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Generation Z has unique characteristics compared to other generations. A generation that is always looking for references for something almost 24 hours a day via mobile technology and social networks. The aim of this research is to determine Generation Z's opinions on forming co-creation in coffee shop properties that are currently trending. In addition to the development of digital marketing and the growth of coffee shop  businesses in Indonesia, this generation is used to giving feedback and suggestions via social media or directly.The research method used utilizes literature studies and literature reviews from books, journals and previous consultancy research institutions. The results of the research  show that Generation Z plays a role and actively participates in the formation of co-creation in coffee shops, although Zero Consumers are said to be among those who do not care about the menu, price, quality, etc. hold. Brand, The atmosphere in the cafeteria.

This research was conducted on the importance of computer laboratories and the implementation of teaching and learning activities because most of the material is practical. It will be very difficult to carry out practical learning without learning tools or media that are in accordance with the guidance of the curriculum material, there are other subjects that cannot be done without a laboratory. The practicum learning process is less than optimal because one of the reasons is that students don't pay too much attention to the learning material. And there are no tools or software to monitor student work in the process of using computers. The Network Development Lifecycle method is used to develop and design network topologies, in this case the use of the Net Support School application to monitor learning in the laboratory at SMP Negeri 2 Bukittinggi. The result of implementing net support school is that teachers can share learning materials from the computer server. Teachers can also supervise and control students' activities in using computers while the learning process is in progress. So using the net support school application is very helpful for use in the learning process.  

This study seeks to create a system for monitoring the environment using the Internet of Things. It aims to help farmers manage their rice fields in a way that is both effective and sustainable. The system will provide farmers with real-time data on soil moisture, air temperature, and rainfall, allowing them to make informed decisions. Sensors will collect this information, which will be analyzed and presented simply through a web or mobile app. Early simulations suggest that this system could boost crop yields by as much as 15% and cut water use and costs by 20%. These findings point to the IoT monitoring system as a practical means to enhance rice farming's efficiency and productivity. Yet, there are hurdles. Issues like poor network infrastructure, high costs of implementation, and the farmers' ability to adapt to this technology need to be overcome for the system to work properly. This study aspires to make a real difference in promoting sustainable farming practices while paving the way for more advanced IoT solutions in the future.

This paper explains the process of issuing free trade zone (PPFTZ) customs notification documents through the Batam PT customs and excise system. Natio Bahari Agency Batam. The methods used in this observation are field research and interviews where the author obtains material or paper materials directly from the field and interacts directly with people who have an interest in the world of document publishing, both officers and fellow agents, apart from that. The author also met directly with relevant sources who were interested in the subject matter taken from PT. Natio Bahari Agensi which is one of the companies operating in the field of agency and Financial Services Customs Entrepreneurs. PT Natio Bahari Agensi Batam Serves various types of vessels ranging from tugboats, Dredger barges, to offshore vessels in Batam Harbor. In the document management system, the system used is the CEISA-PPFTZ system. After the Commercial Invoice and Packing List is received by the PPJK from the importer or exporter, it can be continued by carrying out the process of submitting a new document registration number in the CEISA system until an Approval Letter can be issued. Goods Release (SPPB) or Goods Release Service Note (NPPB). The purpose of this research paper is to find out how the process of issuing Free Trade Zone Customs Notification Documents in the Batam Special CEISA System by PT. Natio Bahari Agency Batam. There are still various obstacles experienced in processing documents, such as network problems in the system which cause difficulties in inputting data in the system, delays in sending supporting documents by exporters and importers, and so on.

Energy efficiency is a critical concern in wireless sensor networks (WSNs) due to the limited power resources available in sensor nodes. Prolonging network lifespan while ensuring reliable data transmission is essential for successful deployment in various applications, such as environmental monitoring, military operations, and industrial automation. This paper presents a mathematical model designed to optimize energy consumption across various nodes in WSNs. By implementing simulations and analyzing data from these models, the study demonstrates significant improvements in extending network lifespan while maintaining reliable data throughput. The findings contribute valuable insights into energy management for large-scale sensor deployments.

Learning with Stimulus Environment Problem Solving (SEPS) is an involved learning process student in a way active good in network nor outside network in reading, writing, doing experiment, and analyze, as well look for solution to the problem social related science environment public.  This study aims to know the effectiveness of the Stimulus Environment Problem Solving (SEPS) learning model so that can increase ability think level high among students biology education Universitas Muhammadiyah Surakarta. Method in study this is correlate ability think critical and solving problem 2 groups student a total of 40 students where is one group totaling 20 students use learning model of Stimulus Environment Problem Solving (SEPS) and other groups with Project based Learning. Analysis hypothesis using the t test and effect size. Based on application of the Stimulus Environment Problem Solving (SEPS) model in the classroom experiment experience increase in t test results significant in cycle I and cycle II. Based on the effect size test, the effectiveness of the Stimulus Environment Problem Solving (SEPS) model is included in category tall with value 3.244 so effectiveness of the Stimulus Environment Problem Solving (SEPS) model on Skills think level tall own category tall.

Power systems are critical infrastructure that face significant challenges due to increasing demand and inherent complexity. Predicting failures in power systems is crucial for enhancing grid reliability, minimizing downtime, and optimizing maintenance processes. This study evaluates various deep learning models, specifically convolutional neural networks (CNN), recurrent neural networks (RNN), and transformer models, for predicting power system failures. By analyzing these models’ performance metrics on historical power grid data, the study provides insights into the strengths and weaknesses of each approach. The findings contribute to the development of more robust predictive models for power system reliability.

Accurate image segmentation is a pivotal process in medical imaging, essential for supporting diagnosis, treatment planning, and monitoring disease progression. This study evaluates the effectiveness of machine learning algorithms, including U-Net, Fully Convolutional Networks (FCNs), and Mask R-CNN, in achieving high-precision segmentation of medical images. Experimental results demonstrate that these models significantly enhance segmentation accuracy, enabling more precise diagnostic outcomes in clinical settings and advancing the development of automated medical imaging technologies.

Not having the geographic potential to be explored as a tourist area does not discourage the people of Mojogedang Village from improving their welfare. The objectives to be achieved in this activity are: To train Mojogedang Village PKK members to master marketing techniques through social media and internet networks by creating interesting content with digital marketing facilities so that they can generate significant income to improve the family economy in Mojogedang Village as a supporting area for Tourism Village in Mojogedang District. The approach technique is carried out using seminar methods and simulations directly utilizing the laptop and smartphone of each participant. With this dedication, Mojogedang Village PKK members can open their minds to competition, quality products, and the correct production process and develop themselves through digital marketing training, to improve economic welfare in the future. The following is the link to the results of the training https://bit.ly/Nuget_Jantungpisang_MojoGedang

Assistance in the use of social media for Micro, Small and Medium Enterprises (MSMEs) provides advantages and convenience in carrying out promotions to increase sales volume. The method used in community service is introduction, creation and assistance by choosing the right application is TikTok Shop. The TikTok Shop platform merges with TikTok as a TikTok platform that can share short videos without any time limits and has a wide reach and network with high traffic. The advantages of the TikTok Shop are expected to be able to help Mui Cat Shop to increase sales volume so that the expected profit is achieved.

Cyber Physical Systems (CPS) are vital for managing and controlling critical infrastructures, such as industrial control systems, power grids, and transportation networks. These systems integrate digital and physical components, offering numerous benefits for industrial automation. However, the increasing interconnectivity of these systems has introduced new security vulnerabilities, particularly in anomaly detection and system reliability. This research aims to address these challenges by proposing an edge based anomaly detection framework that leverages lightweight deep learning models, specifically designed to operate efficiently on resource constrained edge devices. Literature Review: Previous studies have shown the effectiveness of anomaly detection in CPS, with traditional methods struggling to keep up with the complexity and scale of modern industrial environments. Machine learning and deep learning approaches, particularly hybrid models combining rule based systems and AI, have emerged as effective solutions for real time anomaly detection. Techniques such as model compression, quantization, and pruning are essential for adapting these models to resource limited edge devices while maintaining high detection accuracy and low latency. Materials and Method: The proposed framework integrates deep learning models such as Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) networks, optimized for edge computing environments. The datasets used for training and testing include industrial network traffic and sensor anomaly datasets. Model optimization techniques like pruning and quantization were applied to reduce computational overhead and energy consumption on edge devices. Results and Discussion: The framework demonstrated high detection accuracy (AUC of 0.9720) with ultra low latency (0.0019 seconds training time), making it highly suitable for real time anomaly detection in CPS. Resource efficiency was achieved by optimizing the models for edge devices, reducing energy consumption while maintaining performance. The framework also significantly improved security by identifying anomalies early, preventing potential threats to critical infrastructures. Future directions include exploring federated learning to enhance privacy and data sharing across distributed devices.

This study explores the integration of hybrid AI control models, combining reinforcement learning (RL) and robust adaptive control, to improve the adaptability, performance, and stability of autonomous manufacturing systems. Traditional control systems, while effective under stable conditions, often struggle to cope with disturbances and varying production demands. Hybrid AI models, which integrate classical control methods such as Proportional Integral Derivative (PID) with machine learning techniques like RL, deep Q-networks (DQN), and deep deterministic policy gradient (DDPG), enhance decision-making capabilities in dynamic production environments. The study develops a hybrid RL robust control framework and tests it in both simulation and real-world scenarios. Performance metrics, including production efficiency, system stability, and adaptability, are assessed under various disturbance conditions, such as machine failures and fluctuating demands. The hybrid model significantly outperforms traditional PID control in terms of efficiency and stability, demonstrating faster convergence and better adaptability in dynamic environments. Statistical analysis confirms the superiority of the hybrid system over standalone RL models and traditional PID control. This model’s scalability and adaptability make it a promising solution for Industry 4.0 applications, addressing key challenges in real-world manufacturing systems by ensuring computational efficiency and the ability to manage large-scale data. The findings contribute to the development of more robust and efficient control strategies for autonomous manufacturing systems in uncertain environments.

This research uses a qualitative approach to understand the efforts of the North Lampung Police in dealing with motorbike theft. Qualitative methods allow for in-depth analysis of strategies, policies and obstacles faced in addressing this problem. Preventive efforts are carried out through integrated security, appeals to the community, as well as routine patrols and raids. Repressive efforts are carried out by taking firm action against perpetrators of crimes, as well as careful investigations and inquiries into cases of theft. However, the North Lampung Police face a number of obstacles in tackling motor vehicle theft crimes. The lack of timely public reports, the difficulty of obtaining sufficient evidence, and illegal motor vehicle trafficking networks are the main challenges. Even though preventive and repressive efforts have been carried out, innovation and better cooperation between the police and the community are still needed to overcome this problem. In conclusion, the North Lampung Police have made various efforts to tackle motor vehicle theft, but are still faced with a number of obstacles. Better coordination is needed between the police, government and community to create a safer and more comfortable environment for all parties.

This study aims to analyze the impact of social relationships in crowdfunding, focusing on the aspects of information, trust, and project funding. In the context of crowdfunding, social relationships between fundraisers and backers play a significant role in creating an environment that supports project success. The literature review reveals that strong social networks and information transparency can increase the level of backer trust. Trust built through positive social relationships significantly contributes to funding decisions made by individuals. In addition, this study shows that the quality of social relationships can serve as a catalyst in attracting financial support, with a wider network providing access to greater resources. Thus, the results of this study emphasize the importance of fundraisers to build and maintain good social relationships as a strategy to increase the chances of success in crowdfunding. Further research is recommended to explore cultural differences and contexts that may influence the dynamics of social relationships in crowdfunding.

The purpose of this research is to analyze the services of the population and civil registration offices towards ownership of identity cards, family cards and birth certificates for citizens of North Central Timor District. This research method is empirical juridical legal research, which is field research that examines the applicable legal provisions and the reality that occurs in the community. The aspects to be researched in this research are conducted through interviews, observation and documentation. The results showed that the service of DISDUKCAPIL TTU Regency was good enough, so that there was an increase in residents who wanted to take care of population documents, by looking at indicators of the five dimensions of public services, namely Tangible (Physical Evidence), Reliability, Responsiveness, Assurance, Empathy. Factors that hinder, namely: public awareness, distance, power outages resulting in disrupted networks, damage to tools/machines. Efforts of DISDUKCAPIL TTU Regency; conducting socialization related to the importance of ownership of population documents, conducting online ball pick-up activities, providing special services to people with disabilities, coordinating with parties related to public services to be able to get services from DISDUKCAPIL.

The development of increasingly sophisticated technology has resulted in changes in the use of internet networks used for online buying and selling ore-commerce. This research aims to determine the importance of implementing the principle of good faith in online buying and selling agreements as a responsibility for losses experienced by consumers in online transactions as an effort to protect consumers. The principle of good faith is an important principle, good faith must be exercised by sellers in order to provide protection to consumers, in order to minimize violations of consumer rights.

The importance of maintenance on the FTTX network as a communication service infrastructure is to avoid interference. Good maintenance activities must be carried out in an appropriate and consistent manner. The research methods used are observation, interviews with related parties as well as documentation and literature study. The maintenance activities carried out consist of FO Cut, FAT Loss/Problem, Bad Core, Low RSL, Bed Barrel, Bed Pigtail, FO Bending. Fiber To The X (FTTX) will be carried out periodic maintenance according to a predetermined schedule if a disturbance is found then there must be more action. Translated with DeepL.com (free version)

This article has been retracted at the request of the Editor-in-Chief. The journal was alerted to issues within this article, including significant overlap in content, methodology, and visual materials with another previously published article: "Social Engineering Attack Classifications on Social Media Using Deep Learning" (DOI: 10.32604/cmc.2023.032373) published in Computers, Materials & Continua in 2023. Upon thorough investigation, it was found that the article substantially reproduces ideas, methodologies, and figures from the original work without proper attribution, violating the ethical standards of the journal and academic publishing. The authors were contacted and asked to provide an explanation for these concerns. The corresponding author acknowledged the oversight and accepted responsibility for the duplication. Consequently, the authors formally requested the withdrawal of the paper. As per journal policy, the Editor-in-Chief has decided to retract the article due to a breach of publication ethics. The journal sincerely regrets that these issues were not detected during the manuscript screening and review process and apologizes to the authors of the original article, as well as to the readers of the journal. For more information on the journal’s ethical policies, please visit: Retraction Policy.

Analysis of the use of the Boruvka algorithm method in the context of electricity supply is an important topic in the development of electricity systems. In this study, we evaluate the effectiveness and application of the Boruvka algorithm in distribution optimization and power grid management. We study ways in which the Boruvka algorithm can be used to identify optimal electricity distribution paths, improve system efficiency, and minimize the potential for grid damage or failure. This research provides a deeper understanding of the potential and limitations of the Boruvka algorithm in the context of modern electricity infrastructure.

Integrating deep learning methodologies is pivotal in shaping the continuous evolution of computer-aided design (CAD) and computer-aided engineering (CAE) systems. This review explores the integration of deep learning in CAD and CAE, particularly focusing on generative models for simulating 3D vehicle wheels. It highlights the challenges of traditional CAD/CAE, such as manual design and simulation limitations, and proposes deep learning, especially generative models, as a solution. The study aims to automate and enhance 3D vehicle wheel design, improve CAE simulations, predict mechanical characteristics, and optimize performance metrics. It employs deep learning architectures like variational autoencoders (VAEs), convolutional neural networks (CNNs), and generative adversarial networks (GANs) to learn from diverse 3D wheel designs and generate optimized solutions. The anticipated outcomes include more efficient design processes, improved simulation accuracy, and adaptable design solutions, facilitating the integration of deep learning models into existing CAD/CAE systems. This integration is expected to transform design and engineering practices by offering insights into the potential of these technologies.