Identity and Access Management (IAM) is critical for securing digital assets, particularly in financial technology (FinTech) systems, where unauthorized access can lead to significant financial losses. Three formal research questions guide this work: (RQ1) Do AI-driven models statistically significantly outperform traditional rule-based IAM systems in anomaly detection accuracy? (RQ2) Which AI model best balances precision and recall for real-time insider-threat detection under class-imbalanced IAM log conditions? (RQ3) Are the observed performance gains robust and stable across cross-validated experimental folds? This study evaluates the performance of AI-driven anomaly detection models, including autoencoders, random forests, and support vector machines, in detecting unusual user activities and potential insider threats. The Autoencoder model achieved the highest overall accuracy of 94.2% (+/- 0.8% across five-fold cross-validation) with a precision of 92.8% and recall of 91.5%. The Random Forest attained a slightly lower accuracy (92.5%) but excelled in recall (93.2%), highlighting its strength in identifying actual malicious activities. Compared to traditional rule-based IAM methods, which achieved only 78.4% accuracy, AI models significantly improved anomaly detection, particularly for subtle or previously unseen threats. McNemar's tests confirm that all accuracy improvements over the baseline are statistically significant (p < 0.001). The Autoencoder also demonstrated the lowest latency (120 ms), making it suitable for real-time deployment. These results confirm that AI-enhanced IAM systems can effectively strengthen security and operational efficiency in FinTech environments, within the scope of the simulated and publicly available datasets employed in this study.

This research aims to develop and assess an information system for managing daily operations in the palm oil plantation industry, specifically for PT Kaltim Utama Plantation I. As a crucial part of Indonesia's economy, the palm oil sector faces numerous operational challenges, such as tracking activities, monitoring resources, and generating accurate reports. The designed system focuses on enhancing processes like spraying, land clearing, fertilizing, and harvesting. The study utilizes the System Development Life Cycle (SDLC) methodology, following the waterfall model, which includes the stages of analysis, design, implementation, testing, and maintenance. In the analysis phase, system requirements were identified to meet operational demands. The design phase involved creating workflows and data
structures to represent plantation operations. The outcome is a web-based information system prototype that streamlines management processes, centralizes data, and improves reporting precision. The system is aimed at simplifying information retrieval, increasing operational efficiency, and aiding decision-making. The implementation is expected to address operational inefficiencies at PT Kaltim Utama Plantation I by reducing manual processes. Additionally, the system includes data visualization tools to quickly evaluate plantation performance and offers scalable features for future growth.

Edge computing has emerged as a critical paradigm for enabling low-latency, bandwidth-efficient, and scalable data processing in distributed IoT environments. However, its effectiveness fundamentally depends on how data is cached, stored, aggregated, and fused across heterogeneous and resource-constrained edge nodes. To address this, the present survey conducts a comprehensive and methodologically rigorous examination of data-management techniques in edge computing. An initial corpus of 150 publications was collected from major scientific databases and processed through the PRISMA framework, resulting in 25 high-quality surveys that revealed data management as the most fragmented and underdeveloped component of the edge ecosystem. Building on these insights, we performed an in-depth analysis of 75 state-of-the-art research papers published between 2018 and 2025, covering four core data-management pillars: data caching, data storage, data aggregation, data validation and data fusion. For each area, we synthesize current design strategies, highlight measurable performance outcomes, and critically evaluate architectural, algorithmic, and system-level limitations. A unified cross-technique analysis further reveals unresolved challenges in scalable data placement, coded storage, privacy-preserving aggregation, multi-modal fusion, and the absence of integrated data pipelines. The survey concludes by outlining open research directions and proposing a consolidated roadmap toward intelligent, interoperable, and workload-aware data-management frameworks for next-generation edge computing systems.

Information and Communication Technology (ICT) has become central to teaching and learning in higher education, yet effective integration depends on users’ ICT competence and institutional implementation strategies. This study examines the role of ICT literacy in supporting teaching and learning and evaluates whether cultural perceptions significantly influence ICT adoption among undergraduate students at Kaduna State University, Nigeria. A quantitative survey design was employed, with data collected from 150 students and academic staff and analysed using descriptive statistics and the Mann–Whitney U test. The findings suggest that ICT literacy is perceived as positively supporting access to learning resources, communication, and classroom engagement. However, statistical tests did not reveal significant differences between respondent groups, indicating broadly similar perceptions among students and staff. While respondents acknowledged cultural considerations, these factors did not exert a substantial influence on ICT acceptance or use within the institutional context. The results suggest that ICT literacy and institutional support structures play a more immediate role in shaping technology adoption than general cultural perceptions. From a management engineering perspective, the study highlights the importance of ICT infrastructure planning, structured digital skills training, and policy-driven ICT integration in higher education. The findings provide practical guidance for university administrators and education managers seeking to improve the effectiveness and sustainability of ICT-enabled teaching and learning systems. 

Sungai Kunjang is one of the primary land transportation facilities in Samarinda City, East Kalimantan, located on Untung Suropati Street in the Karang Asam Ulu subdistrict. Officially inaugurated on June 24, 1989, by Mayor Waris Husain, it serves multiple transportation modes, including public passenger vehicles (PPV), pioneer services, and intercity routes. Although the station currently provides essential information services—such as departure schedules, route options, fare details, and a basic complaint system—these services are not yet supported by a structured Information Technology (IT) and Information System (IS) framework. The lack of integration hampers service efficiency and the optimization of business processes. This research aims to design an Enterprise Architecture (EA) for the station by applying The Open Group Architecture Framework (TOGAF) Architecture Development Method (ADM). The proposed design focuses on aligning business objectives with IT/IS strategies to improve the delivery of transport information and complaint management services. The resulting blueprint is expected to serve as a strategic reference for developing an integrated information system that enhances decision-making, streamlines operations, and improves service quality at Sungai Kunjang Station. By using selected phases of the TOGAF ADM, the study provides a practical foundation for digital transformation within public transport infrastructure in the region.