Background of study: The evolution of cloud computing has transformed server management, yet many management interfaces remain web-based and are not optimized for mobile devices. aaPanel, a popular server control panel, also faces this challenge of providing a mobile-friendly monitoring experience.

Aims and scope of paper: This research aims to design and build a mobile application named "aaPanel Mobile" to efficiently monitor vital server statistics on Android devices, addressing the gap in mobile accessibility for aaPanel users.

Methods: The software development follows the Waterfall model, covering requirements, design, development, testing, deployment, and maintenance. The application was built using Expo React Native, integrates with the aaPanel API for data retrieval, and its functionality was verified through black-box testing.

Result: Testing results confirmed that all primary functionalities were successfully implemented. The application correctly displayed resource usage statistics, provided real-time data updates, and listed websites as expected.

Conclusion: The "aaPanel Mobile" application has met its design and functional objectives, proving to be a viable tool ready for use in a production environment to help administrators monitor servers from anywhere.

Background of Study: Big data visualization and visual analytics are essential in social media analysis because they help process large and complex data into more understandable information. With this technique, we can identify patterns, trends, and relationships in social media data, such as user interactions and social influences, that are difficult to analyze without visualization tools. This allows for faster, more accurate, and in-depth analysis, and helps with data-driven decision-making in a variety of areas.

Aims and Scope of Paper:  The purpose of this paper is to review the literature related to big data visualization and visual analytics in the context of social media, using bibliometric analysis methods to identify current trends, techniques used, and important tools.

Methods: This study used a bibliometric design to analyze the literature in the Scopus database with three main keyword combinations: "Big Data Visualization" AND "Social Media Analysis", "Visual Analytics" AND "Bibliometric Analysis", as well as "Data Visualization" AND "Social Media Analytics". The literature analyzed consisted of journals published between 2015 and 2025.  After data collection, filtering is carried out using OpenRefine to eliminate bias and duplication, ensure the accuracy and validity of the data, resulting in objective insights into trends in data visualization and social media analytics.

Results: A summary of key findings on the most widely used techniques and tools in big data visualization on social media.

Conclusion: Closing on the contribution of bibliometric analysis in understanding the development of big data visualization in social media research.

Background of study: IoT enabled systems such as mobile phones, smart homes, and medical applications play a significant role in improving convenience and usability in everyday life. The combination of IoT networks with graphical user interface designs, including websites and online applications, continues to grow rapidly. Despite this progress, interactive usability challenges remain, especially in aligning IoT technologies with human centered interaction principles.

Aims and scope of paper: This study explores the usability of interactive design and identifies new attributes that contribute to improving user experience in IoT enabled systems. It aims to classify logical and physical attributes that influence usability and to propose new interactive dimensions for IoT based design.

Methods: The study uses a descriptive and analytical approach based on a review of relevant literature and conceptual analysis. Logical and physical usability attributes were examined to understand their roles in interactive design and user experience across IoT environments.

Result: The results reveal that two major factors, logical and physical attributes, are fundamental to interactive design. These attributes can guide new rules for IoT based usability, expanding human computer interaction beyond traditional interfaces such as keyboard, mouse, and screen, and including motion, gaze, and posture as part of new interaction mechanisms.

Conclusion: This study focuses on interface usability to help developers create more attractive and user friendly designs. The findings open new research areas in interactive IoT systems and contribute to the development of adaptive, efficient, and human oriented interface design principles for future IoT applications.

Background of study: The rapid development of IoT-enabled systems has transformed user interaction by enabling intelligent, responsive, and interconnected digital environments. However, existing studies often emphasize traditional usability factors while overlooking emerging interaction attributes essential for next-generation Digital Twin and IoT-based interfaces.

Aims and scope of paper: This study aims to investigate next-generation Digital Twin user experience (UX) by exploring interactive IoT design attributes, including gesture-based interaction, gaze tracking, multimodal interfaces, and AR-assisted usability. The research also develops an enhanced usability framework that integrates efficiency, cognitive load, and user satisfaction metrics.

Methods: Using a mixed-method approach, the study integrates quantitative evaluations (task completion time, error rates) and qualitative assessments (NASA-TLX, SUS). Data were collected from open-source IoT usability datasets and supported by prototype testing, including touch-based, voice-assisted, gesture-controlled, and AR-enhanced interfaces.

Result: Findings show that AR-enhanced and touch-based interfaces significantly improve task efficiency, reduce cognitive load, and increase user satisfaction. Gesture-based systems, while offering immersive interaction, exhibit higher error rates and cognitive strain. Users also expressed concerns regarding data security and interface complexity in IoT-enabled environments.

Conclusion: IoT-enabled Digital Twin interaction offers substantial improvements in usability and engagement, particularly through AR and touch-based designs. However, challenges persist in gesture accuracy, voice recognition consistency, and privacy risks. This research establishes a structured framework for future IoT-UX development, emphasizing adaptive, intuitive, and user-centered design principles.

Background of study: In the digital era, football clubs increasingly adopt mobile applications to enhance fan engagement and service delivery. The PERSIS Solo application is a new digital innovation that provides official club information, ticket sales, and merchandise services. However, there has been no prior evaluation of user acceptance, which is crucial for ensuring the successful adoption and continued use of such technology.

Aims and scope of paper: This study examines the determinants of user adoption of the PERSIS Solo mobile application through the application of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) framework. There are several influences of key factors examined in this study, they are performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habitual behavior on user acceptance, while taking into account the moderating role of demographic variables such as age, gender, and user experience.

Methods: A quantitative approach was employed with 150 active users of the PERSIS Solo application as respondents. Data were examined using Structural Equation Modeling with Partial Least Squares (SEM-PLS) in SmartPLS to assess the validity and reliability of constructs as well as the relationships between variables.

Results: Out of 25 tested hypotheses, six were supported. Social influence, hedonic motivation, and habit significantly influenced Behavioral Intention, while facilitating conditions, habit, and Behavioral Intention significantly affected Use Behavior.

Conclusion: The study concludes that hedonic motivation and habit are dominant predictors of user acceptance and actual usage. The findings provide empirical insights for improving mobile applications in sports organizations and contribute to understanding digital fan-engagement systems in Indonesia.