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The rapid advancement of digital communication technologies has transformed how individuals interact and build communities online. This study aims to analyze and synthesize previous research on digital social network modeling as a framework for predicting interaction and solidarity within online communities. Using a literature review approach, this research examines studies published between 2012 and 2025 that focus on social network modeling, online interaction analysis, and digital solidarity prediction. The findings reveal that integrating structural data, content, affective factors, and community evolution provides a comprehensive understanding of online social dynamics. Key variables influencing digital solidarity include member engagement, mutual trust, information flow, and the quality of interpersonal relationships. Furthermore, predictive models based on social network analysis (SNA) have proven effective in identifying changes in interaction patterns and assessing the strength of social bonds within digital environments. This study concludes that digital social network modeling serves as an essential tool for strengthening online community management strategies, fostering inclusivity, cohesion, and productivity in the era of Society 5.0.

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.

The rapid development of information and communication technology has driven the need for information services that are more relevant and adaptive to user behaviour. This research aims to integrate data analytics in the study of user behaviour to support the development of effective information services. The dataset used is Kaggle's Online Retail Dataset, which includes sales transaction data of online retail companies in the UK from December 2010 to December 2011. The analysis was conducted through customer segmentation using K-Means Clustering algorithm and predictive analysis with Association Rule Mining. The segmentation results successfully grouped customers into four main clusters, namely loyal customers, potential customers, passive customers, and low-spending customers. Model evaluation showed optimal performance with an accuracy rate of 85%, precision of 82%, recall of 78%, and F1-Score of 80%, and Silhouette Score of 0.62, indicating effective customer segmentation. The findings prove that the application of data analytics can provide deep insights into customer behaviour and support the development of more personalised and adaptive information services. This research is expected to be a reference in designing data-driven information service development strategies in various sectors.

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.

Background: Digitalization is fundamentally reshaping health financing, enabling more efficient management of healthcare resources, improving service delivery, and increasing accessibility. This systematic review explores the intersection of digital tools and financial health systems, examining their transformative potential.Objective: The primary aim of this review is to identify the impact of digitalization on healthcare financial management, highlighting its benefits and addressing the challenges that arise during its implementation.Methods: A systematic review of 10 studies was conducted, focusing on digital health financing and employing PRISMA guidelines to ensure rigorous selection and analysis. The data extraction process identified thematic relevance, methodological rigor, and contextual insights.Results: The findings reveal that digitalization enhances resource allocation, patient accessibility, and administrative efficiency. Technologies such as blockchain and artificial intelligence optimize transparency and predictive financial modeling. However, significant challenges include data security vulnerabilities and the integration of digital tools with legacy systems.Conclusion: Digital technologies present transformative potential for healthcare financing. However, strategic implementation, robust governance, and cross-sector collaboration are critical to overcoming challenges and maximizing the benefits of digitalization. By addressing these needs, digitalization can create sustainable, inclusive, and equitable healthcare systems for the future.

This research aims to develop a Geographic Information System (GIS) for mapping and analyzing agricultural land in Deli Serdang Regency, North Sumatra. The system was designed using the Rapid Application Development (RAD) method to speed up the development process and ensure the system meets user needs. The research results show that GIS is able to provide effective solutions to agricultural data management problems, such as mapping harvest area, potential empty land, and distribution of production results. in real time. With an easy-to-use interface, this system supports efficient crop distribution, data-based policy development, and sustainability of the agricultural sector. This research suggests developing additional features such as predictive analysis, user training, and regular evaluation to optimize the benefits of GIS in supporting sustainable agricultural development.

Customer satisfaction is a crucial indicator in assessing the quality of a company's products, services and overall experience. This research aims to identify the level of customer satisfaction and optimize the available data for effective use in sentiment analysis. In this study, we analyzed 4,353 customer reviews collected over the past year, with 3,481 reviews used as training data and 871 reviews as testing data. The analysis process was conducted using the Cross-Industry Standard Process for Data Mining (CRISP-DM) approach and leveraged the Logistic Regression algorithm to build a predictive model. Model evaluation using the confusion matrix yielded an accuracy of 94.60%, a precision of 94.26%, and a recall of 94.60%. The analysis was conducted using Jupyter Notebook and the Python programming language. The results indicate that sentiment analysis is effective in identifying and predicting customer satisfaction levels, which in turn can help a company’s products improve its service strategies. The optimization of previously underutilized data now provides deeper insights into customer perceptions and expectations, enabling the company to make more targeted decisions and enhance overall customer satisfaction.

Customer satisfaction is a crucial indicator in assessing the quality of a company's products, services and overall experience. This research aims to identify the level of customer satisfaction and optimize the available data for effective use in sentiment analysis. In this study, we analyzed 4,353 customer reviews collected over the past year, with 3,481 reviews used as training data and 871 reviews as testing data. The analysis process was conducted using the Cross-Industry Standard Process for Data Mining (CRISP-DM) approach and leveraged the Logistic Regression algorithm to build a predictive model. Model evaluation using the confusion matrix yielded an accuracy of 94.60%, a precision of 94.26%, and a recall of 94.60%. The analysis was conducted using Jupyter Notebook and the Python programming language. The results indicate that sentiment analysis is effective in identifying and predicting customer satisfaction levels, which in turn can help a company’s products improve its service strategies. The optimization of previously underutilized data now provides deeper insights into customer perceptions and expectations, enabling the company to make more targeted decisions and enhance overall customer satisfaction.

In today’s date Artificial intelligence (AI) has substantially transformed marketing strategies and specifically Viral Marketing by enhancing the content personalization, targeting the audience and real time campaign optimization. The study explored the Artificial Intelligence impact on Viral marketing with a comprehensive review of 20 literatures that highlights the diverse applications of AI such as predictive analytics, natural language processing (NLP) and AI-driven visual content creation. This study employed meta analysis approach to evaluate how effectively AI could boost marketing reach, engagement and return on investment (ROI). The finding of the study indicates a positive correlation between the efficiency of Viral Marketing campaigns and the integration of AI, despite the fact highlighting ethical and transparency. The study concludes with practical suggestions for using AI in Viral marketing in a responsible and efficient manner to enhance its potential while mitigating related dangers. This study also highlights AI’s revolutionary role in changing market dynamics.

Natural disasters such as earthquakes, hurricanes, and floods pose significant risks to critical infrastructure. AI-driven disaster response systems provide real-time analytics, predictive modeling, and automated response strategies to mitigate damage and improve recovery efforts. This paper explores how AI-powered drones, satellite imagery, and sensor networks enhance disaster monitoring and decision-making. Additionally, the study discusses the role of AI in optimizing emergency resource allocation and predicting infrastructure vulnerabilities. Through an analysis of past disaster management strategies, this research aims to propose AI-integrated frameworks that enhance disaster preparedness and resilience.

Natural disasters such as earthquakes, hurricanes, and floods pose significant risks to critical infrastructure. AI-driven disaster response systems provide real-time analytics, predictive modeling, and automated response strategies to mitigate damage and improve recovery efforts. This paper explores how AI-powered drones, satellite imagery, and sensor networks enhance disaster monitoring and decision-making. Additionally, the study discusses the role of AI in optimizing emergency resource allocation and predicting infrastructure vulnerabilities. Through an analysis of past disaster management strategies, this research aims to propose AI-integrated frameworks that enhance disaster preparedness and resilience.

PT XYZ is a company that produces various gases in the form of Oxygen, Nitrogen, and Argon in gas and liquid form. In the production process, the machine runs 24 hours non-stop and will stop production when maintenance is employed once a year. The Air Separation Plant (ASP) machine performance measurements are needed to determine the effectiveness of its production. The reseacrh aims to analyze the factors that affect machine productivity. The method used is quantitative, using the Overall Equipment Effectiveness (OEE) approach. In addition, this study uses interviews to determine the factors that affect engine performance. The results show that the OEE value of the machine is 75%, which is still below the international standard OEE value of 85%. Therefore, it is necessary to optimize engine performance. Based on the analysis using a fishbone diagram, there are five influencing factors: human factors, machines, work methods, environment, and materials. Based on these five factors, the engine factor most influences the low value of engine performance. The predictive maintenance calculations are conducted, especially on expender and cold box machines, every 95 days of machine should use with work efficiency of around 40 hours.

The advancement of information technology has transformed the management of academic systems in universities, necessitating effective and efficient IT management to support operational and administrative processes. Universitas Amikom Purwokerto uses a web-based academic system to integrate academic and administrative services. However, issues such as server connection failures often disrupt operations. Based on an ITIL V3 analysis, the system is at Maturity Level 3 (*Defined*) in problem management, with recommendations to improve real-time monitoring and process flexibility. Implementing these recommendations is expected to enhance service quality and user experience. This study aims to analyze the service quality of Universitas Amikom Purwokerto’s academic system within the service operation domain, particularly in the problem management subdomain. According to the ITIL V3 framework, the system's maturity is at the *Defined* level (Level 3), indicating that problem-handling processes are well-documented but limited in flexibility. These findings highlight the need for improvements in real-time monitoring, predictive analysis, and user feedback to better meet dynamic needs. Such recommendations are expected to improve operational effectiveness and overall user experience.    

The purpose of this study is to develop a predictive model to predict the trend of the emas price over time and to investigate how this affects important economic variables. As a popular commodity that is regarded as a safe refuge for investments, changes in the price of emas can have a significant impact on a number of economic indicators, such as inflation, consumer spending, and investment decisions. Compiling historical data on emas prices, macroeconomic variables, and other related topics is a component of research methodology. Regression analysis and ARIMA modeling are two methods of deret waktu analysis that can be used to create an andal model of problems. The model's predictive accuracy is then determined by using appropriate statistical metrics. This study's findings provide new and important information about the factors influencing changes in the price of emas and how they affect the economy as a whole. The model that is being used can be used by investors, financial institutions, and policymakers to predict gold price movements and make informed decisions to mitigate risks and capitalize on opportunities. The implications of this research extend all the way to the emas market since it increases our understanding of the close relationship between commodity prices and economic dynamics. The knowledge gained can help create more comprehensive investment and economic policies, which will eventually affect the stability of the economy and economic growth.

Energy efficiency has become a critical focus in manufacturing plants due to rising operational costs and increasing environmental concerns. The growing importance of energy management is driven by the need to reduce energy consumption, lower emissions, and enhance overall operational efficiency. Traditional maintenance practices, such as reactive and preventive maintenance, often lead to unnecessary downtime, high repair costs, and inefficient energy usage. In contrast, predictive maintenance (PdM), supported by Internet of Things (IoT)-enabled sensor networks, offers a proactive approach to minimizing energy waste by predicting equipment failures before they occur. This study develops a predictive maintenance framework using IoT-based sensor networks to optimize energy usage and reduce energy losses in manufacturing plants. The research begins with an overview of IoT sensor network architectures and their applications in industrial automation, including sensors such as temperature, vibration, and pressure sensors. It explores predictive analytics techniques, such as machine learning and artificial intelligence, used for failure prediction, which are key to enhancing energy efficiency. The study emphasizes how predictive maintenance contributes to industrial sustainability by reducing carbon footprints and optimizing energy consumption. The research methodology involves the installation of IoT sensors in critical machinery, real-time data analysis using machine learning algorithms for failure prediction, and energy consumption measurement before and after implementing IoT-based interventions. The results show significant improvements in energy consumption efficiency and operational productivity. Predictive maintenance led to reduced unplanned downtime, increased equipment reliability, and a more sustainable manufacturing process. However, challenges such as sensor integration, initial setup costs, and data security concerns were identified. The study concludes with recommendations for integrating IoT-based predictive maintenance systems into manufacturing plants to further optimize energy usage and promote sustainability.

This article explores the utilization of Artificial Intelligence (AI) as a tool for enhancing efficiency in public communication within the BANI era (Brittle, Anxious, Nonlinear, Incomprehensible). The aim of this research is to investigate how AI can improve the effectiveness of public communication amidst uncertainty and complexity. The research employs a descriptive qualitative approach with a literature review, analyzing data from relevant journal articles, books, and case studies. The study finds that AI plays a crucial role in addressing system fragility through misinformation detection, reducing public anxiety by providing personalized and responsive information, and managing uncertainty and complexity through predictive analysis and data simplification. The results indicate that AI can enhance the efficiency and clarity of public communication but must be complemented by stringent regulations and ethical considerations to ensure responsible use. With the right approach, AI can be an effective tool in improving public communication in this challenging era.

The aim of the study is to delve into the disclosure of future financial statements and its reflection on the sustainability of the banking sector by extrapolating and reporting the pillars of banking sustainability. It adopting a mechanism at a high level in the country to promote data technology and increase support for its resources in order to provide an appropriate structure for the development of the banking and financial sector by providing appropriate information at the right time and expanding the preparation of future studies and research and linking them to many variables that are considered influences faced by the Iraqi financial market in the contemporary business environment for providing a suitable environment in the capital market by supporting the banking and financial sector by means of modern technology and obliging companies within the sectors. The study's goal was accomplished by using the content analysis method for the annual reports of banking units listed in the Iraqi financial market for the years 2020–2023. This was done after developing an indicator to gauge future financial statements' level of disclosure in compliance with the Iraqi Financial Market Law and the disclosure guidelines issued in accordance with it, as well as the fact that the index is being used in the country for the first time. The study draws several conclusions, the most important of which being that , One of the most notable recommendations reached by the study is to require the banking units listed in the Iraqi financial market to display future financial statements in order to ensure the achievement of sustainability supporting the banking sector within the annual reports. The level of disclosure of future financial statements varied in the financial reports of the banking units listed in the Iraqi financial market, where the average disclosure was high with a positive moral relationship to the market financial value.

This work investigated how artificial intelligence (AI) and cloud computing are related to cost control in smart manufacturing. It aims to show how these technologies improve manufacturing environments' decision-making and cost efficiency for better management accounting. The study thoroughly examined the literature for the qualitative research methodology. The cloud computing benefits from lowering operating costs, the use of AI in predictive maintenance, and the incorporation of these technologies into management accounting systems are only key area and trends from the thematic analysis. According to the results, offering scalable and flexible computing resources enabled companies to quickly adjust to shifting demands of the market, cloud computing greatly reduced costs. Yet, the study also emphasized the difficulties in the management of the resources, such as the possibility of inefficiencies and higher expenses due to inefficient resource distribution. Also, AI technologies enhance the efficiency and accuracy of accounting procedures, freeing up professionals for the concentration on strategic duties such as financial analysis and decision support. The report suggested that to reduce and avoid inefficiencies, businesses should carefully manage their cloud resources. For the improvement of operational efficiency and decision-making, businesses were required to include AI-driven solutions into their management accounting systems. The study also found that enterprises should give priority to implementing cloud computing and AI technologies to stay competitive in the quickly shifting smart manufacturing market. These technologies provide a substantial area for innovation in cost management.

This study examines the relationship between life satisfaction and the effectiveness of systemic lupus erythematosus (SLE) treatment through a systematic review and meta-analysis of 32 studies (N=8,746) published between 2018 and 2023. The analysis reveals a moderate negative correlation between life satisfaction and lupus disease activity (r=-0.38, 95% CI: -0.44 to -0.32, p<0.001). More specifically, the data demonstrate a strong positive correlation between life satisfaction and quality of life in the context of health (r=0.52, 95% CI: 0.47-0.57, p<0.001) and a moderate positive correlation with medication adherence (r=0.34, 95% CI: 0.28-0.40, p<0.001). Longitudinal analysis (n=6 studies) shows that early improvements in life satisfaction are significantly predictive of reductions in lupus disease activity (?=-0.24, p<0.001) and enhancements in quality of life (?=0.29, p<0.001) over six months to 2 years. Meta-regression analysis further identifies age (?=0.008, p=0.03) and disease duration (?=0.015, p=0.01) as significant moderators of the effect of life satisfaction on SLE treatment outcomes. These findings extend previous research by Diener and Chan (2011) on well-being and health and by Mok et al. (2019) on depression in SLE by emphasizing the specific role of life satisfaction in SLE management. The novelty of this study lies in its focus on life satisfaction rather than solely negative risk factors, highlighting the potential for life satisfaction-based interventions in the management of SLE. This research supports the integration of life satisfaction assessments and psychosocial interventions into standard SLE care protocols, offering new insights into the psychoimmunological approach to SLE treatment.

Tropical diseases remain a serious public health challenge in Southeast Asia, particularly malaria, which has high morbidity and mortality rates. The complexity of their spread is influenced by various factors, including climate, environment, and population, requiring a spatially-based analytical approach to understand their distribution patterns. This study aims to develop a regression-based spatial model to predict the spread of tropical diseases and identify hotspots in high-risk areas. The data used include tropical disease case reports from national health agencies, climate data (temperature, rainfall, humidity) from BMKG and WorldClim, and population data (density and mobility) from  BPS and other official sources. The analysis was conducted using a Geographic Information System GIS for spatial mapping, as well as the application of spatial regression models, namely the Spatial Lag Model SLM and Spatial Error Model SEM. The results show that the developed model is able to predict disease distribution with a high level of accuracy, demonstrated by statistical validation through AIC, and Morans I. One of the main findings is the identification of malaria hotspots with a confidence level of 93, as well as the mapping of tropical disease risk predictions covering the Southeast Asian region. These results have significant implications for public health policy, particularly in resource allocation, prevention program planning, and priority area-based interventions. Furthermore, this study recommends the integration of big data and machine learning technologies to enrich predictive models and develop more adaptive early warning systems. Thus, this research contributes to strengthening tropical disease control strategies in Southeast Asia with a comprehensive spatial data-driven approach.