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Public transportation is a primary necessity for supporting daily activities in Denpasar City. One mode of transport that plays a strategic role is the Trans Sarbagita Bus. As a mass transportation service, this bus system is expected to provide effective, efficient, and affordable mobility. This study aims to analyze: (1) public perception of the effectiveness of the Trans Sarbagita Bus service; (2) the simultaneous effect of accessibility, punctuality, and ticket price on service effectiveness; (3) the partial effect of each of these variables; and (4) the strengths, weaknesses, opportunities, and threats (SWOT) of the Trans Sarbagita Bus system. A quantitative method with an associative approach was employed. Data were collected from 105 active respondents through observation, interviews, and a five-point Likert scale questionnaire, and analyzed using multiple linear regression via SPSS. The findings show that: (1) public perception is generally positive, especially in terms of comfort and safety, although accessibility and punctuality remain suboptimal; (2) accessibility, punctuality, and ticket price simultaneously have a significant effect on service effectiveness; (3) each of these variables also has a positive and significant partial effect; and (4) strengths lie in low fares and safety, while key weaknesses include limited access and delays. Opportunities stem from policy support and rising public awareness, whereas threats include the dominance of private vehicles and low intermodal integration. It is recommended that the government enhance public transport campaigns through media and community partnerships, as well as expand bus stop access and intermodal connectivity to improve service effectiveness.

Development administration represents a critical intersection between public administration theory and practical development implementation. This research examines the evolution of development administration, analyzing its theoretical foundations, contemporary challenges, and emerging strategies for effective governance in developing nations. Through a comprehensive review of literature and case studies, this study identifies key factors that contribute to successful development administration, including institutional capacity building, participatory governance, technological integration, and sustainable resource management. The findings suggest that effective development administration requires a multifaceted approach that balances top-down policy implementation with bottom-up community engagement, while leveraging modern technologies and maintaining long-term sustainability perspectives.

This study explores the relationship between religiosity and Organizational Citizenship Behavior (OCB) within contemporary organizations by adopting a positive psychology perspective. OCB is defined as discretionary behaviors by employees that go beyond their formal job duties and contribute positively to the overall functioning and success of the organization. Examples include helping colleagues, being punctual, and demonstrating initiative, which foster a cooperative and productive work environment. Religiosity, on the other hand, is understood as a belief system that shapes an individual’s values, attitudes, and behaviors, both intrinsically—stemming from internal spiritual commitment—and extrinsically—motivated by external social or cultural influences. Through a systematic review of ten national and international academic journals, this study identifies several common dimensions of OCB, such as altruism (helping others selflessly), civic virtue (active and responsible participation in organizational affairs), and conscientiousness (going beyond minimal job requirements). The findings reveal a consistent positive correlation between religiosity and OCB, especially in sectors like education, Islamic banking, and healthcare, where religious principles strongly influence organizational culture. The integration of religious values into organizational practices encourages employees to exhibit behaviors that promote harmony, ethical conduct, and a sense of community within the workplace. This is particularly evident in employees who possess high levels of intrinsic religiosity, as their internalized beliefs motivate them to engage in prosocial actions that benefit the organization. The study recommends that human resource management strategies incorporate religious or spiritual values to cultivate a positive organizational culture. Such integration can be achieved through training programs, leadership development, and reward systems that emphasize ethical behavior and social responsibility, ultimately leading to improved employee satisfaction, loyalty, and overall organizational performance.

Background: Government administration plays a crucial role in delivering public services and implementing policies effectively. However, many developing countries face significant challenges in administrative capacity, bureaucratic efficiency, and service delivery quality. Objective: This study aims to analyze the key factors contributing to administrative weaknesses in government and propose a comprehensive framework for strengthening government administration to improve public service delivery and governance effectiveness. Methods: This research employs a mixed-methods approach, combining literature review, case study analysis, and expert interviews. Data was collected from 150 government officials across three administrative levels (national, regional, and local) and analyzed using thematic analysis and statistical methods. Results: The study identified five critical areas for administrative strengthening: (1) Human resource development and capacity building, (2) Digital transformation and technology integration, (3) Process optimization and bureaucratic reform, (4) Performance management systems, and (5) Citizen engagement mechanisms. Implementation of these components showed significant improvements in service delivery efficiency (p < 0.001) and citizen satisfaction scores (p < 0.01). Conclusion: A comprehensive approach to strengthening government administration requires coordinated efforts across multiple dimensions. The proposed framework provides a roadmap for systematic administrative reform that can enhance governance effectiveness and public service quality.

The National Health Insurance (JKN) program is a strategic initiative by the Indonesian government to ensure accessible and affordable healthcare for all citizens. Based on literature review and analysis of secondary data, the JKN program significantly contributes to reducing direct household health expenditures (Out-of-Pocket/OOP), particularly among low-income and vulnerable populations. However, several challenges remain, including additional outlays for uncovered services such as non-generic medications and inpatient class upgrades, as well as limitations in health infrastructure. Furthermore, the integration of digital technology through the JKN Mobile application demonstrates a high level of effectiveness in administrative efficiency and user participation. This platform has expanded healthcare accessibility and improved user satisfaction, although digital literacy and technological disparities persist. Hence, strengthening technology-based policies and conducting regular evaluations are essential for ensuring the sustainability and equity of the JKN program implementation.

This paper investigates how Big Data Analytics (BDA) can accelerate the transition to a low-carbon digital economy. We present a systematic literature-based research framework (2015–2025) that synthesizes applications of BDA in energy systems, transportation, industry and supply chains. The methodology combines systematic review and conceptual modelling to identify pathways through which BDA reduces emissions: (1) demand-side optimization, (2) operational efficiency, (3) predictive maintenance and (4) data-driven policy and market instruments. Results highlight concrete case examples smart grids, digital twins, and green supply-chain analytics and quantify benefits reported in recent literature. Key challenges such as data governance, carbon costs of computing, and policy integration are discussed. The paper concludes with policy recommendations and a research agenda to align digitalization with decarbonization goals.

Inclusive and sustainable economic growth is a major challenge in today's digital age. Financial technology (FinTech) plays an important role in bridging the financial access gap while supporting environmental responsibility through the application of green finance. This study aims to analyse the role of FinTech in promoting inclusive economic growth and environmental sustainability, particularly through the application of blockchain technology in urban renewable energy systems. The research method uses a qualitative approach with secondary data analysis, supported by mathematical models to measure green financial inclusion and FinTech investment efficiency. The results show that the implementation of green FinTech significantly increases financial inclusion, expands economic access for the MSME sector, and reduces carbon emissions through investments in environmentally friendly energy projects. Global case studies in India and Sweden show that multisectoral collaboration and adaptive regulatory policies are key to creating a sustainable FinTech ecosystem. Therefore, the integration of technology, policy, and digital literacy among the public is necessary to realise digital finance that is fair, efficient, and oriented towards a green future

The global environmental crisis caused by population growth, waste, and resource exploitation demands a shift towards a more sustainable economic system. This study examines the role of Artifical Intelligence (AI) and Blockchain technological innovations as solutions in strengthening the implementation of the circular economy at the global level. Using qualitative methods through literature studies and focused group discussions, the resuts show that AI can optimize waste management and supply chains efficiently, while Blockchain increases transparency and accountability through secure digital recording. The integration of the two forms the Digital Circular Economy (DCE)-an innovative solution that promotes resource efficiency, data fairness, and global carbon footprint reducation. DCE is a strategic step toward creating new sustainability standards that are smart, transparent, and future-oriented.

This study explores the integration of sustainability principles into digital product development. It aims to create a framework for embedding environmental, social, and economic sustainability within digital design practices. Using a descriptive qualitative method based on literature analysis and thematic review, this study identifies core challenges and strategies in applying sustainable design. Findings highlight that sustainability integration enhances product value, reduces carbon footprint, and supports the Sustainable Development Goals (SDGs). The study contributes to a better understanding of how organizations can balance innovation with sustainability in digital transformation.

The rapid development of information technology has opened up opportunities for new threats to national security, one of which is information system hacking. Cyberattacks not only cause economic losses and disrupt public services but also pose a serious threat to Indonesia's digital sovereignty. In this context, state intelligence plays a strategic role as the vanguard in detecting, analyzing, and countering various threats to the country's strategic information systems. However, strengthening the state intelligence function in countering hacking crimes still faces various obstacles, ranging from immature regulations, weak inter-agency coordination, to limited technology and human resources. This study aims to assess the effectiveness of strengthening state intelligence in countering information system hacking and to formulate the urgency of updating national legal policy to support the formation of a strong, integrated, and professional cyber intelligence system. Using normative legal research methods supported by conceptual and case-based approaches, this study concludes that strengthening state intelligence requires regulatory updates, institutional integration, and investment in technology and human resources to safeguard national sovereignty in the digital era.

Rapid developments in information technology have opened up opportunities for new threats to national security, one of which is hacking of information systems. Cyber attacks not only cause economic losses and disrupt public services, but also pose a serious threat to Indonesia's digital sovereignty. In this context, state intelligence has a strategic role as the front line in detecting, analyzing, and countering various forms of threats to the country's strategic information systems. However, strengthening the function of state intelligence in dealing with hacking crimes still faces various obstacles, ranging from regulatory aspects that are not yet adaptive, weak inter-agency coordination, to limitations in technology and human resources. This study aims to examine the extent of the effectiveness of strengthening state intelligence in countering information system hacking, as well as to formulate the urgency of updating national legal policies that support the formation of a strong, integrated, and professional cyber intelligence system. Using a normative legal research method supported by a conceptual and case approach, this study concludes that strengthening state intelligence requires regulatory reform, institutional integration, and investment in technology and human resources in order to maintain national sovereignty in the digital age.

Liquid Petroleum Gas, commonly known as LPG, is widely used in household activities, especially for cooking. However, its flammable nature makes this gas very hazardous if a leak occurs, which can result in an explosion that damages buildings, endangers the safety of those living there, and causes financial losses. Recently, the improper or unsafe use of LPG gas has led to numerous accidents and fires. This raises serious concerns for the people who use it. LPG gas leaks are often difficult to detect due to various factors, such as the absence of the gas's distinctive odor or the absence of people around the leak location. This study aims to detect gas leaks to minimize or prevent fires and LPG gas explosions. The methods in the research that will be carried out include identification, literature study, data collection, design, implementation, system testing, and conclusions. In this study, the design and implementation of an LPG gas leak detection system based on Arduino will be carried out to minimize this risk. The system will use MQ-2 to determine the concentration of LPG gas in the air. When a leak is detected, the Arduino microcontroller will process the input and automatically close the solenoid and activate the buzzer as an alarm. The implementation and testing results concluded that the system can detect LPG leaks above 600 ppm and respond effectively by cutting off the gas supply and providing an audible warning. This system is expected to improve household safety by providing early warning of gas leaks. Future developments may include integration with an Android app for smartphones, enabling more practical remote monitoring.

This study explores the integration of blockchain technology with federated learning (FL) to enhance cross-organizational healthcare analytics while ensuring privacy and data security. Federated learning allows multiple institutions to collaboratively train machine learning models without sharing sensitive patient data. Instead, local data is used to train models, and only model parameters are exchanged. However, privacy concerns and data sharing inefficiencies have hindered broader healthcare collaboration. Blockchain, a decentralized ledger technology, addresses these concerns by ensuring data integrity and transparency, providing an immutable and tamper-proof record of all transactions. This study investigates how the combination of blockchain and federated learning can overcome these challenges, facilitating secure and efficient data sharing between healthcare institutions. The study uses synthetic multi-institution healthcare datasets to simulate real-world collaboration scenarios. The blockchain-enabled federated learning system ensures that no raw patient data is shared, significantly reducing the risk of privacy breaches while still allowing healthcare institutions to collaborate on predictive model development. The results show that while there is a slight decrease in model accuracy compared to centralized methods, the trade-off is outweighed by the privacy and security benefits. Blockchain’s integration ensures that model updates are transparent, enhancing trust between institutions and reducing concerns about data integrity. Moreover, the use of blockchain’s smart contracts automates and enforces compliance, further streamlining collaboration. This research contributes to the field by demonstrating how blockchain-integrated federated learning can create a secure, scalable, and privacy-preserving framework for collaborative healthcare analytics. The findings underscore the potential for this approach to enhance healthcare outcomes and improve decision-making across institutions while ensuring patient data protection.

This study explores the development and application of a digital twin-driven cybersecurity risk assessment model for Industrial Internet of Things (IIoT) networks. The increasing complexity and interconnectivity of IIoT systems have expanded the attack surface, making them vulnerable to a wide range of cyber threats. The digital twin model addresses this challenge by creating real-time virtual replicas of physical systems, which can simulate and predict network vulnerabilities and attack vectors. The model uses machine learning algorithms and real-time data to simulate cyberattacks, including Distributed Denial of Service (DDoS), malware, and data breaches. By providing continuous monitoring and dynamic risk predictions, the digital twin model enhances the resilience of IIoT networks compared to traditional cybersecurity frameworks. The findings indicate that the model's ability to predict potential cyber threats and simulate various attack scenarios provides a more proactive and accurate approach to cybersecurity in IIoT environments. Additionally, the study highlights key mitigation strategies, including adaptive security mechanisms, real-time anomaly detection, and the use of lightweight encryption for resource-constrained devices. Despite its effectiveness, challenges such as computational requirements, integration with legacy systems, and scalability were identified. This research underscores the strategic importance of digital twin models in securing IIoT systems and advancing Manufacturing 4.0 ecosystems. Future research should focus on enhancing model accuracy, expanding its application to diverse industrial sectors, and improving interoperability with legacy systems to further strengthen the security posture of IIoT networks.

The integration of autonomous systems in traffic management has become increasingly important as urban populations and vehicle numbers continue to rise, leading to significant congestion. Traditional traffic signal control systems, which rely on fixed timing, are no longer sufficient to handle the dynamic and complex nature of urban traffic. To address these challenges, the proposed explainable Deep Reinforcement Learning (DRL) framework aims to optimize traffic signal control by dynamically adjusting traffic signals based on real-time data. This approach enhances traffic flow efficiency, reduces congestion, and improves overall system performance. The framework leverages Vehicle-to-Everything (V2X) communication, which enables real-time data exchange between vehicles, infrastructure, and other road users, extending the perception range of autonomous vehicles and providing valuable insights for traffic signal optimization. Additionally, the integration of smart infrastructure, such as smart intersections, plays a crucial role in enabling adaptive traffic management and facilitating better coordination across multiple intersections. One of the key advantages of the proposed system is its transparency, achieved through the implementation of explainable AI (XAI) techniques. These mechanisms provide clear insights into the decision-making processes, ensuring that traffic management authorities and system users can understand the rationale behind the system’s decisions. Although challenges such as data accuracy, scalability, and cybersecurity risks remain, the proposed DRL framework shows great promise in revolutionizing traffic management systems. Future research directions include enhancing data collection methods, improving the scalability of the system for larger cities, and further developing explainability features to improve trust and adoption in real-world applications.

This paper explores the application of quantum-inspired optimization algorithms in the training of large-scale Graph Neural Networks (GNNs) within distributed cloud-edge environments. GNNs have gained significant attention due to their ability to model complex relationships in graph-structured data, yet their training presents challenges such as high computational demand, inefficient resource allocation, and slow convergence, especially for large datasets. Traditional meta-heuristic algorithms, while useful, often face scalability and performance issues when applied to such large-scale tasks. To address these challenges, we propose a quantum-inspired meta-heuristic algorithm that leverages quantum principles, such as superposition and entanglement, to enhance optimization processes. The algorithm was integrated into a hybrid cloud-edge system, where computational tasks are dynamically distributed between edge nodes and the cloud, optimizing resource utilization and reducing latency. Our experimental results demonstrate significant improvements in training speed, resource efficiency, and convergence rate when compared to traditional optimization methods such as Genetic Algorithms and Simulated Annealing. The quantum-inspired algorithm not only accelerates the training process but also reduces memory usage, making it well-suited for large-scale GNN applications. Furthermore, the system's scalability was enhanced by the hybrid cloud-edge architecture, which balances computational load and enables real-time data processing. The findings suggest that quantum-inspired optimization algorithms can significantly improve the training of GNNs in distributed systems, opening new avenues for real-time applications in areas such as social network analysis, anomaly detection, and recommendation systems. Future work will focus on refining these algorithms to handle even larger datasets and more complex GNN architectures, with potential integration into edge devices for enhanced real-time decision-making.

This research focuses on the development and evaluation of an Adaptive Edge-AI framework designed to optimize real-time data processing and decision-making in resource-constrained environments, specifically within smart city infrastructures. The primary problem addressed is the challenge of minimizing latency, reducing energy consumption, and ensuring the reliability of Cyber-Physical Systems (CPS) when using Internet of Things (IoT) devices. The objective of the study is to assess the effectiveness of this framework in real-world smart city applications such as traffic monitoring, environmental sensing, and smart utilities management. The proposed method integrates lightweight AI models, edge computing, and adaptive resource management techniques, including Federated Learning and Neural Architecture Search, to ensure optimal performance while addressing hardware constraints. The main findings reveal that the framework significantly improves real-time inference speed, reduces energy consumption of IoT devices, and enhances CPS reliability by minimizing communication delays and ensuring continuous system operation despite network disruptions. The application of this framework to smart transportation and urban utilities further demonstrates its potential to optimize city management processes. The study concludes that the Adaptive Edge-AI framework offers a promising solution for smart cities, enhancing operational efficiency, sustainability, and resilience. It is recommended for integration into smart city infrastructures to enable better resource management and decision-making in real-time applications.

The rapid growth of RT/RW Net-based internet services among the community has driven the need for an integrated, efficient, and easy-to-use billing system. The self-help RT/RW Net model often faces challenges in terms of administrative management, especially related to customer recording, billing, and financial reporting which are still done manually. Common problems faced by partners are payment systems that have not been digitized and minimal transparency in data collection and tracking of customer transactions. The purpose of this activity is to design and implement a web-based billing application built using the Laravel framework. This application is designed to facilitate the process of managing customer bills, monitoring internet service usage, and preparing financial reports automatically and in a structured manner. The method of implementing the activity includes the system design stage, training partners in using the application, and assistance in direct implementation in the RT/RW Net operational environment. The results of the activity show that the developed system has succeeded in automating most of the billing process and increasing the efficiency of administrative management by up to 80%. In addition, this system also has a positive impact on increasing transparency and accuracy of customer data. Suggestions for further development include the addition of automatic payment features through integration with payment gateways, as well as improving the user interface to make it more responsive and user-friendly.

This study aims to explain the urgency of instilling character values in education in the modern era, describe the strategies or approaches used in the educational environment, and identify challenges and solutions in their implementation. The method used is qualitative with a descriptive approach. Data collection techniques included observation, in-depth interviews, and documentation, with the research subjects consisting of teachers, students, and principals at primary and secondary education levels. The results show that the cultivation of character values such as honesty, responsibility, discipline, tolerance, and cooperation is very important to form learners who not only excel in academics, but also have moral integrity and emotional maturity. Strategies used in character cultivation include integration of character values in learning, habituation of positive attitudes, teacher role models, and extracurricular activities. The main challenges in implementing character education are the negative influence of digital media, the weak role of the family, and the lack of consistency in character development. Solutions include improving educators' competence in character education, strengthening cooperation between schools and parents, and creating a school environment that supports a positive culture. Thus, character education should be an integral part of the education system, through synergistic cooperation between schools, families and communities.

This study examined the effectiveness of applying the Logan Avenue Problem Solving (LAPS-Heuristic) learning model on mathematics learning outcomes among Class VII students at Wusto Imam An-Nasai Gowa using a one-group pretest–posttest design. The population consisted of all seventh-grade students enrolled in the even semester of the 2024/2025 academic year. A learning outcome test was employed as the primary research instrument to measure students’ performance before and after the implementation of the learning model. The data analysis involved descriptive statistical methods combined with gain tests to assess the extent of improvement. The findings revealed that students’ mathematics achievement fell predominantly within the very high category, with 53.85% of learners classified in this range, indicating a notable increase in category levels compared to their pretest performance. Furthermore, the percentage of students who successfully met the required minimum competency standards after the application of the LAPS-Heuristic model reached 96.15%. This outcome clearly illustrates that the vast majority of students—over 85%—achieved or exceeded the established passing threshold, suggesting that the integration of the LAPS-Heuristic approach was highly effective in enhancing mathematics learning outcomes. Overall, the results underscore that the LAPS-Heuristic learning model can serve as a practical and impactful strategy to improve student engagement, comprehension, and mastery of mathematical concepts in junior high school settings.