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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.

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.

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.

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.

This research explores the impact of Cache Aware optimizations on signal processing pipelines in High Throughput computing systems. The growing demand for efficient memory management in modern computing systems, especially for data-intensive applications such as artificial intelligence (AI) and multimedia processing, necessitates the development of optimized memory hierarchies. Traditional memory systems often suffer from memory bottlenecks, significantly reducing the performance of these systems. This study investigates how memory hierarchy optimizations, particularly cache line aware optimization, dependency-aware caching, and adaptive cache replacement algorithms, can mitigate these challenges and improve system performance. Through analytical modeling and experimental benchmarking, this work evaluates various memory hierarchy configurations, including processing-in-memory (PIM) and three-dimensional integrated circuits (3D ICs), comparing them to conventional systems. The results demonstrate that Cache Aware optimizations lead to a reduction in memory access latency by up to 30%, while throughput improved by up to 40%. Additionally, cache hit rates increased by 25%, and energy consumption was reduced by up to 20%, highlighting the effectiveness of optimized memory management. The research contributes to the field by providing valuable insights into the design and implementation of efficient signal processing pipelines. It also identifies key challenges, including the need for dynamic occupancy mechanisms and DAG-aware scheduling algorithms, and suggests potential areas for future research, such as the exploration of collaborative caching approaches and further optimization of cache-adaptive algorithms. This work lays the foundation for more efficient, high-performance computing systems that can handle large datasets and complex tasks in real-time applications.

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 research explores the integration of Enterprise Architecture (EA) and Artificial Intelligence (AI) to optimize strategic decision-making in digital service-oriented organizations. These organizations often face challenges such as fragmented decision-making due to disconnected IT systems and limited data-driven insights. The objective of the study is to develop an integrated framework that combines EA and AI to enhance decision-making accuracy, operational efficiency, and strategic alignment. The study employs design science research methodology, involving the development of the framework, expert validation, and testing in simulated organizational scenarios. The findings reveal that the integrated framework improves decision-making by providing real-time, data-driven insights, predictive analytics, and better alignment with organizational goals. AI's role in analyzing large datasets and generating actionable insights allows decision-makers to anticipate future trends and make more informed decisions. The framework significantly outperforms traditional EA approaches, particularly in terms of predictive decision support and adaptive intelligence. The study concludes that the integration of EA and AI provides a robust solution for organizations looking to improve strategic decision-making, enhance operational efficiency, and stay competitive in dynamic business environments.

The increasing integration of Cyber physical Systems (CPS) into industrial environments has highlighted the need for secure, scalable, and efficient cryptographic key management systems. Traditional centralized key management protocols are often limited by vulnerabilities such as single points of failure, scalability issues, and significant overhead. Blockchain technology presents a promising solution to these challenges by leveraging decentralization, immutability, and transparency to enhance security and efficiency in CPS. This study investigates the use of blockchain based cryptographic key management systems, focusing on smart contracts for automated key distribution and rotation. Experimental results demonstrate that blockchain based systems significantly improve system integrity, auditability, and resilience, offering enhanced protection against cyber-attacks and reducing the risks associated with centralized systems. Blockchain’s decentralized architecture eliminates the need for a central authority, making it more resistant to tampering and operational failures. Additionally, smart contracts automate the key management process, improving efficiency while maintaining a high level of security. The study also evaluates the impact of blockchain on communication performance, finding that it reduces latency and overhead by automating processes and eliminating the need for centralized control. Despite these advantages, challenges such as scalability, latency, and integration with legacy systems remain. The study concludes by suggesting future research directions, including the development of lightweight blockchain protocols tailored for industrial applications and the integration of blockchain with emerging technologies like Artificial Intelligence (AI) to further enhance key management in CPS. Blockchain based solutions have the potential to transform the security landscape of industrial environments, offering greater robustness, reliability, and trust.

Smart cities are increasingly leveraging advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data Analytics to optimize urban management and improve the quality of life for citizens. However, managing vast and diverse datasets from numerous sources in real-time presents several challenges. This research proposes a modular framework that integrates distributed data processing engines with container-based workflow orchestration to address scalability, latency, adaptability, and fault tolerance in smart city data analytics. The framework utilizes cloud native technologies, including Apache Spark and Kubernetes, to efficiently manage resources and ensure high availability. The experimental setup tested the framework’s ability to handle dynamic data loads, demonstrating scalability through real-time resource allocation and low-latency processing. The adaptability of the framework was evident in its seamless integration with various data sources, such as environmental sensors and traffic management systems, which require different processing methods. Additionally, the framework’s modularity provided fault tolerance, enabling continued operation even if individual components failed, a crucial feature for mission-critical applications in smart cities. Compared to traditional monolithic systems, the proposed framework outperformed in flexibility, scalability, and performance, offering significant improvements in handling real-time data streams. Despite these advantages, challenges remain, particularly in integrating heterogeneous data formats and optimizing real-time processing for high-priority applications. The research highlights the importance of scalable data analytics and efficient workflow orchestration for the future of smart city platforms, offering a foundation for the development of more resilient, adaptable, and efficient cloud native infrastructures.

The increasing adoption of cloud native microservices has brought about significant improvements in scalability, flexibility, and resilience. However, these advancements also introduce substantial security challenges, particularly in distributed environments where traditional perimeter-based security models prove inadequate. This paper proposes a secure architecture for cloud native microservices that integrates Zero trust Network Access (ZTNA) and multi layered encryption techniques to address these security concerns. The architecture operates on the principle of "never trust, always verify," ensuring that access to resources is strictly controlled and continuously monitored. By incorporating multi layered encryption methods such as RSA and AES, the architecture ensures data protection both in transit and at rest, significantly reducing the risk of data breaches and unauthorized access. Through experimental evaluations, the proposed architecture demonstrated its effectiveness in preventing lateral movement, mitigating data leakage, and resisting common attack vectors such as man-in-the-middle (MITM) attacks and privilege escalation. Additionally, the performance of the system remained optimal, with minimal overhead despite the additional security layers. The architecture's scalability and robust security mechanisms make it a viable solution for real-world microservices environments, where both security and performance are crucial. This paper discusses the potential impact of this secure architecture on the broader field of distributed system security and offers recommendations for future work, including the integration of advanced machine learning techniques for real-time threat detection and automated responses, as well as the adaptation of the architecture for emerging technologies like edge computing and 6G networks.

Limited legal literacy among village officials may contribute to disorderly village administration, inconsistent document formats, weak record-keeping, and poor archive traceability, ultimately affecting public service quality. This Community Service Program (Pengabdian kepada Masyarakat/PKM) aimed to strengthen the legal literacy of village officials in Larangan Luar Village, Pamekasan Regency, to improve orderly and accountable village administrative governance. The program applied a hands-on training model combined with mentoring/document clinics and a pretest–posttest evaluation involving village officials (n = 18). Implementation consisted of three core training sessions and two mentoring sessions over four weeks, focusing on document legality, official correspondence standards, numbering and registers, and basic archive management. Results showed an increase in the average legal literacy score from 54.1 (pretest) to 74.6 (posttest), an improvement of 20.5 points. Beyond knowledge gains, document quality and administrative order improved through the adoption of practical administrative tools developed during the program. Key outputs included a concise module (±22 pages), 10 village administrative document templates, one village administrative SOP, and a simple filing system based on document classification and folder numbering. In conclusion, strengthening village officials’ legal literacy through practice-based training and mentoring effectively supports improvements in village administrative governance and is potentially replicable in other villages in Pamekasan with context-specific adjustments.

Distributed software systems face significant challenges related to data quality due to their complex, decentralized architecture. These systems often involve multiple nodes responsible for processing and storing data, making it difficult to maintain consistency and ensure accurate data across the entire network. In particular, issues like data inconsistency, latency, and data fragmentation are prevalent in distributed environments. To address these challenges, this study proposes an integrated data quality governance strategy that combines real time monitoring and automated anomaly detection using machine learning models. The proposed strategy aims to improve data consistency, enhance anomaly detection capabilities, and reduce the need for manual intervention, ultimately improving overall data governance in distributed systems. Real time monitoring ensures immediate identification of data issues as they occur, while machine learning models, such as autoencoders and Isolation Forests, automate the detection of anomalies based on high reconstruction errors and data isolation techniques. The study evaluates the proposed strategy through real-world distributed system scenarios, comparing its effectiveness to traditional approaches like periodic audits and manual validation. Results demonstrate that the integrated approach leads to faster anomaly detection, reduced data inconsistencies, and improved overall system performance. The use of advanced machine learning techniques and real time analytics significantly enhances the system's ability to maintain high data quality standards across multiple distributed nodes. This strategy has wide-ranging implications for industries that rely on distributed systems, such as finance, healthcare, and IoT, where data integrity is essential for operational success. Future research can focus on integrating more advanced machine learning techniques and optimizing the real time monitoring framework to handle larger and more complex systems.

This study aims to map the direction of criminal punishment policy formulated in criminal law provisions, test claims of humanizing punishment through sanction design and the principle of individualization, and describe forms of repression that operate through normative flexibility, law enforcement discretion, and morally nuanced criminalization. The research method used is normative legal research, utilizing both a legislative and conceptual approach to analyze the norms, principles, and objectives of punishment. The results show that Article 51 articulates the objectives of punishment, including the rehabilitation of offenders, community protection, and the restoration of social balance. However, this provision functions primarily as normative legitimacy for a flexible sanction architecture. The existence of alternative punishments and oversight mechanisms refines the form of punishment while expanding state intervention into the social life of offenders. Furthermore, the regulation of conditional sentences and adjustments to the implementation of sanctions increase the discretion of law enforcement officials. Repression does not disappear, but rather shifts through regulations on morality, public order, recognition of living law, and the threat of symbolic punishment, shifting the relationship between the state and individuals toward ongoing administrative control.

This research aims to develop an E-Catalog Information System for the Library of SMPK St. Yohanes Wewewa Barat as a digital platform that provides students and teachers with fast, accurate, and efficient access to book collection information. The system is designed to replace the previous manual book search process, which often caused delays and reduced the effectiveness of library services. The development of this system applies The Model-View-Controller (MVC) method structures the system by dividing it into three distinct parts: the model, the view, and the controller, each with its own specific function resulting in a structured architecture that is easier to manage, maintain, and improve. This method was chosen because it supports modular development and provides flexibility for future system enhancements. The results of this study show that the developed e-catalog system functions properly and performs well according to the intended design. All main features, including book searching, book detail display, and book data management, operate optimally without significant issues. Moreover, the system helps improve the efficiency of library services and enhances the user experience in accessing digital book information.

This study aims to develop a student learning progress monitoring system designed to assist teachers and parents in tracking students’ academic performance at SMP Negeri 2 Loura. The main issue identified in the school is that academic information is still distributed using manual procedures, which results in delays and limited transparency regarding students’ learning progress. To address this problem, the system was developed using the Model View Controller (MVC) architecture and the Waterfall approach to system development, which consists of several sequential phases such as analyzing requirements, designing the system, implementing the solution, conducting tests, and performing ongoing maintenance. The findings of this research show that the system that has been created is capable of presenting academic information in a complete and structured manner, including assignment scores, daily tests, and semester examinations. The system provides faster and easier access for teachers to input grades and for parents to monitor their children’s academic development in real time. Functional testing shows that all features operate correctly according to user needs, with no errors found during system operation.

This study examines the intellectual footprint of Nusantara scholars in the tradition of Qur'anic interpretation from classical to contemporary times. This study is motivated by the importance of understanding the role and contribution of Nusantara scholars in building a distinctive and contextual style of interpretation with local culture. The purpose of this study is to trace the development of methods, styles, and approaches to interpretation used by scholars from time to time. The method used is qualitative with a literature study approach through analysis of exegetical works such as Tarjuman al-Mustafid by Abdur Rauf as-Singkili, Tafsir Al-Munir by Nawawi al-Bantani, Tafsir Al-Furqon by Al-Hasan, Tafsir Al-Qur'an Karim by Prof. Dr. H. Mahmud Yunus, Tafsir Al-Azhar by Hamka, to Tafsir al-Mishbah by M. Quraish Shihab. The research results show that during the classical period, interpretation focused on spirituality and spirituality, while in the modern and contemporary eras, interpretations have developed that emphasize the maqasid al-shari'ah (purpose of law). They focus not only on literal meaning but also on universal wisdom and objectives. This development reflects the continuity between the global Islamic scholarly tradition and the local socio-cultural realities of the Indonesian archipelago. Interpretations of the Quran by Indonesian scholars not only enrich the global Islamic heritage but also demonstrate that Islam is capable of dialogue with cultural and contemporary contexts without losing its universal values.

This study examines the ideas of Ziauddin Sardar and Azyumardi Azra in three major areas: Islamic education, digital religious authority, and religious moderation. Using a library research approach, this article analyzes how Sardar’s Postnormal Times (PNT) framework explains global complexity, chaos, and contradictions that shape the future of Islamic thought and education. Meanwhile, Azra’s concept of Islam Nusantara and wasathiyah provides a historical and cultural foundation for constructing moderate Islamic identity in Indonesia. Findings indicate that Sardar emphasizes adaptive education oriented toward future literacy, while Azra highlights the integration of tradition, modernity, and local culture. In the context of digital authority, Sardar views the transformation as a structural effect of postnormal conditions driven by algorithmic systems, while Azra stresses the need to strengthen scholarly legitimacy based on sanad, institutions, and ethical guidance. Both perspectives converge on the importance of moderation. Sardar presents moderation as a strategy to manage global complexity, whereas Azra positions wasathiyah as the inherent identity of Islam in the archipelago. This study concludes that synthesizing both frameworks can strengthen Islamic education, stabilize digital religious authority, and reinforce Indonesia’s moderate Islamic identity in responding to contemporary challenges.

This study aims to analyze the opportunities, threats, strengths, and weaknesses of Arch Barbershop, a modern barbershop in Maumere City. The study was conducted using a descriptive approach through observation of the business environment, competitor analysis, and identification of local consumer preferences. The analysis shows that Arch Barbershop has promising opportunities due to the increasing trend of grooming among teenagers and young adults, and the lack of barbershops offering modern and consistent service concepts in the area. However, this business also faces threats in the form of competition from new barbershops, rapidly changing consumer preferences, and dependence on barber skills as the spearhead of service quality. Furthermore, internal weaknesses such as limited space, inflexible operating hours, and a less than optimal variety of services pose challenges to business development. By understanding these opportunities, threats, strengths, and weaknesses, Arch Barbershop can formulate a more effective development strategy to increase its competitiveness and business sustainability in Maumere City.

Globalization and the penetration of digital culture have accelerated the homogenization of values, which has weakened the cultural identity and social character of the younger generation. This condition places local wisdom as a source of values that not only needs to be preserved but also systematically transformed in education. This article offers a critical analysis of the integration of local wisdom in character education as a mechanism for social transformation. Using a structured literature review approach, this study analyzes the concept of local wisdom, the role of teachers as agents of cultural preservation, the challenges of implementation in the context of modern education, and strategic models for integrating local values into the curriculum and school culture. The findings show that local values such as gotong royong (mutual cooperation), tepa selira (harmony), Tri Hita Karana (harmony between humans, nature, and the divine), Silih Asah Asih Asuh (mutual encouragement, love, and care), ABS-SBK (mutual respect, honesty, and integrity), and various traditional philosophies of the archipelago have proven effective in strengthening students' social character, enhancing social cohesion, and strengthening national identity. However, obstacles such as the dominance of global culture, low cultural literacy among teachers, and weak national curriculum support are significant hindrances. This article proposes a three-layer integrative model of contextual curriculum, culture-based pedagogy, and participatory school culture as a strategy for transforming social values that are adaptive to the challenges of the digital age. The conclusion emphasizes that character education based on local wisdom is not only an instrument for cultural preservation but also a strategic framework for building social resilience and national identity amid rapid global change.

The traditional homes of indigenous communities in Indonesia are highly vulnerable to natural disasters, particularly earthquakes, due to Indonesia's location in the Pacific Ring of Fire, which experiences high seismic activity. This situation demands the strengthening of traditional buildings to withstand potential earthquakes. Stilt houses, as a form of traditional architecture, possess characteristics that actually support earthquake resilience, such as flexible structures, the use of lightweight materials, and the application of local wisdom passed down through generations. With the development of modern construction techniques, stilt houses have the potential to become safer and more adaptable dwellings to earthquake shocks. However, indigenous communities, particularly those living in earthquake-prone areas and with lower levels of education, often face limited knowledge and skills related to the basic principles of earthquake-resistant construction. This lack of understanding results in traditional house construction without considering structural safety aspects, ultimately increasing the risk of serious damage and even collapse during an earthquake. These impacts not only threaten life but also cause significant material losses. This community service activity aims to improve the preparedness and resilience of the Sentani Indigenous community by developing earthquake-resistant stilt house models, increasing knowledge and skills in earthquake-safe house construction and maintenance, and encouraging the use of innovative technology and materials. The expected outcomes are the creation of model houses that can serve as examples and the dissemination of information on earthquake-resistant construction technology.