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This study aims to analyze the effect of the Current Ratio (CR), Debt-to-Equity Ratio (DER), and Return on Equity (ROE) on the stock prices of healthcare companies classified as sharia-compliant on the Indonesia Stock Exchange (IDX) for the 2020–2024 period. The background of the study is motivated by notable stock price fluctuations among sharia healthcare issuers, such as the sharp decline in PT Kimia Farma Tbk and price dynamics of other issuers including KLBF, MIKA, PEHA, and SIDO. The analysis uses a quantitative approach applying Partial Least Squares – Structural Equation Modeling (PLS-SEM) implemented in WarpPLS 8.0. The results indicate that CR does not have a significant effect on stock price (p = 0.174), while DER has a negative but not statistically significant effect (p = 0.484). In contrast, ROE has a positive and significant effect on stock price (p < 0.001), making ROE the dominant factor influencing investor interest. Simultaneously, the three independent variables explain only 20.2% of stock price variation, while the remaining 79.8% is influenced by factors outside the research model. The Tenenhaus goodness of fit (GOF) value of 0.450 suggests the research model has good overall quality despite the limited explanatory power of the tested financial variables.

The purpose of this study is to investigate the connection between the probability of financial statement fraud and the components of the Fraud Hexagon: pressure, opportunity, rationalization, capability, arrogance, and collusion. Additionally, we examine how Environmental, Social, and Governance (ESG) Disclosure functions as a moderator. Banks listed on the Indonesia Stock Exchange (IDX) between 2021 and 2024 are the subject of this study. We make use of secondary data gathered from business sustainability and annual reports. Purposive sampling was used to choose the bank sample depending on the completeness of the data. We use the Partial Least Squares (PLS) method of Structural Equation Modeling (SEM), which works well for evaluating models with complex variables, for the analysis. The results of this study are expected to provide insights into how each element of the Fraud Hexagon contributes to financial statement fraud and how ESG Disclosure can mitigate these risks.

This study investigates the impact of auditor competence and red flag awareness on fraud detection ability, examining the moderating role of professional skepticism. As fraudulent financial reporting poses a critical threat to the integrity of financial disclosures and stakeholder trust, understanding the key factors influencing an auditor's detection capabilities is essential. This study employed a quantitative approach, gathering data from auditors at Public Accounting Firms (KAP) in Bali Province via a four-point Likert scale questionnaire. The data were subsequently analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4 software. The findings indicate that both auditor competence and an awareness of red flags significantly and positively enhance fraud detection capabilities. Conversely, professional skepticism, when analyzed for its direct influence, demonstrated a significant negative effect on this ability. Furthermore, skepticism exhibited a complex moderating role: it significantly weakened the positive relationship between competence and fraud detection, while not significantly moderating the link between red flags and detection ability. These results provide crucial theoretical contributions by revealing the nuanced and sometimes counter-intuitive role of professional skepticism. Practically, they inform policy for audit firms and regulatory bodies, suggesting that while fostering competence and red flag awareness is vital, the application of skepticism requires a more sophisticated and refined approach to truly enhance audit quality and overall fraud detection effectiveness.

This study aims to analyze and test the mediating role of job satisfaction in the relationship between career development and job stress on employee loyalty at BPR Agung Sejahtera. This study used a quantitative methodology to collect primary data from 90 employees of BPR Agung Sejahtera through questionnaires. Partial Least Squares (PLS) with Structural Equation Modeling (SEM) was used to test the relationship between latent variables. Data testing included validity, reliability, bootstrapping , Adjusted R-Square, Path Coefficient, and Specific Indirect Effects . The results showed that career development had a positive and significant effect on job satisfaction. Conversely, job stress was found to have a negative and significant effect on job satisfaction. Career development also had a positive and significant effect on employee loyalty. Job stress had a negative and significant effect on employee loyalty. Job satisfaction had a positive and significant effect on employee loyalty. However, the mediating role of job satisfaction in the relationship between career development and job stress on employee loyalty proved to be insignificant. This finding indicates that employee loyalty is more influenced by good career development and low levels of job stress than the mediating role of job satisfaction. Overall, these findings conclude that managing career development and work stress through job satisfaction is crucial for increasing employee loyalty. It is hoped that these research findings will help BPR Agung Sejahtera's management better manage its human resources to achieve the company's goals.

Employee work engagement is the extent to which employees are able and willing to commit to an organization, and the results of such commitment can be observed in their performance and tenure. This study aims to examine the effect of transformational leadership style and job satisfaction on work engagement through perceived organizational support at PT. Viva Vegas Ventury Denpasar. The study was conducted at PT. Viva Vegas Ventury Denpasar with a total sample of 45 employees. The sampling technique used was saturated sampling. Data were collected through observation, interviews, questionnaires, and documentation. The analytical method employed was a variance-based structural equation modeling, commonly known as Partial Least Squares (PLS). The results of the analysis show that transformational leadership style, job satisfaction, and perceived organizational support have a positive and significant effect on work engagement. Transformational leadership style and job satisfaction also have a positive and significant effect on perceived organizational support. Moreover, perceived organizational support mediates the influence of transformational leadership style and job satisfaction on work engagement.

This study aims to analyze the influence of digital financial literacy and digital financial inclusion on the economic sustainability of rural micro-entrepreneurs in Deli Serdang Regency, Indonesia. The rapid development of digital financial services offers new opportunities for rural MSMEs to improve transaction efficiency and expand market access. However, the optimal utilization of these services remains limited due to uneven levels of literacy and digital access. This research employs an explanatory quantitative approach using Structural Equation Modeling–Partial Least Squares (SEM-PLS), based on survey data collected from rural business actors who have used at least one digital financial service. The findings indicate a very strong predictive accuracy of the model, with an R-Square value of 0.945 for economic sustainability. Digital financial literacy has a positive and significant effect on digital financial inclusion (β = 0.273; p = 0.001) and on economic sustainability (β = 0.235; p = 0.000). Digital financial inclusion also positively and significantly influences economic sustainability (β = 0.398; p = 0.000). These results demonstrate that improved digital literacy and wider use of services such as e-wallets, QRIS, and mobile banking enhance business resilience and expand economic opportunities for rural MSMEs. Overall, the study underscores the importance of strengthening digital capacities and expanding access to digital financial services as key strategies for advancing inclusive and sustainable rural economic development.

This study aims to analyze the influence of Corporate Image and Service Quality through Customer Satisfaction on Customer Loyalty at Semen Indonesia distributors in Gresik using the Structural Equation Modeling (SEM) approach. The study population consisted of 102 customers, all of whom were sampled. The results indicate that Corporate Image and Service Quality have a significant influence on Customer Satisfaction. Both variables were also shown to have a significant influence on Customer Loyalty. However, unlike several previous research findings, Customer Satisfaction did not have a significant influence on Customer Loyalty. This finding indicates that in the context of the cement distribution business, customer loyalty is not formed solely from satisfaction, but is more influenced by other factors such as consistent service quality, a strong corporate image, competitive pricing, and long-term business relationships. This study emphasizes the importance of strengthening image and improving service quality as key strategies for maintaining customer loyalty.

This study aims to examine and analyze the effects of product quality and distribution channel on repurchase intention, with customer satisfaction and customer trust serving as mediating variables. The research employed a quantitative method with a causal-explanatory approach. The study population consisted of customers at the Weber building materials manufacturing company in East Java, with a sample of 275 respondents selected using purposive sampling. Data were collected via questionnaires and assessed using a Likert scale. The data analysis was conducted using the Structural Equation Modeling (SEM) technique with Partial Least Squares (PLS) software. The findings of the study reveal the following: 1) Product quality has a significant positive effect on customer satisfaction; 2) Product quality has a significant positive effect on customer trust; 3) Product quality has a significant positive effect on repurchase intention; 4) Distribution channel has a significant positive effect on customer satisfaction; 5) Distribution channel has a significant positive effect on customer trust; 6) Distribution channel has a significant positive effect on repurchase intention; 7) Customer satisfaction has a significant positive effect on customer trust; 8) Customer satisfaction has a significant positive effect on repurchase intention; 9) Customer trust has a significant positive effect on repurchase intention.

This study aims to examine and analyze the effects of product quality and distribution channel on repurchase intention, with customer satisfaction and customer trust serving as mediating variables. The research employed a quantitative method with a causal-explanatory approach. The study population consisted of customers at the Weber building materials manufacturing company in East Java, with a sample of 275 respondents selected using purposive sampling. Data were collected via questionnaires and assessed using a Likert scale. The data analysis was conducted using the Structural Equation Modeling (SEM) technique with Partial Least Squares (PLS) software. The findings of the study reveal the following: 1) Product quality has a significant positive effect on customer satisfaction; 2) Product quality has a significant positive effect on customer trust; 3) Product quality has a significant positive effect on repurchase intention; 4) Distribution channel has a significant positive effect on customer satisfaction; 5) Distribution channel has a significant positive effect on customer trust; 6) Distribution channel has a significant positive effect on repurchase intention; 7) Customer satisfaction has a significant positive effect on customer trust; 8) Customer satisfaction has a significant positive effect on repurchase intention; 9) Customer trust has a significant positive effect on repurchase intention.

As the center for employee competency development, BSI Corporate University (BSU) plays a crucial role in strengthening BSI’s competitiveness within Indonesia’s rapidly growing Islamic banking industry. BSU’s performance from 2021 to 2023 experienced fluctuations, indicating the need for an evaluation of factors influencing employee performance. Previous studies have also shown inconsistent findings regarding the influence of Islamic Work Ethics and AKHLAK core values on performance, thereby creating a research gap. This study aims to examine the influence of Islamic Work Ethics and AKHLAK core values on employee performance at BSU. Data were collected through a quantitative survey of BSU employees, resulting in 60 valid responses, and analyzed using SmartPLS 4.1.1.2. The research employed a descriptive quantitative approach with Partial Least Squares Structural Equation Modeling (PLS-SEM) as the analytical technique. The findings indicate that Islamic Work Ethics and AKHLAK core values have a positive and significant influence on employee performance.

This study analyzes the impact of artificial intelligence on youth digital political engagement through a Structural Equation Modeling approach. The development of algorithmic technology has changed the way young people access, assess, and react to political information, requiring an empirical understanding of its mechanisms of influence. This study explores how digital literacy, trust in AI, and perceived usefulness shape online political participation. Data was obtained from an online questionnaire targeting individuals aged 17-30 who are active with AI, then analyzed using the SEM-PLS 4 method. The main findings reveal that digital literacy and trust in AI have a strong influence on perceived usefulness, which acts as a key mediator in encouraging such participation. This indicates that the impact of AI is not direct, but rather occurs through cognitive processes that guide young people in assessing the benefits of technology. The implications of this research emphasize the importance of strengthening digital literacy, algorithm transparency, and responsible AI implementation to strengthen inclusive youth political participation in the digital environment.

This study aims to analyze the influence of Innovative Work Behaviour, Upskilling, and Work Ethic on the Health Workers Performance with Intention to Stay as an intervening variable at RSUD Sumberrejo. The background of this research stems from challenges in improving service quality, high workload, and the need to strengthen competency and retention among health workers. The study employs a quantitative approach using a survey method through the distribution of questionnaires, with data analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) through SmartPLS version 4.0. The population consists of 216 employees, and the sampling technique used is non-probability sampling, resulting in 140 respondents. The findings reveal that Innovative Work Behaviour, Work Ethic, and Intention to Stay have a significant positive effect on the performance of health workers. In addition, Innovative Work Behaviour and Work Ethic significantly influence Intention to Stay. However, Upskilling shows a positive but non-significant effect on both Intention to Stay and Performance, indicating that skill enhancement requires managerial support and motivation to contribute effectively to employee performance. Intention to Stay serves as a mediating variable in several relationships among the constructs. Strengthening innovative behaviour, work ethic, and competency development, accompanied by appropriate retention strategies, is essential for improving the performance of health workers in regional hospitals. Future studies are recommended to develop the research model by incorporating additional variables that may have stronger effects on Intention to Stay and Performance.

This study aims to examine the effect of employee competence, behavioral control, and trust on employee performance at the Bojonegoro District Health Office with Organizational Citizenship Behavior (OCB) as an intervening variable. The background of this study is related to the importance of employee performance in achieving public service organizational goals, particularly in the health sector. This study uses an explanatory quantitative method with a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The research sample consisted of 100 employees at the Bojonegoro District Health Office. The results of the analysis show that the influence of employee competence on employee performance is not significant, but employee competence has a significant influence on OCB. Behavioral control has a significant effect on employee performance, but its effect on OCB is not significant. Trust has a significant effect on OCB, but its direct effect on employee performance does not. OCB is proven to have a significant effect on employee performance. Therefore, OCB plays an important role as a mediator in improving employee performance. This study provides a theoretical contribution by clarifying the relationship between variables in the context of public service-based government organizations. In practical terms, the results of this study are expected to form the basis for recommendations to improve the quality of human resources and develop managerial strategies at the Bojonegoro District Health Office in order to support the effectiveness and efficiency of public services.

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.

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.

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. *    

: The rapid expansion of cybersecurity standards and threat intelligence frameworks has led to significant semantic fragmentation among security terminologies, hindering effective information retrieval and interoperability across systems. Traditional keyword-based search approaches are inadequate for capturing the contextual meaning of security terms, particularly within formal frameworks such as NIST, MITRE ATT&CK, and CWE. This study addresses this challenge by proposing CyberBERT, a transformer-based semantic search framework designed to align cybersecurity terminologies through deep contextual representation and ontology-driven reasoning. Research Objectives: The primary objective of this research is to develop a semantic retrieval model capable of understanding conceptual relationships between security terms beyond lexical similarity. Methodology: The proposed methodology fine-tunes a BERT-based model on the NIST Glossary corpus using a combination of masked language modeling and triplet loss objectives to generate discriminative semantic embeddings. These embeddings are further aligned with cybersecurity ontologies, including MITRE ATT&CK and CWE, to enhance semantic consistency and explainability. Semantic retrieval is performed using cosine similarity within a 768-dimensional embedding space and evaluated using Mean Reciprocal Rank (MRR) and Precision@K metrics. Results: Experimental results demonstrate that CyberBERT achieves an MRR of 0.832, outperforming domain-adapted baselines such as SecureBERT and CyBERT. The integration of ontology alignment improves semantic accuracy by over 6%, while robustness evaluations confirm resilience against adversarial linguistic perturbations. Visualization using t-SNE reveals coherent semantic clustering aligned with the five core NIST Cybersecurity Framework functions. Conclusions: In conclusion, CyberBERT effectively bridges semantic gaps across cybersecurity terminologies by combining transformer-based contextual learning with ontological reasoning. The framework offers a robust, interpretable, and scalable solution for semantic search, supporting improved interoperability and knowledge discovery in cybersecurity operations and standards harmonization.

The development of science and technology has encouraged the utilization of graph theory in solving optimization problems, particularly in transportation systems and tourism route planning. Medan City, as a metropolitan area with dense road networks and widely dispersed tourist destinations, faces challenges in selecting efficient travel routes. This research aims to determine the optimal route between tourist destinations in Medan City using the Minimum Spanning Tree (MST) method with Prim’s Algorithm. The research was conducted using a weighted graph modeling approach, where each tourist destination is represented as a vertex and the distance between destinations is represented as an edge weight. Distance data and estimated travel time were obtained through digital mapping using Google Maps and then analyzed through iterations of Prim’s Algorithm to produce a minimum spanning tree without forming cycles. The results show that all 23 tourist destinations are successfully connected in a single MST structure with a minimum total distance of 68.97 km and a travel time of approximately 199 minutes or 3 hours and 19 minutes. This model is expected to serve as a reference for tourism planning and support urban transportation efficiency based on mathematical computation.

The intersection between digital transformation and the halal economy has created new pathways for sustainable global development. This study explores how the Halal Digital Ecosystem (HDE)—an integrated network of producers, certifiers, regulators, and consumers—contributes to sustainable growth by aligning digital innovation with Islamic ethical principles. Employing a mixed-method explanatory sequential design, the research combines quantitative analysis using Structural Equation Modeling–Partial Least Squares (SEM–PLS) with qualitative interviews involving policymakers, halal entrepreneurs, and certification authorities from Indonesia, Malaysia, and GCC countries. The findings reveal that digital infrastructure readiness and certification transparency significantly influence sustainable growth, while innovation capability acts as a mediating factor that strengthens the relationship between digitalization and sustainability outcomes. Qualitative insights highlight persistent challenges, including fragmented cross-border governance, limited digital literacy among halal SMEs, and the absence of harmonized certification standards. These barriers indicate that achieving sustainability requires not only technological advancement but also ethical and institutional alignment based on maqāṣid al-sharī‘ah. The study introduces the Halal Digital Ecosystem–Sustainability (HDE–S) Framework, providing both theoretical and practical contributions to the literature on sustainable digital economies. Policy implications emphasize the need for integrated halal digital governance, capacity building, and regional collaboration to enhance competitiveness, inclusivity, and ethical compliance in the global halal market.

The concept of supremum is fundamental in real analysis and plays a crucial role in the optimization of single-variable real functions. In practice, not all functions attain their supremum explicitly, which necessitates numerical approaches to evaluate their behavior computationally. This study aims to analyze the supremum of several one-dimensional real functions with different characteristics using a grid-search method implemented in Python. Four functions were examined: a parabolic function, a rational function with a sharp peak, a discontinuous piecewise function, and a function with a vertical asymptote. The analysis involved modeling the functions, discretizing the domain, performing numerical approximation of the supremum, verifying the results against analytical values, and using graphical visualization to observe the function behavior near the supremum. The findings indicate that the supremum of the parabolic, rational, and piecewise functions can be accurately identified, with results consistent with analytical expectations despite minor deviations caused by grid resolution limitations in the rational function. Meanwhile, the function with a vertical asymptote yields an unbounded supremum, which cannot be attained within the domain. These results demonstrate that Python provides stable and reliable numerical estimates of the supremum across various types of one-dimensional real functions, validating the effectiveness of computational methods in supporting conceptual understanding of supremum.