra. Welfare inequality is measured using the Gini Index, while poverty levels are measured by the percentage of the poor population at the provincial level. This study uses a quantitative method with a panel data approach covering ten provinces on the island of Sumatra during the period 2020–2024. The analytical techniques used include panel data regression with fixed and random effects models, as well as classical assumption testing to ensure model validity. The results show that welfare inequality has a positive and significant effect on poverty levels, meaning that increasing inequality in income distribution tends to increase the number of poor people. This finding indicates that uneven economic growth can worsen the welfare of the community, especially low-income groups. Therefore, more inclusive and sustainable development policies are needed, particularly in increasing equitable access to education, health services, and productive employment opportunities to reduce inequality and poverty levels on the island of Sumatra.

This research aims to develop a digital-based governance model for village finance to support the realization of a Smart and Green Village. The study analyzes how digital transformation in village finance management can increase transparency, accountability, and efficiency, while promoting sustainability-oriented budgeting and environmental practices. A Systematic Literature Review (SLR) was used to identify, evaluate, and synthesize scientific publications from 2022 to 2025, accessed through reputable databases such as ScienceDirect, Springer, Wiley, Taylor & Francis, SAGE, ACM, and IEEE. The review focused on topics including digital governance in villages, digital public finance, smart village development, green budgeting, environmental sustainability, and rural digital transformation. Findings indicate that digital-based village finance governance enhances administrative efficiency, strengthens budget transparency through real-time monitoring, minimizes financial deviation risks, and boosts public participation in fiscal accountability. Integrating digital systems with green budgeting features enables the prioritization of sustainable programs, such as renewable energy, waste management, climate change mitigation, and green infrastructure development. The study suggests a comprehensive digital-based governance model that includes e-budgeting, e-accounting, digital payment systems, public transparency dashboards, and environmental performance indicators to support the implementation of a Smart and Green Village. This research offers strategic insights for village governments, policymakers, and practitioners on the importance of adopting digital governance tools to improve financial management and strengthen sustainable development at the local level.

This research, entitled “Implementation of a Web-Based Student Tuition Payment Information System at SMA Swasta Manda Elu,” aims to improve the efficiency of recording and managing students’ tuition fee payments. Previously, the tuition payment process was handled manually, which often led to recording errors, delays in reporting, and difficulties in retrieving student payment data. To address these issues, a web-based information system was developed to assist the school administration in recording, reporting, and monitoring tuition payments more effectively and efficiently. The development method used in this study is the Model View Controller (MVC) approach, which separates the system’s view, logic, and database layers to simplify system management and maintenance. The final result of this research is a web-based tuition payment application that runs properly, provides convenience for school administrative staff in recording transactions, and enhances the accuracy and speed of the school’s financial administration process at SMA Swasta Manda Elu.

The global economic crisis has prompted developing countries, including the BRICS group (Brazil, Russia, India, China, and South Africa), to strengthen cooperation in creating a multipolar economic order as a form of resistance to Western domination. Mass media plays a crucial role in framing this dynamic. This study aims to examine how The Jakarta Post constructs the BRICS narrative through three main dimensions in Vincent Mosco's Political Economy of Communication theory: commodification, spatialization, and structuring. This study uses a descriptive qualitative approach, with data collection techniques in the form of documentation and analysis of news published in The Jakarta Post between June 18, 2024, and January 31, 2025. Data were categorized based on the type of information and main themes, then analyzed using Mosco's theoretical concepts. The results show that the commodification process occurs when geopolitical issues, such as Indonesia's opportunity to join BRICS, are transformed into media commodities appealing to elite readers and strategic economic actors. This practice also involves the contribution of experts, but often without equitable compensation. Spatialization is reflected in the real-time distribution of digital content that transcends geographical and temporal boundaries, strengthening the penetration of BRICS discourse into the international public sphere. Structuralization emerges in the narrative of building a new world order through BRICS agendas such as dedollarization, technological integration, and alternative payment systems, although it remains overshadowed by the dominance of Western financial institutions. Thus, media coverage not only represents geopolitical dynamics but also transforms strategic issues into information products with economic value, while revealing the tension between the aspirations of developing countries and established global hegemony.

Background: The rapid advancement of digital technologies in the Industry 4.0 era has transformed industrial mechanical systems into highly interconnected and data driven environments through the integration of sensors, the Internet of Things (IoT), data analytics, and cyber physical systems. This increasing complexity requires more adaptive and accurate monitoring and prediction methods than conventional simulation approaches, which often face limitations in capturing real time dynamic system behavior. Objective: This study aims to develop a predictive performance model for industrial mechanical systems by integrating Digital Twin technology with Physics Informed Machine Learning in order to improve monitoring accuracy and support predictive maintenance strategies. Methods: This research adopts a data driven modeling and simulation approach by developing a digital representation of an industrial mechanical system that is connected to real time sensor data. The prediction model is constructed using a Physics Informed Neural Network (PINN), which integrates operational data with physical principles governing system dynamics. The research process includes the development of a Digital Twin model, integration of sensor data, training of the PINN model, model validation using experimental data, and evaluation of prediction performance using statistical metrics. Results: The results indicate that the integration of Digital Twin technology and PINN significantly improves the prediction accuracy of industrial mechanical system performance compared with conventional simulation methods and purely data driven machine learning models. The proposed model is capable of representing system dynamics more consistently, accurately following sensor data patterns, and providing strong potential for supporting machine condition monitoring and predictive maintenance strategies in modern industrial environments.

This study investigates the impact of household consumption and investment on the open unemployment rate in West Kalimantan over both short and long terms. Using time series data from 1995–2024 and employing the Vector Error Correction Model (VECM) in EViews 12, results show all variables become stationary after first differencing. Johansen cointegration confirms a long-run equilibrium relationship. VECM findings reveal that investment significantly increases unemployment in the long run, while household consumption has no significant effect. In the short term, a significant error correction mechanism exists, indicating adjustment toward long-run equilibrium. Diagnostic tests confirm model validity through absence of autocorrelation and normally distributed residuals. These results highlight the need for more targeted policies to reduce unemployment.

This study aims to analyze the causal relationship between interest rates, exchange rates, and inflation in Indonesia during the period 1994–2023 using the Vector Error Correction Model (VECM) approach. The data used are monthly time series secondary data obtained from the World Bank. The Johansen cointegration test results indicate a long-term relationship among the three variables. However, the Granger causality test finds no significant short-term causal relationship. The VECM estimation reveals that inflation is the most responsive variable in correcting long-term disequilibrium, while the exchange rate plays a dominant role in influencing both inflation and interest rates. The Impulse Response Function and Variance Decomposition results indicate that these variables interact dynamically, especially in the medium to long term. These findings highlight the importance of exchange rate stabilization and enhancing the effectiveness of monetary policy to maintain macroeconomic stability in Indonesia.

This study aims to examine the effect of Return on Assets (ROA) and Return on Equity (ROE) on stock prices of agro-industrial companies listed on the Indonesia Stock Exchange (IDX) during the post-Covid-19 economic recovery period, specifically from 2021 to 2023. The research method used is a quantitative approach with a causal associative type. The data used are secondary data from annual financial statements and year-end stock prices. Data analysis was conducted using multiple linear regression with classical assumption tests to ensure model validity. The results indicate that ROA and ROE have both simultaneous and partial effects on stock prices. ROA demonstrates a stronger influence compared to ROE, suggesting that asset management efficiency is a key determinant of stock value in agro-industrial firms during the economic recovery period.

This research investigates the implementation of Random Forest algorithms combined with Synthetic Minority Over-sampling Technique (SMOTE) to predict elementary school dropout rates in Indonesia, supporting the Indonesia Emas 2045 vision. A significant gap was identified in previous studies, which, despite utilizing artificial intelligence for dropout interventions, had not integrated temporal dimensions into data analysis. A temporal data-based classification model was developed using Indonesian Ministry of Education data from 2021-2023, incorporating lag features, delta calculations, and rolling statistics. Two models were implemented: one with SMOTE achieving 99% accuracy with perfect recall for high-risk regions, while the non-SMOTE model reached 100% accuracy. Temporal features were identified as crucial predictors, reflecting external fluctuations and annual changes impacting dropout decisions. This approach enables educational institutions to allocate resources more efficiently by prioritizing operational assistance for high-risk schools. The model's capacity to identify high-risk regions with 100% recall represents a strategic investment in strengthening Indonesia's human resource sustainability. To address the limitations of provincial aggregate data, expansion to include individual-level variables and model validation at district or school scales is recommended for future research.

The low reasoning ability of students in processing information has an impact on learning outcomes and creative thinking skills. So it is necessary to develop a valid, practical, and effective learning model to improve students' creative thinking skills. The research method used was the Borg and Gall model which was modified into 8 stages. This research was tested on fourth grade students of SD Negeri Kalisalam 2. Data collection used questionnaires for student expert validators, and teachers, non-test assessments to measure students' creative thinking skills, and interviews. This research evaluates the validity, practicality, and effectiveness of the developed learning model. The results of expert validation showed that the model and learning materials were very valid with an average score of 90.16%. In detail, the learning model validator gave a score of 93.66%, while the material expert gave a score of 86.66%. The practicality assessment before and after the trial was 84% and 80% respectively, both in the “practical” category. The effectiveness of the model was measured through student questionnaires with an average of 79% (“effective”) and non-test assessment of creative thinking skills, where 16 out of 23 students showed improvement after the application of the learning model. This guidebook/prototype of inquiry-based learning model development with scaffolding techniques is considered effective for improving students' creative thinking skills, besides that this model can also help teachers in encouraging innovation in classroom learning to be more interesting.

The stability of the banking sector is crucial in maintaining a country’s financial system and economic sustainability. This study analyzes the impact of Net Interest Margin (NIM), Non-Performing Loans (NPL), and Capital Adequacy Ratio (CAR) on banking stability in Indonesia. The inconsistency of previous research findings indicates a research gap that requires further exploration. This study employs a quantitative approach using secondary data from financial reports of conventional banks listed on the Indonesia Stock Exchange (IDX) from 2019 to 2023. The sample was selected using purposive sampling, resulting in 39 banks with 195 observations. Data analysis was conducted using multiple linear regression with classical assumption tests, including normality, heteroscedasticity, multicollinearity, and autocorrelation, to ensure model validity. The results show that NIM positively but not significantly affects banking stability, while NPL has a negative and significant effect. CAR also significantly influences banking stability. Enhancing banking intermediation effectiveness through NIM and CAR can strengthen financial stability, whereas increasing credit risk, reflected in NPL, can weaken stability. This study provides insights for regulators and banking management in designing more effective policies to maintain banking sector stability in Indonesia.