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Corporate financial performance is an important factor in maintaining business sustainability amid increasingly intense competition. One of the commonly used indicators of financial performance is Return on Assets (ROA), which reflects a company’s ability to generate profits through the efficient use of its assets. Corporate profitability is influenced by various internal factors, including capital structure and liquidity. This study aims to analyze the effect of Debt to Equity Ratio (DER) and Current Ratio (CR) on Return on Assets (ROA). This research employs a quantitative approach using secondary data obtained from corporate financial statements. The research sample consists of 36 observations selected through purposive sampling. Data analysis techniques include descriptive statistical analysis and multiple linear regression analysis using SPSS software. The results show that, partially, the Debt to Equity Ratio does not have a significant effect on Return on Assets, while the Current Ratio has a positive and significant effect on Return on Assets. Simultaneously, Debt to Equity Ratio and Current Ratio have a significant effect on Return on Assets, with Current Ratio being the most dominant variable. The findings indicate that effective liquidity management plays a crucial role in improving corporate profitability. The implications of this study are expected to provide useful insights for corporate management in making financial decisions, particularly related to liquidity management and capital structure.

Women micro, small, and medium enterprises (MSMEs) play a strategic role in strengthening both family and community economies; however, they experience to obtain fundamental skill in financial management. One of the main problems is the mixing of personal and business finances, which turn into challenges for entrepreneurs to monitor business performance and to address access financing. This community service activity aims to improve insight of women entrepreneurs in Medan regarding the importance of separating personal and business finances through simple, practice-based training. The activity was conducted in 7 November 2025 in Medan and was attended by 150 women MSME participants from a local entrepreneurship community. The training was held for one day using interactive lectures, group discussions, and hands-on cash recording exercises. The results showed a significant improvement in participants’ understanding of financial separation concepts and their ability to record business transactions. A total of 85% of participants were able to create a simple cash record format and expressed a commitment to applying separate financial practices. This activity contributed to improving the financial literacy of women micro-entrepreneurs and strengthening their managerial capacity toward sustainable business growth.

The era of digitalization demands that the world of education adapt quickly to technological developments. One of the basic skills that elementary school students need to have is the ability to operate a computer, particularly in typing and using word processing applications. Community service research was conducted at the Kemala Bhayangkari Foundation 1 Medan with the aim of improving students' digital literacy thru typing training using Microsoft Word and the interactive media 10fastfingers.com. The training method is conducted using a workshop model, which consists of two main topics. The first material is a basic introduction to typing using the ASDF;LKJ pattern and the use of important keyboard keys. The second material is an introduction to basic Microsoft Word features, such as changing font type and size, using Bold, Italic, Underline, changing text color, and highlighting. Additionally, students are trained to use 10fastfingers.com to interactively improve their typing speed and accuracy. The research instruments are pretest-posttest, observation, and student response questionnaires. The research results show a significant improvement in typing skills. Average speed increased from 8–10 words per minute to 20–25 words per minute, with over 85% accuracy. Student responses were also very positive; they felt more confident, more motivated, and able to use Microsoft Word more effectively. This training program has proven effective in improving digital literacy among elementary school students while also fostering an interest in technology-based learning.

This research examines the coordination efforts of the Karanganyar District Disaster Management Agency (BPBD) in managing disasters, identifying obstacles, and exploring efforts to strengthen these coordination efforts. Utilizing a qualitative approach with a case study method, the study aims to investigate the mechanisms of cross-sector coordination, communication patterns, and division of labor within BPBD. Data was gathered through in-depth interviews, observations, and document studies, and analyzed using an interactive model. The findings indicate that BPBD's coordination is relatively effective, demonstrated through unified action, a multi-channel communication system, and a clear division of labor. However, challenges remain, including the complexity of cross-sectoral coordination, differences in capacity and authority, and the pressure of emergency situations. The study concludes that improving coordination requires enhancing staff discipline, developing human resource capacity, and strengthening an integrated work system. These efforts are vital for improving the overall effectiveness of disaster management at the local level, ensuring a more efficient and comprehensive response to disasters in the future.

This study explores the transformative role of big data-driven Decision Support Systems (DSS) in global digital enterprises, particularly focusing on their impact on operational efficiency and corporate governance. By leveraging big data analytics, DSS offer organizations the tools to process vast amounts of real-time data, enabling executives to make more informed decisions that optimize resources, improve productivity, and reduce operational costs. The research highlights the integration of predictive analytics, machine learning, and real-time data processing within DSS, which allows businesses to gain strategic insights and anticipate market trends. Furthermore, the study emphasizes the significant role of DSS in enhancing corporate governance, improving transparency, accountability, and compliance with regulations. These systems foster better decision-making processes, which contribute to building trust among stakeholders and ensuring long-term organizational success. However, the study also identifies several challenges in implementing big data-driven DSS, including data management complexities, technological integration difficulties, and the need for skilled personnel. Despite these challenges, the findings demonstrate that big data-driven DSS are pivotal in driving competitive advantage, operational optimization, and governance improvements. The research concludes with actionable recommendations for executives to adopt and implement big data-driven DSS, emphasizing the importance of continuous support, training, and system integration. The study also suggests future research directions, including exploring the integration of emerging technologies like AI and IoT into DSS and assessing their long-term impact on sustainability and corporate governance.

Explainable Artificial Intelligence (XAI) has become a critical area of research within artificial intelligence, focusing on improving the transparency and interpretability of machine learning (ML) models, often referred to as "black-box" models. The need for XAI techniques arises from the inherent complexity of ML models, which can make their decision-making processes difficult for users to understand. This study investigates various XAI techniques, including LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), to assess their impact on model interpretability without significantly compromising predictive performance. A comparative experimental design was used, applying these XAI methods to different ML models, including deep neural networks and ensemble methods, within large-scale enterprise data analytics systems. The results indicate that XAI methods significantly enhance model transparency and decision traceability, allowing users to understand the influence of individual features on predictions. While a slight reduction in predictive accuracy was observed, especially with simpler models, the trade-off between interpretability and performance was deemed acceptable, particularly in fields requiring transparency, such as healthcare, finance, and autonomous systems. The use of XAI in enterprise data systems has practical implications for fostering trust and enabling informed decision-making among stakeholders. Furthermore, the study discusses the challenges and limitations of applying XAI techniques, such as complexity, scalability, and model-specific limitations. Future research is suggested to focus on developing more scalable and efficient XAI methods, enhancing their applicability across various model types, and addressing the challenges of real-time applications. This will be crucial in ensuring the widespread adoption of XAI in critical domains, promoting the ethical use of AI while maintaining predictive accuracy.

The integration of Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) systems has emerged as a critical strategy for modern digital enterprises aiming to enhance customer experience and operational efficiency. This study examines the impact of CRM-ERP integration on customer satisfaction, personalized service, and organizational responsiveness. By adopting a mixed-methods approach, this research combines quantitative customer data analysis and qualitative managerial interviews to assess the benefits and challenges of CRM-ERP integration. Key findings highlight significant improvements in customer experience, with increased satisfaction and personalized interactions facilitated by a unified view of customer data. Operational efficiencies were also realized through streamlined processes, better alignment of departments, and enhanced decision-making based on real-time, data-driven insights. Despite these positive outcomes, challenges such as system integration complexities, data fragmentation, and resistance to change were identified, which hindered the speed of integration and full utilization of the systems. This study demonstrates that CRM-ERP integration provides a competitive advantage by improving both customer service and business agility, particularly in industries undergoing digital transformation. For digital enterprises, integrating these systems is crucial for maintaining a seamless customer experience across various touchpoints and achieving greater operational effectiveness. The paper concludes by suggesting future research on the long-term impact of CRM-ERP integration on customer loyalty, business growth, and the potential role of emerging technologies like AI and blockchain in further enhancing these systems.

The increasing integration of Cyber physical Systems (CPS) into industrial environments has highlighted the need for secure, scalable, and efficient cryptographic key management systems. Traditional centralized key management protocols are often limited by vulnerabilities such as single points of failure, scalability issues, and significant overhead. Blockchain technology presents a promising solution to these challenges by leveraging decentralization, immutability, and transparency to enhance security and efficiency in CPS. This study investigates the use of blockchain based cryptographic key management systems, focusing on smart contracts for automated key distribution and rotation. Experimental results demonstrate that blockchain based systems significantly improve system integrity, auditability, and resilience, offering enhanced protection against cyber-attacks and reducing the risks associated with centralized systems. Blockchain’s decentralized architecture eliminates the need for a central authority, making it more resistant to tampering and operational failures. Additionally, smart contracts automate the key management process, improving efficiency while maintaining a high level of security. The study also evaluates the impact of blockchain on communication performance, finding that it reduces latency and overhead by automating processes and eliminating the need for centralized control. Despite these advantages, challenges such as scalability, latency, and integration with legacy systems remain. The study concludes by suggesting future research directions, including the development of lightweight blockchain protocols tailored for industrial applications and the integration of blockchain with emerging technologies like Artificial Intelligence (AI) to further enhance key management in CPS. Blockchain based solutions have the potential to transform the security landscape of industrial environments, offering greater robustness, reliability, and trust.

The increasing sophistication of cyber threats has rendered traditional cybersecurity models insufficient in safeguarding enterprise networks. This study introduces a risk aware cybersecurity governance model that integrates real time threat intelligence with predictive anomaly detection to proactively mitigate potential threats. By leveraging advanced machine learning and AI techniques, the model enhances the ability to identify and address cyber threats before they can escalate into significant incidents. The model’s ability to predict anomalies, analyze real time threat intelligence feeds, and provide early warnings allows for faster response times and reduced risk exposure compared to traditional reactive models. Through simulations and real-world use cases, the proposed model demonstrated a 30% reduction in response time and a 25% decrease in overall risk exposure, showing its potential to improve security decision-making and resilience in dynamic threat environments. Unlike traditional models that rely on static rules and periodic policies, the proposed model uses predictive analytics to stay ahead of evolving threats, ensuring continuous monitoring and rapid adaptation. This proactive approach enhances organizational resilience, particularly in handling sophisticated cyber threats such as ransomware, malware, and phishing attacks. Despite its effectiveness, challenges such as data overload, scalability, and the need for interpretability in AI models remain. Future research will focus on refining predictive models, improving scalability for larger networks, and enhancing the explainability of machine learning models to foster greater trust in automated cybersecurity systems. This study contributes to the ongoing evolution of cybersecurity governance by demonstrating the value of integrating predictive and real time monitoring technologies for enhanced threat detection and mitigation.

This study aims to analyze the implementation of Good Corporate Governance (GCG) principles at PT Bank Mandiri (Persero) Tbk as one of the state-owned banks that plays a strategic role in the Indonesian banking system. The implementation of GCG is crucial in maintaining public trust, improving performance, and ensuring business sustainability in the banking sector. This research employs a qualitative method with a descriptive approach, focusing on secondary data analysis obtained from annual reports, corporate governance reports, sustainability reports, and official information published on the website of PT Bank Mandiri (Persero) Tbk. The results indicate that Bank Mandiri has consistently implemented the principles of transparency, accountability, responsibility, independency, and fairness in its corporate governance system. These principles are reflected in information disclosure practices, clear organizational structures, regulatory compliance, independent decision-making processes, and fair treatment of all stakeholders. Overall, the implementation of GCG at PT Bank Mandiri (Persero) Tbk contributes positively to strengthening internal control systems, enhancing public trust, and supporting the stability and sustainability of banking operations.

Distributed software systems face significant challenges related to data quality due to their complex, decentralized architecture. These systems often involve multiple nodes responsible for processing and storing data, making it difficult to maintain consistency and ensure accurate data across the entire network. In particular, issues like data inconsistency, latency, and data fragmentation are prevalent in distributed environments. To address these challenges, this study proposes an integrated data quality governance strategy that combines real time monitoring and automated anomaly detection using machine learning models. The proposed strategy aims to improve data consistency, enhance anomaly detection capabilities, and reduce the need for manual intervention, ultimately improving overall data governance in distributed systems. Real time monitoring ensures immediate identification of data issues as they occur, while machine learning models, such as autoencoders and Isolation Forests, automate the detection of anomalies based on high reconstruction errors and data isolation techniques. The study evaluates the proposed strategy through real-world distributed system scenarios, comparing its effectiveness to traditional approaches like periodic audits and manual validation. Results demonstrate that the integrated approach leads to faster anomaly detection, reduced data inconsistencies, and improved overall system performance. The use of advanced machine learning techniques and real time analytics significantly enhances the system's ability to maintain high data quality standards across multiple distributed nodes. This strategy has wide-ranging implications for industries that rely on distributed systems, such as finance, healthcare, and IoT, where data integrity is essential for operational success. Future research can focus on integrating more advanced machine learning techniques and optimizing the real time monitoring framework to handle larger and more complex systems.

In high-performance computing (HPC) environments, the need to balance memory efficiency and query performance is crucial for ensuring optimal system performance. Traditional data structures, such as B-trees and hash tables, often prioritize either memory usage or query speed, leading to suboptimal performance in memory-constrained systems. This paper proposes a hybrid data structure that combines the strengths of multiple traditional data structures to optimize both memory usage and query processing speed. The proposed hybrid structure integrates cache-conscious algorithms, dynamic memory allocation, and compression techniques for intermediate query results. The approach is evaluated through extensive benchmarking tests comparing it to standard data structures like B-trees and hash tables under various workloads. Results show that the hybrid data structure reduces memory overhead by up to 30% while maintaining query processing speeds up to 1.5 times faster than conventional methods. Furthermore, the hybrid structure demonstrates robust performance across different types of queries, including both point and range queries, ensuring versatility and efficiency. The findings indicate that this hybrid approach provides a promising solution for HPC systems, where both memory efficiency and query speed are essential. Future research can explore extending the hybrid structure to distributed systems and emerging technologies, further improving its scalability and adaptability to new computational paradigms.

This research focuses on the design and evaluation of a novel parallel graph optimization algorithm incorporating dynamic load balancing (DLB) to address inefficiencies in heterogeneous computing environments. Large-scale graph optimization problems, such as those in social networks, bioinformatics, and transportation systems, often suffer from computational imbalances when using traditional static load balancing approaches, leading to underutilized resources and prolonged execution times. The primary objective of this research is to develop an algorithm that can dynamically adjust workload distribution across processors, enhancing computational efficiency and scalability. The proposed method combines heuristic techniques, including region expansion and multilevel partitioning, with diffusive load balancing strategies to minimize inter-processor communication overhead. Experimental results demonstrate that the proposed algorithm reduces execution time by up to 40% compared to static methods, with optimized resource utilization and more balanced workload distribution. The scalability of the algorithm is also evident, as it adapts effectively to increasing problem sizes and processor counts. These findings suggest that dynamic load balancing is crucial for improving parallel graph optimization in real-world applications. Future work will focus on further enhancing the algorithm’s responsiveness to rapidly changing workloads and expanding its applicability to additional domains.

The use of Information Systems and Information Technology (IS/IT) is a crucial factor in improving the efficiency and accuracy of retail sector operations. PT XYZ, a retail-level staple goods provider, has implemented IS/IT to support sales recording, inventory management, security, and operational reporting. This study aims to assess the effectiveness, efficiency, security, and compliance of the information system used. The audit was conducted through observation, interviews, document review, and system testing, referring to the COBIT framework and the ISO/IEC 27001 standard. The audit results indicated that the system supported core operational functions, but weaknesses remained, including insufficient system documentation, limited access controls, and suboptimal data security and backup mechanisms. Furthermore, reliance on manual procedures has the potential to lead to errors and slow down service delivery. Therefore, system improvements, strengthening security controls, developing standard operating procedures, and enhancing the competency of human resources and IT infrastructure are needed. This audit is expected to support improved service quality and secure, effective, and sustainable IT governance at PT XYZ.

Adolescents are an age group that is vulnerable to reproductive health problems, including the risk of Sexually Transmitted Infections (STIs) and risky sexual behaviors, due to limited knowledge and psychosocial maturity. Schools have a strategic role in providing reproductive health education as a promotive and preventive effort. This counseling activity aimed to determine adolescents’ knowledge regarding the prevention of Sexually Transmitted Infections (STIs) among students at Parulian 3 School, Medan.This study employed a one-group pretest–posttest approach. The participants were all seventh-grade students who met the inclusion criteria. The intervention consisted of reproductive health education delivered through lectures, discussions, and visual media. Data were collected using questionnaires measuring knowledge and attitudes before and after the counseling session, and then analyzed descriptively by comparing pretest and posttest results. The results showed an increase in students’ knowledge and attitudes after receiving reproductive health education. The differences in scores before and after the intervention indicate that the counseling provided was effective in improving students’ understanding and fostering positive attitudes toward the prevention of STIs and risky sexual behaviors. Reproductive health education in the school setting is an effective effort to enhance adolescents’ knowledge and attitudes regarding reproductive health. Therefore, reproductive health education activities should be implemented continuously and integrated into school programs.

Reading ability is a basic skill that plays a crucial role in the success of students' learning process. However, various studies indicate that the reading ability of junior high school students remains low. This study aims to analyze the reading ability of students at the UPT SMP Negeri 2 Mappakasunggu using Alfred Schutz's social phenomenology perspective. The research approach used was qualitative with descriptive methods. Data collection techniques included in-depth interviews, observation, and documentation of students and teachers. The results indicate that students' low reading ability is not solely caused by cognitive factors but is also influenced by subjective meanings formed through students' social experiences. The lack of a literacy culture in the family and school environment results in reading not being perceived as an important or enjoyable activity. Furthermore, the dominant use of gadgets for entertainment creates habits that reduce students' interest and concentration in reading texts. From Alfred Schutz's social phenomenology perspective, these conditions are related to students' lifeworlds and stock of knowledge, which shape their perspectives and actions toward reading. This study concludes that improving students' reading ability requires a comprehensive approach, taking into account experiences, social interactions, and the formation of meaning in reading in students' daily lives.

This study aims to analyze the effect of Current Ratio (CR), Debt to Asset Ratio (DAR), and Inventory Turnover on Earning Per Share (EPS). This research employs a quantitative method with a causal-comparative ex-post facto approach. The population includes food and beverage companies listed on the Indonesia Stock Exchange (IDX) for the 2020-2023 period. The sampling technique used purposive sampling, resulting in 10 companies with a total of 40 observations. Data analysis was conducted using multiple linear regression utilizing SPSS version 25 software. The results indicate that partially, CR, DAR, and Inventory Turnover each have a significant effect on EPS. Simultaneously, these three independent variables significantly affect EPS with a determination coefficient of 83.7%. The implications of this study emphasize the importance of liquidity management, solvency, and inventory efficiency in improving corporate share profitability.

With financial inclusion acting as a moderator, the goal of this study is to examine how financial technology use and financial literacy impact microbusiness performance. Microbusinesses in Bandar Lampung City's food and beverage industry serve as the research subjects. This study employs a quantitative approach, using questionnaires to collect primary data from microenterprises. Purposeful sampling was used to choose 203 microbusinesses that met the research criteria. SPSS was utilized to analyze the data using multiple linear regression and Moderated Regression Analysis (MRA). The study's conclusions imply that the use of financial technology has an effect on microenterprises' performance. Additionally, it has been shown that financial literacy affects microenterprise performance. The findings show that key components in increasing microbusiness performance are the use of financial technology and the entrepreneur's capacity to supervise and make financial decisions. However, the test results indicate that financial inclusion cannot boost the impact of financial technology and financial expertise on microbusiness performance. This implies that the influence of financial technology and financial knowledge on business success is not necessarily enhanced by having access to financial services. It also shows that a key factor in increasing the success of microbusinesses is the characteristics of the entrepreneur. The research's objectives are to assist important stakeholders in creating plans for microenterprise growth, as well as to assist microenterprise actors in improving their financial literacy and utilizing financial technology to its fullest.

Individuals with mental disabilities commonly experience difficulties in self-awareness and emotion regulation, which negatively affect social functioning and psychosocial well-being. These conditions were also observed among beneficiaries in the assisted community, who demonstrated limitations in recognizing emotions, expressing feelings, and engaging in social interactions. This community service program aimed to strengthen self-awareness and emotional regulation skills among individuals with mental disabilities through a psychosocial approach. The program employed activity-based psychoeducation using participatory and supportive methods, including emotional recognition training, self-reflection, positive emotional stimulation, and structured group support. The results indicated improvements in beneficiaries’ ability to identify and express basic emotions, increased self-confidence, enhanced emotional stability, and more active social participation. Qualitative observations revealed behavioral changes from passive tendencies to more responsive and cooperative involvement in group activities. These findings suggest that activity-based psychosocial interventions are effective in improving psychosocial well-being among individuals with mental disabilities.

The high unemployment rate in Indonesia highlights the importance of enhancing work readiness among young people from an early educational stage. Work readiness is influenced by several factors, including learning motivation, communication skills, and parental support. This study aims to examine the relationship between learning motivation, communication skills, and parental support with work readiness among twelfth-grade students at SMAK Kasih Anugrah. This research employed a quantitative approach with a cross-sectional design. The population consisted of all 30 twelfth-grade students, and a saturated sampling technique was applied. Data were collected using structured questionnaires and analyzed through univariate and bivariate analysis using the Chi-Square test. The results indicate a significant relationship between learning motivation and work readiness (p-value = 0.021), communication skills and work readiness (p-value = 0.014), and parental support and work readiness (p-value = 0.005). These findings suggest that learning motivation, communication skills, and parental support play a crucial role in improving students’ readiness to enter the workforce. The results of this study are expected to provide practical implications for schools and parents in strengthening students’ preparation for the world of work