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The study aimed to find out the nature of the budget, know the modern techniques of preparing and implementing the budget, and the consequences of preparing budget based on the modern techniques on performance of government. The descriptive-analytical approach was used to meet the objectives and hypotheses of the research. The data was gathered through a questionnaire whereby 60 questionnaires where distributed to a sample population of accountants working in the Financial Affairs Department at the General Directorate of Education in Wasit Governorate (Iraq), and 57 questionnaires were collected to establish the views of the accountants regarding the effect of preparing and implementing budgets under the modern techniques on the government performance. This paper came up with a number of conclusions. Preparation of the general budget of the state is a technical and political process that involves and is involving different actors in the state. The budget preparation process is a technical process since it involves numerous key calculations that can be used to quantify the anticipated revenues, the cost of activities likely to be obtained in the year and estimation of the planned expenditures. The process of preparing the budget through the traditional ways (line-item budgeting) does not contribute to the optimal planning and budget allocation of the state and, therefore, the fulfillment of the goals laid out by the countries. This paper advises on the need to focus on the current approaches in the budget preparation because they enhance governmental performance, enhance the effectiveness and efficiency of state expenditures, strive to diversify the income, and convert the policies and goals of the state into a definite and actualized reality on the ground, with the help of standards and indicators that modern means of preparing the budget have offered. And to act to modernize the budget system in Iraq, profiteering with the experience of other countries in this domain, and that modernization be a progress, commencing with the budgeting of some ministries or departments within ministries and bodies gradually with the aim of acquiring more experience and expertise, and then meet the impediments which will emerge to proceed to a complete transition.

This study investigates the interconnected roles of board gender diversity and Environmental, Social, and Governance (ESG) performance on firm performance within Indonesia's distinctive two-tier corporate governance system. Utilizing a panel dataset of 80 companies listed on the Indonesia Stock Exchange from 2021 to 2023 and employing a fixed-effects regression model, the analysis measures gender diversity on both the Board of Commissioners (BOC) and Board of Directors (BOD) using the Blau Index, with firm performance proxied by Tobin's Q and ESG performance sourced from Refinitiv Eikon scores. The empirical results reveal that gender diversity on both the BOC and BOD does not have a statistically significant effect on firm performance, failing to support agency, upper echelons, and gender socialization theories. Furthermore, ESG performance demonstrates a significant negative direct effect aligning with the trade-off perspective that current implementation costs outweigh benefits. Crucially, the analysis finds that ESG does not moderate the board diversity-performance relationship, as both interaction terms are statistically insignificant. These findings collectively indicate that the potential governance and strategic advantages of board gender diversity are not being realized in the Indonesian context. The study concludes that this is attributable to several structural barriers, including tokenistic board appointments, the early-stage and often symbolic nature of ESG adoption focused on compliance rather than integration, and a weak institutional environment characterized by voluntary frameworks and socio-cultural constraints that limit the substantive influence of women in governance roles.

Chronic liver disease is a significant health problem and places a significant burden on healthcare services. Deaths from complications of chronic liver disease (cirrhosis and cancer) account for approximately 3.5% of all deaths worldwide each year. Hepatocellular carcinoma (HCC) is a primary liver tumor, accounting for 90% of all liver tumors. Due to limitations in liver transplantation, liver resection is considered the treatment of choice for HCC patients with a single tumor and sufficient liver function reserve. Liver resection and transplantation are the only curative treatment options available for HCC patients. Surgical procedures require anesthesia. General anesthesia is the preferred method for liver tumor resection. There are five main classes of anesthetic agents: intravenous (IV) anesthetics, inhalational anesthetics, IV sedatives, synthetic opioids, and neuromuscular blocking agents. Estimated health risks depend on the extent or severity of liver disease, the nature and timing of surgery, the type of anesthesia, and other comorbidities.

Business judgment rule (BJR) is a principle of protecting directors from suboptimal business decisions that result in company losses. The purpose of this study is to analyze and to explore the legal provisions of BJR and its application principles, which are often related to several cases of directors of companies in making business decisions. The following study uses a normative research method (normative legal research) with a descriptive analytical nature that examines secondary data sources obtained from reading library materials which are finally analyzed qualitatively. Regulations related to BJR can be found in the provisions of the Limited Company Law, the Financial Services Authority Regulation for public companies, and the BUMN Law, as well as the Regulation of the Minister of BUMN in regulating BJR and the application of the principles good corporate governance within the scope of state-owned enterprises. The principle of BJR protection for company directors applies as long as they can prove themselves in managing the company within the corridor fiduciary duty, duty of care, duty of skill, duty of loyalty, and not involved in the practice conflict of interest.

Cloud-native systems are essential for modern software development, offering enhanced scalability, flexibility, and resilience through cloud computing environments. However, ensuring the reliability and performance of these systems presents a challenge due to their dynamic and distributed nature. Traditional testing methods, such as unit and integration testing, while valuable for detecting individual component defects and interactions, are insufficient for predicting failure rates in complex, cloud-native applications. This study explores the effectiveness of various testing techniques and quality metrics in predicting failure rates within scalable cloud-native systems. A comparative experimental study was conducted using three primary testing techniques: unit testing, integration testing, and chaos testing. The results indicate that chaos testing, when combined with advanced quality metrics such as migration rate and mismigration rate, significantly outperforms traditional methods in predicting failure rates and evaluating system resilience. These findings suggest that chaos testing offers a more comprehensive evaluation, simulating real-world disruptions to test system behavior under stress, which is essential for cloud-native environments where high availability and fault tolerance are critical. The study also highlights the importance of integrating predictive quality metrics, which improve the accuracy of failure predictions and enhance system reliability. The study concludes that for cloud-native systems, a combination of advanced testing techniques and predictive metrics is essential for ensuring high availability, scalability, and reliability in dynamic environments. Future research should focus on refining predictive testing approaches, developing standardized frameworks, and empirically validating new testing methods to address the growing complexity of cloud-native systems.

This study compares the scalability and maintainability of three prominent programming paradigms-functional programming (FP), object-oriented programming (OOP), and declarative programming (DP)-in the context of distributed data processing systems. The research aims to evaluate how each paradigm performs under increased data volume and its ability to handle complex operations, while also assessing the ease of maintenance through code readability, modularity, and the flexibility of updating and debugging. The study employs a comparative experimental design, implementing identical data processing tasks, such as data aggregation, filtering, and transformation, across each paradigm. Key findings indicate that FP and DP outperform OOP in terms of scalability, with their stateless nature and high-level abstractions enabling efficient parallel processing and task distribution. FP, with its emphasis on immutability and concurrency, and DP, with its focus on describing desired outcomes rather than implementation specifics, both demonstrate superior performance in handling large datasets. However, while OOP excels in modularity and flexibility, its reliance on mutable state and shared resources hampers its scalability in distributed environments. In terms of maintainability, both FP and DP offer clearer, more maintainable code due to their abstraction levels, making them easier to update and extend. OOP, while modular, presents challenges in managing mutable state, complicating maintenance. This paper concludes with practical recommendations for developers on when to use each paradigm based on system requirements and suggests areas for future research, such as hybrid paradigms and long-term maintainability studies in real-world applications.

Autonomous decision-making systems increasingly rely on complex artificial intelligence models to operate in dynamic and safety-critical environments. While these models provide strong predictive capabilities, their black-box nature limits transparency, trust, and accountability. This study proposes a structured research methodology for integrating Explainable Artificial Intelligence (XAI) into autonomous decision-making systems. The research adopts a conceptual–analytical approach to develop an explainability-oriented framework that embeds transparency across perception, decision-making, and action execution stages. The methodology includes literature-driven problem identification, conceptual framework construction, classification and mapping of XAI methods, and formulation of explainability evaluation criteria. The results demonstrate that effective explainability in autonomous systems requires a hybrid integration strategy, combining in-model transparency with post-hoc explanation mechanisms. A structured mapping of XAI techniques to autonomous system components and a conceptual decision-flow diagram are presented to illustrate explainability integration. The findings highlight that layered and context-aware explainability enhances system interpretability, supports human oversight, and improves safety relevance without compromising autonomous operation. This study contributes a reusable methodological foundation for the design and evaluation of explainable autonomous systems, offering practical guidance for future empirical validation and real-world deployment in safety-critical applications.

Semantic interoperability remains a major challenge in large scale distributed information systems due to heterogeneous data schemas, diverse contextual interpretations, and the dynamic nature of distributed environments. Traditional metadata-based interoperability approaches are often insufficient to address these challenges, as they lack semantic expressiveness and adaptability. This study proposes a context aware knowledge graph framework to enhance semantic interoperability across heterogeneous distributed systems. The research adopts a design-oriented methodology involving requirement analysis, knowledge graph construction, ontology modeling and alignment, context aware semantic representation, and semantic reasoning. A prototype implementation is developed to evaluate the effectiveness of the proposed framework through interoperability scenarios and cross-system semantic queries. The results demonstrate that the proposed approach significantly improves semantic alignment accuracy, query precision, and recall compared to conventional metadata-based solutions. The explicit integration of contextual information and ontology-based reasoning enables adaptive semantic interpretation and reduces ambiguity across systems. Overall, the findings confirm that combining knowledge graphs with ontology modeling and context aware mechanisms provides a robust and scalable solution for improving semantic interoperability in complex distributed information systems.

This article aims to describe the teaching methods and practices of the Philosophy of Islamic Education course at STAI Darussalam. This research uses a qualitative descriptive approach with a field study method that focuses on the learning process, the competencies to be achieved, teaching methods, assignment formats, evaluation systems, and obstacles encountered during the course. Data were obtained through observation and information gathering related to the implementation of the Philosophy of Islamic Education course. The results show that the teaching of the Philosophy of Islamic Education at STAI Darussalam focuses on understanding the nature of Islamic education, developing students' critical thinking patterns, improving educational literacy, and linking philosophical studies to social and political realities. The lecture method remains the dominant method used by lecturers, with an emphasis on students' ability to define and explain basic philosophical concepts accurately as an indicator of understanding. Learning success is measured by changes in students' thinking, which becomes increasingly critical, reflective, and contextual in responding to educational issues and social justice issues.

This article examines the integration of constructivist theory in Christian Religious Education (PAK) by emphasizing the role of personal experience as the basis for faith formation. Through a reflective-theological qualitative approach, this research combines the constructivist thought of Piaget, Vygotsky, and Fosnot with a theological perspective on the incarnation of Christ and the work of the Holy Spirit. The analysis suggests that constructivism can be theologically reinterpreted into a new paradigm called the Theo-Constructive Epistemology of Incarnational Faith, in which faith is not simply taught cognitively, but is constructed through life experiences infused by the presence of God. This approach emphasizes that the learning of faith is relational, participatory, and contextual, in line with the nature of God who works in human history. The findings of this study offer a new direction for PAK praxis to be more relevant to digital contexts and cultural plurality, without losing its theocentric orientation. Thus, the integration of constructivism and incarnational theology opens up space for more dynamic, contextual, and transformative learning of faith, while strengthening the relevance of PAK in facing the challenges of modern times.

Big data platforms face significant challenges related to cybersecurity and privacy due to the vast volume, variety, and velocity of data they manage. Traditional static security measures often fail to address the dynamic and complex nature of big data environments. This research proposes an adaptive cybersecurity framework that integrates dynamic access control and differential privacy mechanisms to enhance both the security and privacy of big data platforms. The dynamic access control mechanism continuously adjusts access permissions in real-time based on changing risk and trust levels, ensuring that sensitive data remains secure even as user roles and data flows evolve. The differential privacy mechanism adds noise to data, preserving individual privacy while allowing for meaningful data analysis. Through simulations and case studies, the framework was evaluated in various real-world environments, including healthcare, IoT, and finance, where it demonstrated scalability, efficiency, and robust security performance. The results showed that the proposed framework significantly reduced unauthorized access attempts and maintained data privacy, while still enabling effective data analysis. Although there were some challenges regarding performance overhead, particularly in resource-constrained environments, the framework remained effective in large-scale systems. The findings highlight the importance of adaptive security practices in big data environments and suggest that future research should focus on refining dynamic security mechanisms and applying differential privacy in diverse real-world scenarios. These advancements are essential for ensuring that big data platforms can handle evolving cyber threats without compromising data utility or privacy.

The rapid proliferation of Internet of Things (IoT) devices across diverse industries has significantly increased the vulnerability of IoT edge networks to sophisticated cyber threats. Traditional intrusion detection systems (IDS), such as signature-based and anomaly-based approaches, are often insufficient in addressing the dynamic and evolving nature of these threats. This study proposes a hybrid intrusion detection system (IDS) framework that combines supervised machine learning (ML) techniques with deep reinforcement learning (DRL) to enhance detection performance in real-time, resource-constrained IoT environments. The proposed framework utilizes supervised learning for initial traffic classification and DRL for adaptive decision-making, enabling the system to continuously learn and optimize its detection policies based on new attack patterns. The hybrid approach significantly improves detection accuracy and reduces false positives when compared to conventional signature-based and single-model ML systems. In addition to improved detection capabilities, the framework's computational efficiency allows it to operate effectively within the constraints of IoT devices, ensuring that it is suitable for large-scale deployments. Benchmark evaluations using publicly available datasets, such as NSL-KDD, IoT-23, and BoT-IoT, show that the hybrid IDS framework outperforms traditional methods, providing a more robust and adaptive solution to cybersecurity challenges in IoT edge networks. The findings of this study suggest that combining machine learning with deep reinforcement learning offers a promising approach to secure IoT environments and address the limitations of existing IDS techniques. Future work will explore enhancing real-time adaptability, scalability, and the detection of zero-day attacks in evolving IoT ecosystems.

Digital transformation initiatives have become essential for organizations seeking to remain competitive in today’s rapidly evolving technological landscape. However, many organizations face challenges due to ineffective Information Systems (IS) governance, which hampers strategic decision-making and the successful execution of these initiatives. This study aims to develop an IS governance framework that enhances decision-making quality by aligning IT decisions with organizational goals during digital transformation efforts. The proposed framework addresses existing gaps in current IS governance models, offering a solution to common challenges such as inadequate governance structures, resource constraints, and misalignment between IT and business strategies. The framework was developed through a mixed-method approach, including conceptual framework development, expert consensus via the Delphi method, and organizational validation studies. Key findings reveal that the framework improves transparency in decision-making, enhances accountability for IT decisions, and ensures better alignment between IT strategies and organizational objectives. By embedding agile leadership and data-driven decision-making principles, the framework enables organizations to respond effectively to the fast-changing dynamics of digital transformation. This study also compares the proposed framework to existing models such as COBIT and ITIL, highlighting its unique features, including its adaptability to the fluid nature of digital transformation. The framework's strengths include its comprehensiveness and flexibility, though its application may face challenges in organizations with limited digital maturity or rigid governance structures. Future research directions include exploring the integration of emerging technologies into the framework and its applicability across different organizational contexts.

This study investigates the determinants of capital structure within the healthcare industry, focusing on the influence of asset structure, profitability, and company size. Given the capital-intensive nature of healthcare—characterized by significant investments in medical technology and infrastructure—understanding how these firms balance debt and equity is critical for financial sustainability and operational growth. Using a quantitative approach, this research analyzes a panel dataset of healthcare companies listed on the Indonesian Capital Market from 2018 to 2023. Furthermore, the capital structure is quantified by the debt-to-equity ratio. Meanwhile, asset structure, profitability, and company size are measured by the ratio of fixed assets to total assets, return on assets, and the natural logarithm of total assets, respectively. Moreover, the data are analyzed using multiple linear regression, supported by the classical assumption testing. As a result, asset structure has a significant positive impact on capital structure, aligning with the static trade-off theory. Conversely, profitability demonstrates a significant negative relationship with capital structure, supporting the pecking order theory. Finally, company size positively affects capital structure, indicating that larger firms have greater access to debt financing. These findings suggest that healthcare managers should optimize their asset utilization and internal reserves to maintain an efficient capital structure that supports long-term healthcare delivery and investor confidence.

The rapid growth of information technology has encouraged small and medium-sized food businesses to adopt digital systems in order to improve service quality and operational efficiency. Ayam Goreng Pakdhe, a local culinary business, still relies on manual ordering processes that often lead to order inaccuracies, slow service, and limited access to sales data. This study aims to design and develop a web-based ordering system for Ayam Goreng Pakdhe using the Spiral software development method, which emphasizes iterative development, risk analysis, and continuous user feedback. The research method consists of requirement analysis, system design, implementation, and evaluation carried out in repeated cycles according to the Spiral model. Data were collected through observation, interviews with business owners and employees, and analysis of existing business processes. The results show that the proposed web-based system is able to streamline the ordering process, reduce human errors, and provide real-time information on orders and sales reports. Furthermore, the iterative nature of the Spiral method allows the system to be adjusted according to user needs and potential risks identified during development. The implementation of this system is expected to enhance operational efficiency, support better decision-making, and increase customer satisfaction, thereby contributing to the digital transformation of small-scale culinary enterprises.

This student aims to determine how BPNT contributes community in Sipatana District, Gorontalo City. Method used is qualitative findings show that the implementation of BPNT in Sipatana District, particularly in Molosipat U and Tanggikiki sub-districts, has been carried out in accordance with the statutory regulations of 2019 regarding the indicators of power distribution and actor strategies, without any personal intervention in determining beneficiary eligibility. Regarding the characteristics of the implementing institutions, coordination among agencies from the district level to RT/RW has run well and has been conducted transparently and participatively to ensure that the assistance is well-targeted. However, on the indicators of implementer compliance and responsiveness, several obstacles are still found such as delays in disbursement, a lack of direct socialization to the community, and slow responses from the sub-district office in handling complaints from beneficiary families (KPM). These conditions lead to a lack of optimal understanding among beneficiaries regarding the mechanism of aid distribution. Additionally, the impact of BPNT on improving beneficiary welfare remains consumptive in nature and has not fully encouraged economic independence among poor households. Therefore, improvements in service effectiveness, two-way communication, and periodic evaluation are required to enhance BPNT’s contribution in reducing poverty rates and achieving sustainable community welfare.

Rapid technological change in the digital era has reshaped the nature of organizational innovation, requiring firms not only to innovate but also to align their innovation activities with technological developments and market dynamics, a capability referred to as Techno-Resonance Innovation Capability (TRIC). Although Knowledge Management (KM) has been widely recognized as a driver of innovation, studies that explicitly link KM to the development of techno-resonant innovation remain limited. This study aims to address this gap by systematically reviewing the literature to examine how KM contributes to innovation capability and how this relationship evolves toward TRIC through absorptive capacity. Using the systematic literature review methodology proposed by Tranfield et al., this study follows three stages planning, conducting, and reporting the review to identify, evaluate, and synthesize relevant studies on KM, absorptive capacity, innovation capability, and techno-resonance. The findings indicate that innovation capability emerges from interconnected KM processes, including knowledge acquisition, sharing, storage, and application, which form the organizational infrastructure for innovation. Absorptive capacity is identified as a key bridging mechanism that enables organizations to transform managed knowledge into innovative outcomes by enhancing their ability to acquire, assimilate, transform, and exploit technological knowledge. This study concludes that integrating KM and TRIC through absorptive capacity extends conventional innovation capability models and provides a stronger theoretical explanation of innovation in technology-driven and digitally dynamic environments.

This study aims to conceptually examine the role of visual media in Arabic vocabulary learning and its relevance in the context of education in the digital era. Vocabulary mastery is a fundamental component in learning Arabic; however, in practice, vocabulary learning often encounters various challenges, such as the abstract nature of vocabulary, low student motivation, and the dominance of conventional teaching methods. This study employed a qualitative approach using library research. Data were obtained through a review of relevant scholarly sources, including books, journal articles, and previous research related to visual media and Arabic language learning. Data were collected through documentation techniques and analyzed using content analysis to examine the concepts, roles, and effectiveness of visual media in Arabic vocabulary instruction. The findings indicate that visual media, both conventional and digital, play a significant role in improving vocabulary comprehension, strengthening memory retention, and increasing students’ motivation and engagement in the learning process. Visual media also help transform abstract vocabulary into more concrete and contextual representations, making learning more meaningful and effective. Nevertheless, the implementation of visual media still faces several challenges, including limited facilities, teachers’ digital competence, and the suboptimal use of technology in learning activities. Therefore, it is necessary to develop innovative instructional strategies and enhance teachers’ competencies to ensure that visual media can be utilized effectively and sustainably in Arabic language learning.

The development of Islamic Religious Education in Banjar Land represents a long historical process marked by social, political, and religious transformations. Since the era of traditional da‘wah conducted by ulama through halaqah, surau-based instruction, and religious study circles (majelis taklim), the Islamic education system in this region has continuously evolved, eventually giving rise to formal educational institutions such as madrasahs and Islamic-based schools. This study aims to chronologically describe these historical dynamics, examine the factors influencing educational change, and analyze the roles of ulama, the Banjar Sultanate, and community institutions in the transformation of Islamic education. The research employs a qualitative method with a historical approach, involving the stages of heuristics, source criticism, interpretation, and historiography. Data were collected from scholarly literature, local manuscripts, and the works of Banjar ulama. The findings indicate that the transformation of Islamic education in Banjar Land, from the period of Islamization to the emergence of madrasahs and Islamic Religious Education in public schools, occurred through at least three major phases: (1) family- and community-based traditional da‘wah through langgar (prayer houses) and halaqah institutions; (2) the establishment of modern madrasahs in the twentieth century as a response to colonialism and modernity; and (3) the integration of Islamic education into the formal national education system in the post-independence era. This study affirms that Islamic education in Banjar Land possesses distinctive characteristics, is adaptive in nature, and is deeply rooted in local culture.

Vitro Fertilization (IVF) technology is a biotechnological innovation that helps infertile couples conceive. However, the application of this technology has raised ethical debates regarding the nature of life, human dignity, and the moral boundaries of artificial reproduction. This article aims to examine the practice of IVF through an ethical deontology perspective, a moral approach that emphasizes obligations, principles, and respect for human beings as ends, not means. The research method used is a literature review with a descriptive-analytical approach to bioethics and moral philosophy literature. The results of the study indicate that based on deontological ethics, the application of IVF can be considered moral if it respects human dignity, does not treat embryos as mere objects, and is carried out with intentions that are in accordance with human moral obligations.