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In 2023, Batam City will record 550 homeless people and beggars undergoing rehabilitation. In 2024 (January-July), this number will drop significantly to 237. The aim of this research is to determine government collaboration in handling homeless people and beggars in Batam City. The method used is descriptive qualitative, there are eight informants with data collection techniques through interviews, observation and documentation. Data analysis was carried out through data reduction, data presentation, and drawing conclusions. This research uses Collaborative Governance theory from Deseve, namely Network Structure (Organizational Structure), Commitment to a Commom Purpose (Commitment to the goal), Trust Among the Participants (mutual trust between actors), the existence of Governance Certainty, Access to Authority (Access to power), Distributive Accountability (Sharing responsibility), Information Sharing (Sharing Information), Access to Resources (Access to Resources. The findings obtained are (1) Network Structure: Satpol PP carries out patrols and the Social Service supports it through the formation of a Rapid Response Team (TRC). (2) Commitment to a Common Purpose: Satpol PP and the Social Service are committed to outreach and reducing the number of Social Welfare Recipients, which is evaluated based on the budget. (3) Trust Among the Participants: collaboration between Satpol PP and the Social Service is running well thanks to open communication, regular coordination meetings, and mutual assistance. (4) There is certainty of governance: the role of Satpol PP and the Social Service is regulated in Regional Regulation No. 6 of 2002 and received support from other institutions. (5) Access to Authority: handling homeless people is regulated by Law no. 23 of 2014, with Satpol PP on patrol duty and the Social Service handling social problems through a budget supervised by the Inspectorate and BPK. (6) Distributive Accountability: The Social Service appoints the relevant OPD and forms a TRC which collaborates with Satpol PP in networking, while the TRC supports large or special activities. (7) Information Sharing: The Social Service shares the latest data and regulations in coordination meetings. (8) Access to Resources: Satpol PP and TRC have resources with a supporting budget as well as contributions from Srikandi PLN in counseling, equipment, and Pelni for the repatriation of homeless people and beggars. Therefore, researchers put forward suggestions, namely that the government should reach out more to the community and provide full assistance so that homelessness and beggars can be reduced.

The proliferation of network information algorithms (NIAs) in contemporary society has sparked significant ethical concerns regarding their societal impact. This study investigates the influence of NIAs on social interactions, decision-making processes, and the perpetuation of structural biases through a multidisciplinary perspective (Ananny, 2023). The findings reveal that while NIAs enhance operational efficiency across various domains, they also introduce ethical challenges, including privacy infringements, systemic inequities, and algorithmic opacity, which threaten social justice. Employing Ananny’s (2023) conceptual framework—which categorizes NIAs into three dimensions: encounters, observation, and probability/temporality—this research deconstructs the operational mechanisms of these algorithms. The analysis demonstrates that NIAs not only replicate historical biases but also engender new forms of discrimination through ostensibly neutral predictive processes. For example, algorithm-driven recruitment systems may perpetuate gender disparities if their training data reflects prior discriminatory practices (Crawford, 2021). This study underscores the inextricable link between technological ethics and societal context, arguing that an overreliance on algorithmic systems risks undermining human autonomy (Zuboff, 2019). The originality of this research lies in its integration of computational ethics theory with empirical case studies, such as the deployment of NIAs in mass surveillance, where privacy is often compromised in pursuit of perceived security. To ensure academic rigor, the arguments are developed through a critical comparison with prior research (e.g., Mittelstadt et al., 2016), while avoiding redundancy in phrasing or structure. Scholars such as Floridi (2019) emphasize the necessity of algorithmic transparency in regulatory frameworks. However, critics like Noble (2018) argue that technical solutions alone are inadequate; structural reforms in data governance and corporate accountability are essential to mitigate the misuse of NIAs. In response, this study proposes an ethical framework that not only addresses technical risk mitigation but also incorporates civic participation in algorithmic decision-making processes. The ethical implications of NIAs necessitate a holistic approach that integrates principles of data justice, independent algorithmic auditing, and public digital literacy. Future research should explore inclusive models of algorithmic governance, particularly in developing nations where regulatory frameworks often lag behind technological advancements. This study concludes with a reflective inquiry: How can algorithmic accountability be ensured if developers lack transparency regarding data sources and programming logic? By addressing these questions, this research contributes to the ongoing discourse on the ethical governance of NIAs and their societal implications.

Male infertility is a growing concern in reproductive health, affecting millions worldwide. Despite its significant impact on couples' ability to conceive, it remains underexplored compared to female infertility. This study offers a comprehensive bibliometric analysis of male infertility research, identifying trends, key contributors, and emerging areas of focus. By analyzing publication patterns, leading journals, influential authors, and collaborative networks, we aim to provide a nuanced understanding of the current state and future directions of this research field. The insights gained are expected to guide future investigations, enhance collaborations, and inform policymakers and clinicians about this evolving area. A systematic literature search was conducted in Scopus and Web of Science databases to retrieve relevant publications on male infertility. ScientoPy was used and bibliometric indicators, such as publication output, citation analysis, author collaboration networks, and keyword co-occurrence analysis, were used to explore the intellectual structure and research trends in this domain. Visualization tools, including VOSviewer, were employed to present the findings. The analysis included 2726 publications spanning from 2014 to 2024. The results reveal the most productive authors, influential journals, and highly cited publications in the field. Key research themes, emerging topics, and the evolution of the research landscape are discussed. Collaborations among authors, institutions, and countries are also examined to identify the intellectual structure and global research networks. This analysis provides valuable insights into the current state of research on male infertility The findings highlight the critical areas of focus, influential studies, and collaborative patterns that have shaped the field. Implications for future research directions, evidence-based practice, and potential areas for interdisciplinary collaboration are discussed.

This study aims to analyze the application of Deep Neural Networks (DNN) as an artificial intelligence approach in processing weather data to support more accurate and stable climate predictions. Increasingly unpredictable and fluctuating weather patterns demand modern analytical methods capable of capturing non-linear relationships among atmospheric variables. DNN is utilized due to its ability to learn complex data structures through multilayer representations that extract deeper features from input variables. Weather data such as temperature, humidity, rainfall, air pressure, and wind speed are processed through several preprocessing stages to ensure optimal model performance. This research employs a descriptive qualitative method based on literature studies to examine the role of DNN in weather prediction systems. The findings indicate that DNN demonstrates strong generalization abilities, robustness to fluctuating data, and more stable predictive outputs compared to conventional statistical approaches. Thus, DNN is considered a promising component for the development of early warning systems and modern data-driven climate analysis, offering improved reliability in understanding and forecasting atmospheric conditions.

In the digital era, the personal branding of lecturers and alumni plays a strategic role in enhancing university brand awareness. A strong academic identity and a positive image presented through digital platforms can strengthen the reputation of higher education institutions. This study aims to analyze how optimizing the personal branding of lecturers and alumni contributes to shaping university brand awareness. The research method used is a literature review by analyzing various scientific sources related to personal branding, brand awareness, and digital marketing strategies in the context of higher education. Data were collected from academic journals, books, and relevant publications discussing the role of digital media in building the image of academic institutions.The findings indicate that consistent and authentic personal branding of lecturers and alumni on digital platforms such as social media, university websites, and professional networks can enhance university exposure and appeal to prospective students and other stakeholders. Active interaction, expertise-based content, and participation in online academic discussions are key factors in building credibility and public trust. The implications of this study provide insights for universities in designing effective digital branding strategies by leveraging the active role of lecturers and alumni. Universities should encourage the strengthening of personal branding through training programs and structured digital communication policies to enhance institutional competitiveness at both national and global levels.

This study aims to address the challenges and propose solutions for the Optimization of Blockchain-Based Cybersecurity Systems to Enhance Resilience Against Ransomware Attacks using a Systematic Literature Review (SLR) approach. Blockchain is increasingly recognized as a transformative technology in cybersecurity due to its decentralized structure, transparency, and robustness in securing data. Despite these advantages, its widespread adoption is hindered by several challenges, including scalability, interoperability, high energy consumption, and limited access to representative ransomware datasets. This research highlights that integrating blockchain with advanced technologies such as data analytics, machine learning, and Explainable AI (XAI) can significantly enhance its effectiveness in combating ransomware.The findings reveal that Graph Convolutional Neural Networks (GCN) enable real-time detection of ransomware patterns in network traffic with an accuracy of up to 95%. Furthermore, Layer-2 solutions like the Lightning Network and sharding effectively alleviate the load on main blockchains, thereby increasing transaction throughput. Efficient consensus mechanisms, including Proof of Stake (PoS) and Delegated Proof of Stake (DPoS), address energy consumption issues, making blockchain more adaptable to IoT and resource-constrained environments. These approaches have proven successful in enabling early detection, mitigation, and prevention of ransomware in IoT systems, cloud infrastructures, and smart grid networks. The implications of this study underscore the potential of blockchain as a critical component of proactive and adaptive cybersecurity systems. However, overcoming existing challenges requires further development of hybrid frameworks that integrate blockchain with data analytics and machine learning technologies. In addition, efforts should focus on standardizing global security protocols to enhance interoperability and creating robust, diverse ransomware datasets to support more accurate detection systems. Future research should also explore methods to minimize latency and improve blockchain efficiency in real-time cybersecurity applications.

Modern cellular tissues have evolved into cell tensegrity structures, where cell strength and stability are established through supportive interactions. Tensegrity improves the efficiency of frequency spectrum use and signal quality in cellular networks. Research shows miRNAs play an important role in the regulation of gene expression in the immune system, providing new insights into the complexity of modern biological systems. Structural Design of Cellular Tissues involves computer simulation studies and laboratory experiments on cell tensegrity. Immune Regulation by MicroRNAs includes analysis of MiRNA gene expression and functionality using technologies such as microarrays and high-throughput sequencing. Data Integration includes identifying microRNA gene targets and understanding transcriptional regulation through methods such as reporter gene assays and Chromatin Immunoprecipitation to predict cellular responses in clinical scenarios. The complex interplay between cellular tensegratas mechanisms and immune regulation by microRNAs shows how widespread their interactions are in human biological systems. The cellular tensegratas structure helps improve intercellular communication, while immune regulation by microRNAs ensures the body's proper response to pathogens. Recent research on microRNAs has exposed their role in the development and function of mammalian immune cells. MiRNAs play an important role in the development and function of mammalian immune cells. The complex interactions between cellular tensile mechanisms and immune regulation by microRNAs demonstrate how structural and functional integration can improve the performance of human biological systems. Integration of these data provides a complete picture of the complexity of modern biological systems and their clinical implications in the diagnosis and treatment of immunological diseases.

Skin cancer (SC) is a highly serious kind of cancer that, if not addressed swiftly, might result in the patient’s demise. Early detection of this condition allows for more effective therapy and prevents disease development. Deep Learning (DL) approaches may be used as an effective and efficient tool for SC detection (SCD). Several DL-based algorithms for automated SCD have been reported. However, more efficient models are needed to improve accuracy. As a result, this paper introduces a new strategy for SCD based on Grey Wolf optimization (GWO) methodologies and CNN. The proposed methodology has four stages: preprocessing, segmentation, feature extraction, and classification. The proposed method utilizes a Convolutional Neural Network (CNN) to extract features from Regions of Interest (ROIs). CNN is employed for feature categorization, whereas the GWO approach enhances accuracy by refining edge detection and segmentation. This technique utilizes a probabilistic model to accelerate the convergence of the GWO algorithm. Employing the GWO model to optimize the structure and weight vectors of CNNs can enhance diagnostic accuracy by a minimum of 5%, based on evaluation outcomes. The application of the proposed strategy and its performance comparison with other methods indicate that the proposed method with GWO predicted SC with an average accuracy of 95.11% and without GWO an Accuracy of 92.66%, respectively, enhancing accuracy by a minimum of 2.5% when we train our model with GWO.

This research was inspired by Bumdesa Podo Makmur, which is an initiative of the village government to improve the community's economy, including educating the public about BUMDes, planting and raising various types of livestock, and running trade and other businesses. There are still individuals who have not joined and taken advantage of BUMDes institutions even though business units are available. The qualitative research methodology used. Data collection methods include documentation, interviews, and observations. Data analysis methods include data reduction, data visualization, data validation, and conclusion drawn. The results of the research are 1) The efforts of the Podo Makmur Kandangan bumdesa in the livestock unit in Kandangan Village, namely improving the quality in the management structure and management of bumdes so that the performance of bumdes managers is getting better. Increasing collaboration with farmer organizations and strengthening social capital capacity, especially in terms of trust and networks to develop livestock business units, is very important in addition to strengthening the management structure of BUMDes. The next step is to try to increase farmers' trust in Bumdes by making the company more open, honest, and honest. 2) The contribution of the Podo Makmur Kandangan bumdesa through the livestock unit to improve the economy in Kandangan Village by carrying out its programs to improve the business units run, especially the livestock unit; helping the community through education, guidance and business improvement training provided by Budes every two months to ensure the proper running of business programs; Assists in organizing, managing, repairing, and supervising the enclosure. This helps goat farms breed and produce livestock products that can be used by many people.

The history of the spread of Islamic education in the early days in Indonesia is an integral part of the process of Islamization of the archipelago that has been going on since the 13th century. Islamic education not only functions as a means of spreading religion, but also as a medium for forming the social, cultural, and intellectual character of the community. This process is carried out through various channels, including the role of ulama, Islamic boarding schools, surau, and mosques as centers of education. This study aims to analyze the dynamics of the process of spreading Islamic education in the early days, by examining various supporting factors such as trade routes, cultural acculturation, and the involvement of local communities. A literature review approach is used to explore relevant literature, including historical manuscripts, current research, and other academic documents. The findings show that the success of Islamic education in the early days cannot be separated from the strategy of cultural adaptation, teaching based on local traditions, and the support of the diaspora ulama community in various regions. Previous research, such as that conducted by Azra, emphasized the importance of the ulama network in Southeast Asia and the contribution of traditional educational institutions in building an inclusive and sustainable Islamic education system. The conclusion of this study confirms that Islamic education in the early days in Indonesia played a significant role in shaping the social structure and religious identity of the community. To strengthen its relevance, further study is needed on the transformation of the early Islamic education system and its implications for the challenges of modern education in Indonesia.

Advances in information technology have encouraged government agencies to implement electronic-based systems to improve the effectiveness, efficiency, and transparency of services. The Community and Village Empowerment Service of Magetan Regency uses the Presence and Performance Application System Si Apik application to record the attendance of State Civil Apparatus as part of the E-Government program. This study aims to evaluate the usability of the Si Apik application using the System Usability Scale method to measure the level of user satisfaction. The research approach involves descriptive qualitative and quantitative methods, with data collection through observation, SUS questionnaires, and semi-structured interviews. The results showed an average SUS score of 52.59, which is in the marginal category, with a grade D and an "OK" rating. This score indicates that the system still has technical constraints, such as network connections and application errors, which affect ease of use. Recommendations for improvement include optimizing features, increasing system stability, and providing clearer technical guidance. This study provides strategic input for the Community and Village Empowerment Service of Magetan Regency in improving the quality of Si Apik application services.