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This study explores the role of visual analytics in enhancing decision-making processes within creative industries, focusing on its application to large-scale multimedia datasets. Visual analytics integrates interactive visualization techniques with computational algorithms, enabling users to explore complex datasets intuitively and derive actionable insights. The research centers on the design and implementation of interactive dashboards tailored to the creative sector, particularly film, music, and advertising industries, to facilitate real-time data exploration. The study also investigates the usability of these tools through expert-based evaluations, aiming to assess their effectiveness in supporting informed and timely decision-making. The findings reveal that interactive visualizations significantly improve insight discovery and pattern recognition, enabling decision-makers to uncover hidden trends in large multimedia datasets. However, challenges related to scalability, user acceptance, and real-time processing were encountered during the implementation phase. The research highlights the practical benefits of integrating visual analytics into industry workflows, which include enhanced content creation, audience engagement, and strategic planning. Furthermore, the study identifies key visual analytics techniques such as dynamic dashboards, pattern recognition, data mining, and clustering, which are essential for analyzing multimedia data. The study concludes by emphasizing the potential for wider applications of visual analytics in other sectors, suggesting future research directions to improve tool performance, scalability, and user accessibility, as well as exploring the integration of emerging technologies like artificial intelligence and virtual reality.

The development of information technology encourages organizations to adopt a more efficient, flexible, and secure data management system, especially in the field of financial management that requires high accuracy and reliability. One of the technologies that is widely used is cloud computing, which offers easy access to data and an integrated security system. This article aims to analyze the utilization of cloud technology in improving the security and accessibility of financial management data. The method used in this study is a literature study by examining various scientific sources, books, and online news relevant to the topic of cloud computing and financial data management. The results of the study show that cloud technology is able to improve data security through the implementation of encryption, multi-layered access control, user authentication, and a reliable data backup system. In addition, cloud technology also improves the accessibility of financial data because it allows users to access information in real-time, flexibly, and without location or device restrictions. Thus, the application of cloud technology can be a strategic solution for organizations in improving operational efficiency, data security, and the quality of decision-making in financial management.

Human–Robot Interaction (HRI) systems increasingly rely on data-driven approaches to interpret multimodal sensory inputs and support natural interaction. However, purely neural-based HRI models often suffer from limited interpretability and insufficient context-aware decision-making, which can reduce user trust and adaptability in dynamic interaction scenarios. To address these limitations, this study proposes a hybrid neural–symbolic HRI framework that integrates multimodal neural perception with explicit symbolic reasoning for adaptive and interpretable robot behavior. The proposed system combines deep neural networks for processing visual, speech, and gesture inputs with a rule-based symbolic reasoning layer that models interaction context, user states, and behavioral constraints. A loosely coupled integration strategy enables neural outputs to be transformed into symbolic representations, allowing logical inference to guide action selection while preserving perceptual accuracy. The framework was evaluated through controlled HRI experiments comparing a neural-only baseline with the proposed hybrid configuration across multiple interaction scenarios. Experimental results demonstrate that the hybrid neural–symbolic system significantly improves interaction accuracy, contextual responsiveness, and user satisfaction, while achieving substantial gains in interpretability. These findings indicate that symbolic reasoning effectively complements neural perception by enhancing transparency and context-aware adaptation without compromising performance. The study concludes that hybrid neural–symbolic architectures provide a promising foundation for developing trustworthy, adaptive, and human-centered HRI systems.

This research aims to develop a multiclass flower image classification system using the Convolutional Neural Network (CNN) algorithm with the EfficientNet architecture. The main problem addressed is the difficulty of manual identification of flower species that share high visual similarity. The research stages include collecting 17,299 flower images across 19 classes, performing data preprocessing such as image resizing, pixel normalization, and augmentation, followed by model training using the EfficientNet transfer learning approach. The model was trained for 10 epochs with an 80:20 training-validation data split. The evaluation results show that the model achieved a validation accuracy of 98.05% with a loss value of 0.0968, and an average precision, recall, and F1-score of 0.98. The trained model was then implemented into a web-based application built using the Next.js framework, enabling users to upload flower images and obtain real-time classification results via the Hugging Face API. The system successfully identified flower species with a confidence level of 99.87%. These findings demonstrate that combining a modern CNN architecture with transfer learning provides efficient and highly accurate flower classification performance, which can be effectively implemented for educational and digital conservation purposes.

This study aims to analyze and prove the influence of Product Quality, Price Perception, Brand Trust, and Informal Communication between Consumers that takes place through Digital Media on Purchase Decisions for Glad to Glow skincare products among Shopee application users in Surabaya. This study uses a quantitative method by distributing questionnaires to respondents who meet the research criteria. Data analysis techniques used include Descriptive Tests, Instrument Tests, Classical Assumption Tests, and Hypothesis Tests through Multiple Linear Regression with the help of SPSS 26. This study was conducted in 2025. The results of the study indicate that all indicators in the research instrument are declared valid and reliable. Based on the Multiple Linear Regression Test, the variables of Price Perception and Informal Communication between Consumers that take place through Digital Media have a more dominant influence on Purchase Decisions, while Brand Trust shows the lowest and insignificant influence. The results of the T Test show that Product Quality, Price Perception, and Informal Communication between Consumers that take place through Digital Media have a significant influence on Purchase Decisions, while Brand Trust has no significant influence. The F Test confirms that simultaneously the four independent variables have a significant influence on Purchase Decisions. The results of the Coefficient of Determination (R²) test indicate that the independent variables are able to explain most of the variation in Purchase Decisions, while the remainder is influenced by other variables outside the study.

Despite its cultural potential, Wetabar Village has not been supported by effective digital promotional media. Information access remains limited, and the lack of interactive promotional platforms has contributed to the low interest of tourists. Current promotional efforts rely heavily on conventional methods, resulting in decreased visitor numbers. The implementation of digital technologies such as Augmented Reality (AR) and Web-based 360° Virtual Reality Tours is considered an effective approach to increasing tourist engagement by offering immersive exploration experiences and interactive cultural content. This research aims to design and implement a web-based tourism promotion media that integrates local cultural content with AR and 360° virtual tour technologies. The development process follows the MDLC research method, utilizing the Lumi H5P platform and PHP for system implementation. The resulting system enables users to digitally explore Wetabar Village through panoramic views and interactive multimedia features. The findings show that the developed application runs smoothly on both Android devices and laptops and is capable of providing hotspot access to operate the virtual reality features. This innovation is expected to enhance tourist interest while supporting cultural preservation and promoting digital transformation within the tourism sector of West Sumba.

This study analyzes multi-stakeholder collaboration in innovation-based regional development planning at the Regional Development Planning Agency (Bappeda) of Sidoarjo Regency, emphasizing institutional mechanisms, data integration and digital innovation, joint financing schemes, and actor capacity and participation dynamics. The research focuses on the need to improve the effectiveness of development planning through planned, participatory, and data-driven intersectoral collaboration. Applying a qualitative case study approach, information was collected through in-depth interviews, participant observation, and analysis of planning documents. It was then analyzed using data reduction, thematic grouping, and source triangulation techniques. The research findings indicate that institutional synergy has been realized through formal forums such as development planning meetings and group discussions. However, coordination remains suboptimal due to overlapping roles between regional government agencies and the lack of a sustainable collaborative mechanism. Digital data integration still faces challenges due to limited platform content and user technical capabilities, resulting in data utilization not fully supporting evidence-based decisions. In terms of financing, collaborative schemes have begun to be implemented but are inconsistent, particularly in combining funding sources from various sectors. The capabilities of actors show striking differences, which impact the quality of public engagement and the efficiency of facilitating the planning process. Overall, these results confirm that the effectiveness of innovative planning is largely determined by the integration of digital systems, strengthening collaboration between stakeholders, and enhancing institutional capacity. This study suggests designing a more organized, interactive, and long-term multi-actor synergy model.

This study aims to analyze the influence of relationship marketing and e-servicescape on customer loyalty of Tokopedia users in Medan City. The rapid development of digital technology encourages Tokopedia to build long-term relationships with customers while creating a convenient and secure online shopping experience. This research employed a quantitative method with an associative approach, using data collected through questionnaires distributed to 100 Tokopedia users in Medan City and analyzed using multiple linear regression. The results show that relationship marketing has a positive and significant effect on customer loyalty, where effective communication, responsive services, and loyalty programs such as Tokopedia Points increase customer trust and emotional attachment. In addition, e-servicescape also has a positive and significant effect on customer loyalty through an attractive interface, easy navigation, and reliable transaction security. Simultaneously, both variables make a substantial contribution to building customer loyalty toward Tokopedia. Thus, effective relationship marketing strategies and high-quality digital environments are crucial factors in maintaining customer loyalty and enhancing Tokopedia’s competitiveness in the increasingly intense e-commerce industry.

This study aims to analyze the influence of terminal users’ perceptions on business development opportunities at Mangkang Type A Terminal, Semarang City, managed by the Central Java Class I Transportation Management Agency (BPTD). As a major transportation hub, the terminal functions not only as a passenger transit point but also as a strategic economic area with potential for commercial facility development. However, the utilization of business opportunities at Mangkang Terminal remains suboptimal, necessitating an evidence-based analysis of the factors that shape these opportunities. This research employs a quantitative approach using a survey method involving terminal users, including passengers, public transport drivers, and terminal visitors. Data were collected using a Likert-scale questionnaire and analyzed through multiple linear regression to examine the influence of user perceptions on business potential, which includes interest in facility usage, demand for additional services, and preferences for commercial space utilization. The findings indicate that perceptions related to comfort, safety, cleanliness, accessibility, and service quality significantly affect business development opportunities. Comfort and service quality emerge as the most dominant factors driving user interest in commercial facilities. These results emphasize that improving terminal service quality can serve as an effective strategy to expand business opportunities and enhance non-tax revenue.

This study conducts a Systematic Literature Review (SLR) to explore the impact of users’ mental models on the implementation of Multi-Factor Authentication (MFA) as a strategy for mitigating password guessing risks in organizational environments. Amid the growing landscape of cyber threats, single-factor authentication has proven to be vulnerable, making MFA an essential layered security solution. However, the adoption of MFA remains slow. Existing studies indicate that expert users perceive MFA as a useful additional layer of verification, whereas non-expert users often view it as a burdensome task (a chore) and may even struggle to distinguish between different types of MFA. Dependence on mobile devices emerges as a common source of frustration for both groups. These findings emphasize that understanding users’ mental models is crucial for improving the implementation and usability of MFA. Innovations such as adaptive MFA or Single Input Multi-Factor Authentication (SIMFA) show potential as solutions to balance security requirements and user experience.