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The restaurant business continues to grow, but few business actors understand how to seize business opportunities to increase competitiveness. This study aims to analyze the application of the Canvas Business Model (BMC) in the culinary industry, with a focus on the fast food business in the city of Surakarta. BMC is used to evaluate nine key elements: value proposition, customer segments, distribution channels, customer relationships, key resources, key activities, key partnerships, cost structure, and revenue streams. The method used in this study is a qualitative descriptive approach, with data collection through surveys, interviews, and observations. The results of the study show that the implementation of BMC can increase the effectiveness of business strategies, especially in terms of product innovation, improved customer experience, and optimization of digital marketing and partnership networks. In addition, a SWOT analysis is conducted to identify the strengths, weaknesses, opportunities, and threats of the culinary business, which is then used as a basis in designing a growth strategy. The main findings of this study propose new business models that are more adaptive and sustainable, including product diversification strategies, the use of digital technology, and strengthening partnerships with suppliers and food delivery service platforms. This research is expected to be a reference for culinary entrepreneurs in developing more competitive and sustainable business strategies in the digital era.

PKK organization, comprised of women (wives as husbands' companions), plays a strategic role in household management, including influencing the behavior of their husbands. The objective of this writing is to analyze the role of PKK members in preventing the abuse of power by husbands. A qualitative approach with a descriptive-analytic method is capable of integrating the theory of the welfare state and the concept of abuse of power in public services. The method used in this community service is a participatory method through Focus Group Discussions (FGD). The research results show that PKK has a strategic position in preventing abuse of power through four main functions: monitoring government programs at the grassroots level, empowering the community through education and socialization, mediating between the community and government, and strengthening good governance. The effectiveness of the PKK role is supported by an organizational structure that extends to the RT/RW level and a strong social network in the community. This research recommends the importance of strengthening the capacity of PKK cadres, developing an integrated reporting system, and increasing coordination with formal supervisory bodies. PKK not only plays a role in improving family welfare but also becomes a strategic partner for the government in preventing abuse of power, supporting the realization of clean governance and quality public services

This work explores the potential of PennyLane and variational quantum-classical algorithms (VQCA) to forecast lung cancer using a structured dataset. The VQCA model performs exceptionally well, with flawless training, validation, and test accuracies of 1.0, demonstrating its capacity to identify patterns in the dataset and provide reliable predictions successfully. Contrarily, the accuracy of the quantum neural network (QNN) and classical neural network (NN) models is lower, demonstrating the benefits of utilizing quantum computing methods for enhanced predictive modeling. We provide a complete examination of the data, stressing the better performance of the VQCA model and its promise in correctly predicting lung cancer. The results highlight the importance of quantum-classical algorithms and help us understand the benefits and drawbacks of various strategies for predicting lung cancer. The study highlights the potential applications of quantum computing techniques in advancing the field of healthcare analytics. It shows the capability of the VQCA model to predict lung cancer using a tabular dataset accurately. Further research in this area is needed to explore scalability and practical implementation aspects. In summary, this study showcases the potential of VQCA and PennyLane in predicting lung cancer and underscores the benefits of quantum computing techniques in healthcare analytics.

This study examines the relationship between communication and auditor competency within Indonesia's public sector, focusing on its impact on audit quality and governance. The research addresses the challenges faced by public sector auditors, such as resource limitations and stakeholder expectations, which demand a balance between technical expertise and effective communication. The study was conducted in North Sumatra Province, involving 130 auditors affiliated with the Aparat Pengawasan Intern Pemerintah (APIP) network. A structured survey was used to collect data, focusing on auditors' communication skills, technical proficiency, and ethical judgment. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for data analysis, allowing the evaluation of complex constructs like communication efficacy and competency. The findings reveal that effective communication positively impacts auditor competency, enhancing collaboration within teams, fostering stakeholder trust, and ensuring actionable outcomes from audits. The results underscore the necessity of refining communication strategies to address governance complexities and stakeholder demands. This study highlights the symbiotic relationship between communication and competency, providing actionable insights for improving audit processes in Indonesia's public sector. It offers a foundation for developing training programs and frameworks that align with the International Professional Practices Framework (IPPF) and modern technological advancements to elevate public accountability.

This study is motivated by the issue of criminalizing drug couriers, who in judicial practice are often equated with the main perpetrators, even though in reality they occupy a subordinate position in the drug distribution network. The lack of clarity in the differentiation of roles in Law No. 35 of 2009 on Narcotics has led to the application of criminal liability that tends to be formalistic and has the potential to disregard substantive justice. This study aims to analyze the form of criminal liability of Class I drug couriers and assess whether the Pekanbaru District Court Decision Number 119/Pid.Sus/2025/PN Pbr has reflected justice in the imposition of criminal penalties. The research method used is normative juridical with a legislative approach, case approach, and conceptual approach. The results of the study show that the criminal liability imposed on couriers in the verdict is direct as the main perpetrator, without considering the theory of the perpetrator's role, degree of fault, and the defendant's capacity to be held responsible. The judge emphasized the fulfillment of the elements of the act as formulated in Article 114 of Law Number 35 of 2009 concerning Narcotics, without adequate analysis of the mens rea and the factual contribution of the courier in the structure of the crime. Justice in this study is understood as substantive justice, which requires consistency between the punishment, role, and degree of fault of the perpetrator. Proportional punishment is interpreted as the imposition of sanctions that clearly distinguish between couriers as subordinate actors and the main actors who control the narcotics network. This study concludes that the criminal liability of narcotics couriers must be based on individualization of punishment and proportionality so that law enforcement not only fulfills legal certainty but also reflects substantive justice.  

This study explores the role of peer mentoring as a strategy to reduce student dropout rates and strengthen social support networks in universities. Using a mixed-methods approach that combined quantitative surveys and qualitative interviews, the research examined students’ sense of belonging, academic motivation, persistence intention, and access to emotional and academic support. The findings indicate that peer mentoring significantly enhances students’ sense of community, increases their academic motivation, and strengthens their commitment to continue their studies. Compared to other retention strategies such as academic counseling or financial support, peer mentoring provides unique advantages by fostering ongoing peer-to-peer relationships and expanding social networks. Moreover, the study found that mentor–mentee interactions create a safe space for students to share academic challenges, personal concerns, and coping strategies, which contributes to improved well-being and greater confidence in navigating university life. Peer mentoring also offers opportunities for skill development, including communication, collaboration, and self-regulation, making it beneficial not only for mentees but also for mentors. These results suggest that peer mentoring not only mitigates dropout risks but also contributes to building long-term resilience and a supportive learning environment for students. Therefore, universities are encouraged to integrate structured peer mentoring programs into their retention initiatives to promote student success and holistic development.

Through the Governance Network, the government has a great possibility to reach problems more closely by utilizing the advantages of the network and developing innovative ideas to overcome public problems and improve the quality of services and public policies. One of the problems is the food security policy. This study uses the concept of Social Network Theory (Klijn & Koppenjan) which looks at the structure in the policy implementation network, namely the aspects of contact, trust, information sharing, and resource exchange as well as how to coordinate between actors in the food security policy implementation network in West Papua Province. The results of the study show that the relationship and participation of actors in the network have not run optimally. This can be seen from the structure in the network which includes trust, information sharing, and resource exchange has not been optimal. Meanwhile, contact from the structure in the network has been carried out well, with the formation of policy tools. Coordination in the food security policy implementation network uses the type of mutual adjustment coordination. The implementation of the Food Security Policy in the Perspective of the Governance Network has not been effective.

This study aims to compile an Information Technology (IT) Master Plan for SMK Ma'arif 1 Metro using The Open Group Architecture Framework (TOGAF) method. This method is applied to design a structured and integrated information technology infrastructure to support operations and learning in schools. The results of the study indicate that the main business processes in schools, such as student registration, curriculum management, and financial administration, are still carried out manually, making them prone to errors and inefficiencies. Through TOGAF, an information system architecture is designed that includes the development of the Academic Information System (SIAKAD) and other school management applications. In addition, improving network infrastructure and cybersecurity are priorities in the proposed technology architecture. Implementation strategies and change management are also formulated to ensure the success of the digital transformation. This IT Master Plan is expected to improve the efficiency of school operations and the quality of learning, as well as support the achievement of more optimal educational goals in the digital era.

Industrialization has helped the economic growth and progress of many countries, but it is still understood about its effects on government, power structures, and social relations. The study investigates the impact of industrialization on these three components, with particular emphasis on Indonesia. Government has changed significantly as a result of industrialization, which has played an important role in driving industrial growth. To attract foreign investment, improve infrastructure, and improve the business environment, governments have implemented several policies, but there are concerns about their ability to oversee industry and protect the interests of local communities. In addition, the power structure has undergone significant shifts, with the emergence of new social classes and the consolidation of power among certain classes. The industrial sector has also created new opportunities for social mobility, but has also exacerbated economic and social disparities. To solve this problem, this study suggests that the distribution of wealth and power must be fairer. Industrialization, along with increased urbanization and urban growth, has had a significant impact on social relations. The study investigates how industrialization has affected social relationships, including its impact on family structures, community networks, and social identities. So, Indonesian government, power structure, and social relations are heavily influenced by industrialization. Although it has produced significant economic gains, it has also created new problems and differences. This research suggests that a deeper understanding of the impact of industrialization is vital, as are the policies needed to address the social and economic impacts of industrialisation.

The development of information technology in the modern era is increasingly rapid. and influences various aspects of life, including early childhood education. The Internet, as a component of information technology, connects computers around the world into a very large network, enabling instant and global exchange of information. This has a significant impact on Religious and Moral Competencies in AUD. The qualitative approach used in this research is a case study design at the Bung Karno State Kindergarten. Data was collected through classroom observations, semi-structured interviews with PAUD teachers, and questionnaires for parents or guardians of students. The research results show that technology, especially the use of gadgets, has a significant influence on the moral and religious values ​​of young children. Children who use technology excessively tend to have difficulty socializing, have apathetic behavior, and show signs of addiction. Challenges in teaching moral and religious values ​​​​include the difficulty of conveying abstract concepts to children who are still at the fundamental cognitive development stage. This research concludes that moral and religious education needs to be fostered from an early age to form quality character. Although technology can facilitate various activities, uncontrolled use can have a negative impact on children's moral and religious development. Therefore, parents, educators and other stakeholders need to do better to manage the use of technology and strengthen the education of moral and religious values ​​​​in early childhood.

Background: The rapid growth of warehouse automation and autonomous mobile robots has increased the need for adaptive navigation systems capable of operating safely and efficiently in dynamic industrial environments. Classical path planning algorithms such as A* and RRT perform well in structured settings but exhibit limitations when handling moving obstacles and environmental uncertainty. Objective: This study aims to develop and evaluate a reinforcement learning based navigation framework integrated with sensor fusion to improve path efficiency, collision avoidance, and robustness in dynamic warehouse scenarios. Method: An experimental research design was implemented combining high-fidelity simulation and real-world warehouse prototype testing. Deep Q-Network and Proximal Policy Optimization models were developed and trained using multi-sensor inputs from LiDAR, camera, and inertial measurement units. Performance was evaluated using path efficiency, collision rate, computational cost, and robustness metrics, with benchmarking against classical algorithms. Results: The results demonstrate that the Proximal Policy Optimization model achieved the highest path efficiency and lowest collision rate while maintaining stable computational performance under dynamic conditions. Reinforcement learning models significantly outperformed classical planners in adaptability and robustness, confirming their suitability for scalable industrial warehouse automation.

Architecture using tree topology has become a popular approach in managing computer networks. Tree topology combines elements of star and bus topologies, resulting in an efficient and scalable network for a variety of purposes. This paper discusses the basic concepts of architecture using tree topology, including hierarchical structures and design advantages. First, the tree topology consists of a parent node (root node) which leads to a number of branch nodes (branch nodes). Each branch node can then connect to other nodes, forming a multilevel hierarchy. In computer networks, this topology is often used to connect a number of LANs (Local Area Networks) into a larger network. The main advantages of tree topology are scalability and management efficiency. The network can be expanded by adding new branch nodes without affecting the rest of the network. Additionally, network monitoring and maintenance becomes more organized due to a clear hierarchical structure. However, tree topology also has disadvantages. If the parent node fails, then all parts of the network connected to that node can be affected. Additionally, complex implementations may require more sophisticated hardware and software. In the modern computing era, tree topologies are used in a variety of contexts, ranging from corporate network infrastructure to online content distribution. Its ability to provide an organized and extensible structure makes it an attractive choice in designing efficient and reliable network architectures.

To effectively comprehend human actions, we have developed a Deep Neural Network (DNN) that utilizes inner spatiotemporal non-locality to capture meaningful semantic context for efficient action identification. This work introduces the Top-Heavy CapsNet as a novel approach for video analysis, incorporating a 3D Convolutional Neural Network (3DCNN) to apply the thematic actions of local classifiers for effective classification based on motion from the spatiotemporal context in videos. This DNN comprises multiple layers, including 3D Convolutional Neural Network (3DCNN), Spatial Depth-Based Non-Local (SBN) layer, and Deep Capsule (DCapsNet). Firstly, the 3DCNN extracts structured and semantic information from RGB and optical flow streams. Secondly, the SBN layer processes feature blocks with spatial depth to emphasize visually advantageous cues, potentially aiding in action differentiation. Finally, DCapsNet is more effective in exploiting vectorized prominent features to represent objects and various action features for the ultimate label determination. Experimental results demonstrate that the proposed DNN achieves an average accuracy of 97.6%, surpassing conventional DNNs on the traffic police dataset. Furthermore, the proposed DNN attains average accuracies of 98.3% and 80.7% on the UCF101 and HMDB51 datasets, respectively. This underscores the applicability of the proposed DNN for effectively recognizing diverse actions performed by subjects in videos.

Computer networks are an important part of modern life, enabling the efficient exchange of information and resources between devices. One type of network topology that is commonly used is a bus topology. The bus topology has a simple structure and is easy to implement, making it an attractive choice for a variety of applications. The main finding of this research is that the bus topology has several advantages and disadvantages. The advantages of bus topology include low cost, ease of installation, and scalability. Disadvantages of bus topology include limited performance, low reliability, and the potential for data collisions.

The implementation of Minister of State Apparatus Empowerment and Bureaucratic Reform Regulation Number 1 of 2020 regarding Guidelines for Job Analysis and Workload Analysis has been carried out. In Indonesia, workload analysis is important for determining the number of employees needed by the Surabaya Education Office so that the organizational structure can be efficient in carrying out its tasks. This study aims to evaluate the implementation of the Workload Analysis Application (ABK) on employee performance at the Surabaya Education Office. The research method used is descriptive qualitative using documentation, observation, and interviews. The ABK application is used to determine the needs of employees at the Surabaya Education Office. Data were collected through documentation, observation, and interviews. The results show that the procedure for using the Workload Analysis Application is in accordance with the guidelines set forth in the Ministerial Regulation. Human resources and infrastructure are quite good because socialization has been conducted to employees. Although the implementation is going well, there are still obstacles related to understanding the ABK filling format by some human resources and the need for additional computer facilities and improvements to an unstable network.

The rapid advancement of deep learning technologies has significantly transformed healthcare analytics, particularly in medical data prediction and classification. This study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) framework for multi-modal healthcare data analysis, integrating medical imaging, structured electronic health records (EHRs), and IoT-generated time-series physiological signals. The proposed architecture combines spatial feature extraction through CNN with temporal dependency modeling via LSTM to enhance predictive accuracy and clinical decision support. A quantitative experimental design was employed, utilizing multi-source healthcare datasets that underwent preprocessing, normalization, and feature engineering prior to model training. The performance of the hybrid model was evaluated using Accuracy, Precision, Recall, F1-Score, AUC-ROC, and Mean Absolute Error (MAE), and compared with conventional machine learning models and standalone deep learning architectures. Experimental results demonstrate that the proposed CNN–LSTM model achieves superior performance, with improved classification accuracy and reduced prediction error, while maintaining strong generalization capability. The findings indicate that integrating spatial and temporal feature learning significantly enhances disease detection, risk stratification, and personalized treatment planning. This approach supports the development of intelligent clinical decision support systems and scalable smart healthcare environments. The proposed framework offers a reliable and efficient solution for advanced healthcare analytics in IoT-enabled systems.

Domestic violence remains a critical pastoral problem because survivors in church communities often face not only physical, psychological, sexual, and economic harm, but also spiritual narratives that may either sustain recovery or silence disclosure. This constructive qualitative study aims to formulate a Christian pastoral counseling model for survivors of domestic violence in church settings. The study applies a library-based practical-theological design by critically synthesizing peer-reviewed literature on intimate partner violence, faith-based responses, trauma- and violence-informed care, pastoral theology, Indonesian legal norms, and church-related studies. The findings show that pastoral counseling for survivors must be structured around safety rather than immediate marital reconciliation; interpret Scripture through dignity, justice, and protection of the vulnerable; recognize religious coercive control as a form of harm; integrate emotional stabilization with spiritual care; and build referral networks with psychological, legal, medical, and social services. The article proposes an integrated PASTORAL-SAFE model that combines risk assessment, survivor affirmation, trauma stabilization, theological reframing, social support, professional referral, perpetrator accountability, and long-term pastoral accompaniment. The implication is that churches need written protocols, trained pastoral teams, confidentiality standards, and collaborative safeguarding systems to prevent pastoral responses from unintentionally reproducing violence.

BRI Bank BRI Agent has become an integral part of the Indonesian government's efforts to increase financial inclusion across the country. This research investigates the risks, role and impact of BRILink agents in expanding access to financial services for the community, especially in remote areas and difficult to reach by conventional financial institutions. Through a qualitative approach involving in-depth interviews and secondary data analysis, this study describes various important aspects related to BRILink agents, including the distribution network, services provided, challenges faced, and their impact on financial inclusion. The findings show that BRILink agents have succeeded in expanding the reach of banking services, facilitating financial transactions, and increasing financial literacy among previously underserved communities. However, challenges such as limited technological infrastructure, high operational costs, and lack of awareness of the benefits of financial services are still obstacles that need to be overcome. Based on the results of this research, it is recommended that the government, regulators and other related parties continue to support and improve the BRILink agent ecosystem through supportive policies, structured training and continuous technological innovation. Thus, BRI Bank BRI agents have significant potential in realizing wider and more sustainable financial inclusion in Indonesia.

Technical advances in computer applications have led to the creation of these library information systems, which are anticipated to support and facilitate the human workforce. It is anticipated that this technology will help with daily tasks in an effective and efficient manner as well. The subject of this study is the library system. There are obstacles in UPT Library SMP 34 GRESIK, and one of them is that the library still uses its service manual procedures, indicating the need innovation and change in the form of a cohesive system to accompany and prepare children in the classroom The library service information system provided by this network-based library information system should make it easier for students to find information and check out books, as well as for librarians to manage information about books and student loans. The waterfall approach was used by the author. To create this internet-based library resource system. The process of implementing this library information system begins with soft requirement analysis, prioritizing the process of borrowing books. Waterfall design techniques are used throughout the design stage, which involves creating a database with MySQL and building a user interface. Relationships are built in the database architecture using Entity Relationship Diagrams and Logic Relationship database (Database) structures.

Social media (social media) has now become a part of the life of Indonesian people who are very phenomenal. Various kinds of advantages and conveniences are offered to interact with everyone both in terms of business even from various circles. Not only that, with the development of internet use and communication technology devices such as smartphones that are increasingly advanced, it has become one of the drivers of the growth of new networking sites that offer friends and information online. Social media has also become the backbone as a means of communication in this digital century. This study aims to determine students' perceptions about the extent of the use of social media in helping them learn English. English that is expected to be understood and learned is English in hospitality and also in advertising language. English that is expected to be understood and learned is English in hospitality and also in advertising language. This research is important to find out how students perceive the effectiveness of using social media to support their learning and understanding and creativity in the field of English for Hospitality and in compiling advertising language. A total of 50 students from the English Literature study program of Udayana University will be used as data sources in conducting this research. This research will use qualitative methods with a case study design. The use of questionnaires as well as semi-structured interviews will be carried out in collecting data. In analyzing the data will use a basic statistical model of the results of questionnaires on students. Meanwhile, the results of the interview data will be analyzed with the Flow model from Miles and Huberman.