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The attendance process at SMK Swasta Dwitunggal 2 Tanjung Morawa is still carried out manually, potentially causing inefficiency, delays, and inaccuracies in attendance data. This condition results in the difficulty of managing attendance data and less than optimal support for managerial decision-making in the school environment. This study aims to design an Enterprise Architecture (EA) for a school attendance system using the TOGAF Architecture Development Method (ADM) framework. The research method used is a qualitative approach with a case study design. The analysis is carried out through mapping existing conditions (AS-IS) and designing expected conditions (TO-BE) in several TOGAF ADM phases, including business architecture, information system architecture, technology architecture, as well as the Opportunities and Solutions and Migration Planning phases. The results of the study are an enterprise architecture blueprint and a migration roadmap that can serve as guidelines for the gradual implementation of a technology based attendance system. This design is expected to improve the efficiency of attendance management, data accuracy, and support the decision-making process in schools.

This study is motivated by the increasing need for accurate modeling and classification of one-dimensional signal data in intelligent systems. The rapid development of deep learning has led to the adoption of more adaptive and complex neural network architectures capable of capturing both temporal dependencies and local patterns in sequential data. This research aims to analyze and compare the performance of several deep learning models, namely Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid Convolutional Neural Network–GRU (CNN–GRU) model for signal data classification. The research method employs a quantitative experimental approach involving data preprocessing, windowing, model training, and performance evaluation. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the hybrid CNN–GRU model outperforms the other models, particularly in capturing local features and long-term temporal dependencies within signal data. These findings suggest that the integration of convolutional layers and recurrent mechanisms enhances feature representation and learning stability. This study is expected to contribute both theoretically and practically to the development of deep learning models for signal processing and time-series-based intelligent applications.

This study aims to examine the historical legacy of the Sriwijaya and Majapahit Kingdoms in the context of the development of Southeast Asian civilization through a literature review approach. The study focuses on the contributions of both kingdoms in various aspects, including politics, economy, maritime activities, culture, education, and international diplomacy. Research data were obtained from primary and secondary sources, such as historical manuscripts, inscriptions, archaeological reports, and national and international scholarly publications. The findings indicate that the Sriwijaya Kingdom played a significant role as a major maritime power and an international center of Buddhist education that influenced intellectual networks across Asia. Meanwhile, the Majapahit Kingdom was prominent in developing systems of governance, law, and regional integration of the Nusantara through extensive political and trade networks. Both kingdoms not only shaped the social and cultural structures of their respective periods but also laid an important historical foundation for the formation of identity and regional dynamics in modern Southeast Asia. Therefore, the legacies of Sriwijaya and Majapahit remain highly relevant for understanding historical processes, regional integration, and the long-term development of Southeast Asian civilization.

Indonesia, as a country with the highest seismicity in the world, requires an accurate earthquake prediction system through the use of the BMKG earthquake catalogue. This research aims to implement ETL-based data pipeline engineering to process 92,887 earthquake catalog entries for the 2008-2023 period into ready-to-use daily time series for the LSTM seismicity forecasting model. The ETL process includes raw data extraction, cleaning of 97% missing values columns on focal mechanism parameters, datetime conversion, daily resampling producing 5,200 entries with earthquake count, total magnitude, and average magnitude features, as well as Min-Max Scaler normalization for LSTM compatibility. The dataset was processed using Google Colab with a stacked LSTM architecture of two layers of 50 and 25 units, dropout 0.2, Adam optimizer, and a sequence window of 30 days to predict the daily earthquake count. The model trained for 100 epochs shows the ability to capture stable seismic activity trends with a consistent decrease in MSE loss, although it shows deviations in extreme spikes due to aftershock sequences. The ETL pipeline proved crucial in ensuring temporal consistency, 100% data completeness, and relevant physics representation, resulting in a reproducible end-to-end framework for disaster mitigation.

This study aims to analyze the role of Gujarat, India, as a major hub in the dynamics of the Islamization of the Indonesian Archipelago (Nusantara) and its impact on the formation of a distinctive Southeast Asian Muslim identity. This research employs a library research method by examining various primary and secondary sources, including historical manuscripts, travel accounts, archaeological findings, and relevant scholarly books and journal articles. The findings indicate that the arrival and spread of Islam in the Nusantara around the thirteenth century were strongly influenced by Muslim traders from Gujarat, who functioned not only as economic agents but also as carriers of religious and cultural values. The process of Islamization occurred relatively peacefully through trade networks, intermarriage, Islamic education, and political alliances with local rulers. The dominance of Sufistic teachings, which were adaptive and accommodative to local cultural traditions, facilitated a process of cultural inculturation that enabled Islam to be accepted without significant social conflict. This interaction produced a moderate and syncretic form of Islam, reflected in religious practices, local traditions, and Islamic architectural forms across the region. The study concludes that the Islamization of the Nusantara was a continuous process of cultural dialogue that shaped a tolerant, contextual, and harmonious Muslim identity in Southeast Asia, distinguishing it from Middle Eastern Islamic traditions.

The hyper-connected work era has fundamentally blurred the temporal and psychological boundaries between professional and personal spheres, triggering a productivity paradox phenomenon where increased digital connectivity is inversely proportional to long-term cognitive efficiency. Consequently, this research aims to explore deeply and comprehensively the critical roles of cognitive resilience and digital wellbeing strategies as vital preventive mechanisms against the rising risk of burnout resulting from cumulative cognitive fatigue. Employing a qualitative approach with a rigorous literature review design, this study synthesizes extensive secondary literature retrieved from accredited academic databases and global policy reports within the last five years to analyze current trends and theoretical frameworks. The in-depth analysis reveals that cognitive resilience, significantly bolstered by metacognitive digital literacy, functions as an adaptive shield against the mounting pressures of information overload and attention fragmentation. Furthermore, the findings emphatically confirm that sustainable productivity is not achieved through borderless connectivity, but rather necessitates disciplined cognitive energy management and the strict implementation of "right to disconnect" protocols. Concluding the study, it is strongly recommended that organizations integrate comprehensive digital wellbeing policies into their work culture architecture to safeguard long-term employee performance and ensure organizational sustainability amidst the challenges of the attention economy.  

Interoperability remains one of the most persistent challenges in digital health systems globally, affecting the continuity, quality, and efficiency of care. Electronic Medical Records (EMR) managers are central to implementing and sustaining EMR systems, yet their experiences navigating interoperability challenges are poorly understood. This study aimed to explore the lived experiences of EMR managers and to develop a conceptual understanding of the barriers and coping strategies involved. A qualitative phenomenological approach was employed, guided by van Manen’s interpretive framework. The study involved 21 EMR managers from public and private hospitals across three major healthcare networks. Participants were selected through purposive and snowball sampling, with inclusion criteria requiring at least two years of EMR management experience and involvement in interoperability efforts. Data were collected via in-depth, semi-structured interviews conducted virtually between September and November 2025. Each interview lasted 45–60 minutes, was audio-recorded, transcribed verbatim, and member-checked. Data were analyzed thematically using NVivo 14, following Braun and Clarke’s six-step method. Reflexivity, triangulation, and peer debriefing ensured methodological rigor, in accordance with COREQ and SRQR standards. Four overarching themes emerged: (1) Fragmented Systems, reflecting technical and architectural incompatibilities across platforms; (2) Role Strain and Isolation, describing limited authority and institutional support; (3) Adaptive Workarounds, highlighting informal coping strategies such as manual data tracking; and (4) Systemic Barriers, pointing to policy volatility and vendor resistance. A conceptual “feedback loop of fragmentation” was developed, illustrating how these themes interact to perpetuate interoperability challenges. EMR managers operate as critical yet often overlooked actors in the pursuit of health system interoperability. Supporting their role through policy stability, resource investment, and participatory system design is essential. Future digital health reforms must integrate managerial perspectives to build resilient and connected health information systems.

The documentary film “Dari Rasi Jadi Mimpi: Langkah Menjaga Tradisi” was an audiovisual work that portrayed the life and traditions of the Cireundeu Indigenous Community in Cimahi, West Java, with a focus on the consumption of Rasi (cassava rice) as a symbol of food security and cultural identity. The background of this work was based on concerns regarding the gradual marginalization of local traditions due to modernization and changing consumption patterns. This project aimed to document and visually represent the cultural values of the Cireundeu Indigenous Community through an ethical and authentic documentary approach. The production stages consisted of preliminary research, field observation, visual concept development, filming, and post-production. During the production process, the author acted as the Director of Photography (DoP), responsible for visual planning, camera blocking, composition, and lighting strategies adapted to the indigenous environment and technical limitations on site. The results indicated that a visual approach prioritizing authenticity, the use of natural lighting, and visual ethics successfully strengthened the documentary narrative without diminishing its aesthetic value. In conclusion, this documentary functioned not only as an audiovisual work but also as a cultural archive, an educational medium, and a sustainable tool for promoting local culture.

This study aims to carry out a thorough analysis of the use of stylistic elements, particularly in terms of diction and language style, as applied by Pitoyo Amrih to shape the character of Gatotkaca in the novel Sisi Gelap Gatotkaca. The character of Gatotkaca, traditionally depicted as a flawless heroic figure, is reimagined by the author through a more humanistic and reflective linguistic approach. Using a qualitative descriptive method alongside stylistic literary criticism, this article examines how linguistic units within the text serve as rhetorical tools to reveal internal conflicts, existential burdens, and the darker aspects of the main character. The analysis process involves identifying word choices that carry a melancholic tone as well as the prominent use of figurative language, including comparisons, contrasts, and emphatic statements. The findings indicate that the use of archaic and philosophical diction, combined with sharp irony and metaphor, effectively transforms the image of Gatotkaca from merely a symbol of physical strength into a representation of human psychological fragility. This research contributes to literary criticism by highlighting how an author’s language style can alter readers' perceptions of established myths within society's culture.

This study aims to analyze the effectiveness of the internal control system in the Personnel Division of PT Sendang Derma Pesona in supporting effective, efficient, and well-governed human resource management. The background of this research is based on the importance of internal control systems in preventing administrative errors, reducing the risk of fraud, and ensuring compliance with labor regulations, particularly in personnel and payroll functions. This study employs a descriptive qualitative approach using an operational audit method. Data were collected through questionnaires, interviews, and document observation covering recruitment procedures, personnel data management, employee transfers, authorization processes, as well as payroll and bank reconciliation procedures. The results indicate that, in general, the internal control system in the Personnel Division has been implemented adequately, as reflected by complete personnel documentation and multi-level authorization in decision-making processes. However, several weaknesses were identified, including the lack of proper segregation of duties between payroll preparation and salary payment, the absence of regular bank reconciliation, and the continued use of manual personnel archive management. These weaknesses indicate that the internal control system still needs to be strengthened to minimize the risk of errors and improve the efficiency of personnel administration. The findings of this study are expected to provide practical implications for management in improving internal control policies and procedures to support better corporate governance and sustainable organizational performance.

In this era of ongoing digitalization, the use of information technology in records management is becoming increasingly crucial to ensure work efficiency and effectiveness. This study aims to examine the implementation of a dynamic archive storage system in the General Ledger Accounting Division of PT KPN Plantation. Specifically, this study identifies the obstacles encountered and analyzes the efforts made to overcome these challenges. Using a qualitative approach, primary data was collected through observation, interviews, and documentation studies with informants from General Ledger Accounting employees. Data analysis was carried out through a series of processes, including data collection, data reduction, data presentation, and conclusion drawing. The results of the study show that although the stages of archive management have been carried out systematically through a combination of physical procedures and digitization using E-Archive, its implementation still faces significant obstacles. The problems identified include managerial aspects such as high transaction volumes that cause document backlogs and space limitations, technical aspects such as the instability of the E-Archive system, and limited human resources with expertise in archiving. Collectively, these obstacles reduce the effectiveness and efficiency of the system, requiring a comprehensive and integrated improvement strategy to achieve reliable and sustainable archive management

PT Telering Distrindo is a telecommunications distribution and retail company whose operations rely heavily on information systems. As the business expands and transaction volumes increase, higher demands for system security and reliability require a strategic evaluation of information technology (IT) implementation to ensure alignment with business objectives. This study aims to analyze the condition of the IT infrastructure at PT Telering Distrindo and to formulate development strategies using the Ward & Peppard framework. The research methods include literature review, system observation, IT architecture analysis, and interviews with internal stakeholders. The results show that the company has implemented core systems such as aPOSPlus, aBusinessPlus, and the PPOB TR Reload application, which play an important role in supporting business operations. However, several weaknesses remain in system security, integration, and IT risk management. The SWOT analysis identifies strengths in access control and authentication mechanisms, opportunities to leverage security as a competitive advantage, and threats from cyberattacks and high dependency on IT infrastructure. The study concludes that the Ward & Peppard framework provides a comprehensive strategic perspective and serves as a foundation for developing a more structured, secure, and sustainable IT strategy to support business growth.

This study investigates the relationship between design patterns, modular architecture, and the maintainability of distributed real time systems developed using agile practices. Distributed real time systems are critical in various sectors, including telecommunications, healthcare, and automotive, where strict timing constraints and reliability are essential. Agile methodologies, known for their flexibility and iterative development, have been widely applied to software engineering, but their impact on long-term system maintainability, especially in complex real time environments, has been insufficiently explored. This research employs an empirical analysis, combining both quantitative and qualitative data from multiple real time system projects using agile methods. The analysis focuses on the application of design patterns, such as Singleton, Observer, and Factory, and evaluates the effectiveness of modular architectures in enhancing system scalability, flexibility, and long-term sustainability. The study also explores how agile practices contribute to system performance and maintainability, despite challenges related to frequent updates and coordination among distributed teams. Key findings show a positive correlation between the consistent use of design patterns and modularity, which significantly improves the maintainability and adaptability of distributed real time systems. This research also highlights the challenges faced by agile methods in maintaining architectural consistency and managing non-functional requirements, particularly in distributed environments. The results contribute valuable insights into adapting agile practices to meet the specific demands of distributed real time systems, offering recommendations for developers and project managers to incorporate modular architecture and design patterns to enhance long-term system sustainability. Further research is suggested to explore new design patterns and investigate the broader impact of agile methodologies on system quality beyond maintainability.

This study investigates the integration of high performance communication protocols with adaptive signal processing engines in multi-core systems, aiming to enhance scalability, throughput, and inter-core communication efficiency. The challenges inherent in traditional multi core architectures, such as communication overhead, latency, and scalability limitations, are addressed through the incorporation of Network-on-Chip (NoC) architectures and adaptive signal processing techniques. By using a multi-core digital signal processing (DSP) platform, the study evaluates the performance improvements achieved by this integration under varying workloads and core configurations. The experimental results show a 35% improvement in throughput and a 25% reduction in communication latency, highlighting the effectiveness of adaptive communication protocols in managing data traffic between cores and reducing bottlenecks. The integration of NoC architecture facilitates parallel data transfers, while adaptive signal processing engines ensure that data flows more efficiently across the cores, enhancing system responsiveness, especially under high data rate conditions. Furthermore, the study explores the scalability of the proposed system, demonstrating its ability to maintain high performance as core counts increase. The findings emphasize the potential of combining advanced communication protocols with adaptive signal processing for optimizing multi-core system performance. Practical implications of this research include the design of scalable, flexible, and efficient multi core architectures suitable for complex, data-intensive applications. Future research should focus on further refining communication protocols and exploring additional integration strategies to enhance the adaptability and scalability of multi-core systems in next-generation computing environments.

Recent advancements in environmental monitoring and robotic control demand systems that are capable of real-time responsiveness, energy efficiency, and reliable operation in dynamic and resource-constrained environments. Conventional cloud-centric cyber-physical system (CPS) architectures often suffer from high latency, continuous connectivity dependency, and increased energy consumption, limiting their suitability for time-critical monitoring and adaptive control applications. To address these challenges, this study proposes an intelligent embedded cyber-physical system integrating Edge AI, low-power sensor networks, and adaptive robotic control for environmental monitoring. The proposed architecture relocates data processing and decision-making closer to the data source, enabling real-time inference, reduced communication overhead, and enhanced system autonomy. The research adopts a design-oriented experimental methodology involving system architecture design, lightweight Edge AI model development, prototype implementation, and performance evaluation under realistic operating conditions. Experimental results demonstrate that the proposed edge-based CPS significantly reduces end-to-end latency and energy consumption while maintaining acceptable inference accuracy compared to cloud-based processing. Furthermore, the system achieves improved communication efficiency and higher operational reliability, particularly under intermittent network connectivity. The findings highlight that embedding intelligence at the edge enables closed-loop sensing, decision-making, and actuation, which is essential for adaptive robotic control in environmental monitoring scenarios. This study contributes a system-level perspective on Edge AI–enabled CPS design and provides empirical evidence supporting the transition from cloud-centric architectures toward distributed, energy-aware, and resilient cyber-physical systems for real-time monitoring and control applications.

Muara Takus Temple is a valuable historical heritage in Riau Province and a testament to the glory of ancient civilization in the Indonesian archipelago. However, the environment around the temple often faces problems such as litter, vegetation damage, and declining public awareness. Clean-up and environmental preservation activities in this area demonstrate the involvement of the younger generation and the community in preserving the historic site. Through a participatory approach, this community service program aims to describe the process, results, and social impact of preservation activities in the Muara Takus Temple area. The results of the community service program demonstrate increased public awareness, the formation of cross-institutional collaboration, and the strengthening of the value of mutual cooperation in preserving cultural heritage and the environment. The results demonstrate that the involvement of the community, students, and site managers has successfully raised collective awareness of the importance of maintaining cleanliness and preserving cultural heritage. Furthermore, this activity strengthened the synergy between historical and environmental preservation through tree planting, waste management, and the provision of educational boards. This activity demonstrates that cultural heritage preservation can be revitalized through active participation and cross-generational collaboration.

Real time decision making applications, such as those used in autonomous vehicles, smart cities, and industrial IoT, require fast, scalable, and accurate analytics to ensure timely responses and optimized operations. Traditional cloud-based systems face significant challenges in meeting these requirements due to high latency, limited scalability, and bottlenecks in data processing. This study explores the use of a hybrid Edge Cloud architecture to optimize End to end machine learning (ML) pipelines for real time applications. The proposed system offloads time-sensitive tasks to edge devices, while computationally intensive processes are handled by the cloud, ensuring efficient use of resources and reduced latency. Experimental results demonstrate that the hybrid model reduces inference latency by up to 70% compared to cloud-only systems, while maintaining model accuracy and increasing throughput. Additionally, the scalability of the hybrid architecture is highlighted, as it can handle large-scale data streams and adapt to varying workloads. The findings show that hybrid Edge Cloud architectures are well-suited for applications where fast decision making is critical, such as autonomous systems and real time analytics in smart cities. However, challenges remain in managing resources across edge and cloud systems, particularly in balancing computational loads and ensuring system reliability. Future research should focus on optimizing task partitioning, integrating advanced edge AI models, and exploring the use of 5G networks to enhance performance further. Overall, the study demonstrates the potential of hybrid Edge Cloud systems in overcoming the limitations of traditional cloud-based ML pipelines and provides insights into the future of real time data processing.

This study develops a computational-based system to identify individual potential through the analysis of signature patterns using Artificial Neural Networks (ANN) and the Backpropagation algorithm. The research aims to explore and examine the effectiveness of applying ANN in recognizing and identifying signature patterns that are assumed to be related to an individual’s potential. In the data processing stage, Principal Component Analysis (PCA) is employed as a dimensionality reduction and feature extraction technique to optimally obtain the main characteristics of signature images. The system performance evaluation is conducted using a total of 80 signature images, consisting of 60 training data and 20 testing data. This study analyzes two network architecture configurations, namely a model with one hidden layer and a model with two hidden layers. The experimental results show that both network configurations achieve the same accuracy level of 92.5%. These findings indicate that the use of Artificial Neural Networks with the Backpropagation algorithm is effective in producing high accuracy in the signature pattern recognition process. Furthermore, the developed system has broad potential applications in the field of personal identification, such as employee evaluation, selection systems, and other applications across various organizational and industrial sectors.

Edge-Internet of Things (Edge IoT) systems are increasingly integral to applications that require real time signal processing, particularly where low latency and energy efficiency are critical. This paper explores the design and performance evaluation of a heterogeneous microprocessor architecture aimed at optimizing energy consumption and real time performance. The heterogeneous architecture integrates multiple types of cores, such as Central Processing Units (CPUs), Digital Signal Processors (DSPs), and Graphics Processing Units (GPUs), to allocate tasks based on computational demand. The proposed design significantly reduces energy consumption, particularly during high-performance tasks, while maintaining real time processing guarantees. Simulation-based performance evaluation was conducted to assess the energy efficiency, latency, and overall system performance under varying workloads, including real time Digital Signal Processing (DSP) benchmarks. The results showed that the heterogeneous architecture outperformed traditional homogeneous processors, demonstrating up to a 19-fold improvement in energy efficiency. Furthermore, the system reduced latency by up to 45% in real time applications, making it particularly suitable for Edge IoT environments such as industrial automation and smart healthcare, where both performance and energy efficiency are critical. Despite some trade-offs in task scheduling complexity, the heterogeneous design was able to balance power consumption and computational performance effectively. The findings suggest that this architecture can serve as a foundation for future Edge IoT systems, providing significant advantages in terms of energy efficiency, real time processing, and scalability. Future work will focus on further optimization of the architecture and exploring its application across various IoT environments.

Cloud native architectures are essential for modern software systems due to their ability to handle dynamic environments, scalability, and high availability. However, ensuring resilience in these systems remains a significant challenge, particularly under varying operational conditions such as high-load periods and failure scenarios. This study aims to assess the resilience of cloud native architectures using quantitative metrics that objectively evaluate key attributes such as availability, fault tolerance, recovery time, and scalability. Through the application of these metrics, the study identifies the strengths and weaknesses of the architecture, providing insights into how the system performs under stress and recovers from failures. The results show that while the architecture demonstrates strong availability and scalability under typical conditions, recovery time and scalability under extreme load conditions reveal areas for improvement. Specifically, issues with resource allocation and self-healing capabilities were identified as key weaknesses affecting the overall resilience of the system. These findings highlight the importance of using data-driven metrics to gain detailed insights into system resilience and to guide architectural improvements. The study also emphasizes the need for continuous monitoring and adaptation of the architecture to optimize fault tolerance and recovery processes. The implications of this research extend to cloud application developers and architects, offering actionable recommendations for improving system resilience. Future research could focus on integrating real-time monitoring systems, developing more advanced resilience metrics, and incorporating AI-driven scaling techniques to further enhance the adaptability and robustness of cloud native systems. By addressing these challenges, cloud native architectures can be better equipped to maintain high performance and reliability in dynamic, real-world environments.