Growth rapid computing cloud, especially on academic, government, and service platforms. public, has trigger improvement frequency and complexity Distributed Denial of Service (DDoS) attacks. Intelligent DDoS attacks AI based capable copy pattern Then cross user valid, so that difficult detected and mitigated. The majority approach mitigation moment This nature reactive, no scalable, and tends to sacrifice availability service for authorized users. Research​ This aiming develop architecture proactive and adaptive defense​ For ensure continuity service during attack ongoing. Security model proposed hybrid​ integrating Zero Trust Architecture (ZTA), adaptive bandwidth control, and isolation service container -based. Architecture consists of from three layer Main: (1) ZTA Policy Engine which performs verification identity and assessment behavior through tokens and policies intelligent; (2) Adaptive Bandwidth Load Balancer which automatically dynamic separate and arrange Then cross based on reputation and level trust ; and (3) Containerized Service Cluster which groups request to in different containers For user trusted and not known . Components addition such as blockchain -based smart contracts are used For recording request and verification access , as well as lightweight AI module used for profiling then cross in real-time. Simulation results show that this model succeed increase availability service for user trusted during attack , press false positive rate , as well as optimize allocation source power. Integration of zero trust policies with intelligence Then cross and segmentation service in real-time forming framework effective and scalable defense​ to modern DDoS threats . In conclusion , the study This contributes a robust , adaptive , and modular architectural model for maintain continuity cloud services in condition network at risk .

The advancement of networking and communication technologies has escalated the IoT and edge computing integration by leaps and bounds. This paper provides a review of the current trends, technologies and issues concerning IoT and edge computing. Looking at the key application areas of smart cities, healthcare, industrial IoT and smart grids this paper demonstrates how edge computing solves the problems of cloud computing such as latency, bandwidth and privacy. The survey also reveals present day constraints such as the lack of standards for scalability, integration problems, and lack of strong data protection measures. This work can be considered as a reference source for researchers and practitioners since it reveals the relationship between IoT and edge computing and gives an idea of further advancements.

This study aims to analyze the implementation of Metaverse technology as a medium for socializing religious moderation at the Palembang Religious Education and Training Center. Using a qualitative approach, this research explores how Metaverse can facilitate the dissemination of religious moderation values and its potential in improving the understanding of balanced, tolerant, and inclusive religious practices among participants. Data was collected through interviews, observations, and document analysis. The research findings show that Metaverse offers a dynamic and immersive platform to deliver training and discussions on religious moderation, allowing participants to interact more deeply with the material presented. However, challenges such as limited access to technology and understanding of the virtual environment were also found. This research highlights the need for further adaptation and support to maximize the effectiveness of this technology in building religious moderation.

In image detection processes where there is a variation in brightness between pixels, techniques are required to obtain optimal and adaptable threshold values for these variations. Therefore, a comparison between the binary thresholding technique and the adaptive method of Otsu is made, in videos with dynamic and static background, weighing the response time of the algorithm, memory used, requirement of the central processing unit and hits in the detections, in the languages of Python and M (Matlab). The techniques in Python present better results in terms of response time and memory space; while, when using Matlab, the lowest percentage of machine requirement is presented. Also, the Otsu method improves the percentage of hits in 12.89 % and 11.3 % for videos with dynamic and static background, with respect to the binary thresholding technique.

This research explores the integration of Augmented Reality (AR) technology into custom gift products by Tricksy.co to create a more interactive and personalized gift-giving experience. By utilizing EyeJack Creator, a platform for creating AR photo markers, the system allows customers to design gifts that display personalized videos when scanned. The process includes generating AR markers, integrating QR codes, and ensuring compatibility with both iOS and Android devices. System testing confirmed that the AR markers successfully displayed the videos as intended, with smooth functionality across different platforms. However, slow internet connections impacted the speed of AR content downloading. This study highlights the potential of AR to revolutionize the gift industry by adding a unique digital layer to physical gifts, offering both emotional and interactive value.

The rapid development of information technology has significantly impacted the business world, including Micro, Small, and Medium Enterprises (MSMEs). However, business actors, particularly in the catfish farming sector, often face challenges in accessing broader markets and improving operational efficiency. This study aims to develop LeleQue, a web-based information system designed to efficiently support the management of catfish farming businesses. By implementing the SDLC (Software Development Life Cycle) method, this research outlines the system development process, from needs analysis to system evaluation The LeleQue application is equipped with key features such as financial bookkeeping, inventory management, debt tracking, financial reports, and discussion forums. The findings indicate that LeleQue provides an integrated platform to meet the operational management needs of catfish farming businesses. Additionally, the application facilitates collaboration among farmers through its community forum. This study contributes to supporting MSME digitalization, particularly in the catfish farming sector, to enhance efficiency, productivity, and business competitiveness.

Software testing is a critical phase in information system development to ensure the system's quality and reliability. This study aims to evaluate the reliability and functionality of PT Perta Sakti Abadi's financial information system using the black-box testing method with the Equivalence Partitioning (EP) technique. This technique allows input data to be grouped into valid and invalid categories, minimizing test cases without reducing testing coverage. The testing focuses on the login feature as the system's primary component by evaluating various input combinations. The testing scenarios include boundary conditions to ensure the system handles inputs correctly in various situations.The results indicate that the system successfully verifies valid credentials, rejects access with invalid data, and provides informative error messages. Additionally, the system demonstrates resilience in handling testing scenarios, including inputs with special characters and empty fields. Input validation mechanisms function optimally, supporting secure user access and ensuring the login feature aligns with functional specifications. This successful testing forms a strong foundation for testing other modules, such as multi-level authentication and data encryption. Thus, the Equivalence Partitioning technique within the black-box testing method proves effective in enhancing the quality of web-based financial information systems.

This study focuses on black-box testing of JivaJoy software, an online product stock management and ordering system. The primary goal of this research is to evaluate the functionality and performance of the system's key features, including profile management, CRUD operations for admin and customer accounts, product stock management, shopping cart, order management, and AI counseling. Black-box testing was applied to assess whether these features meet expected operational standards and user requirements without considering the internal code structure. The test results indicate that most features function as expected. However, some issues were identified related to input validation, particularly with email format, phone number length, file uploads, and product stock management. Additionally, the order management and AI counseling features showed deficiencies in error handling and input validation. Based on these findings, the study recommends improvements in input validation, product stock management, shopping cart functionality, and AI counseling systems to enhance the reliability and performance of the JivaJoy platform. These enhancements are essential to deliver more efficient, secure, and optimal services, ensuring better user experiences and customer satisfaction.

This research aims to determine the impact of Project Based Learning (MBKM) on Problem Solving for Undergraduate Students in Educational Technology at Bina Mandiri University, Gorontalo. The research method used is a qualitative and quantitative approach at four research stages. According to Creswel and Clark, mixed methods research is a research design with philosophical assumptions as well as being an inquiry method. As a methodology, this mixed research involves philosophical assumptions that guide the direction of data collection and analysis, as well as cultivating qualitative and quantitative research approaches throughout many phases of the research process. This research is in the first stage, namely identifying problem solving for Bachelor of Educational Technology students and measuring the effectiveness of MBKM project-based learning at UBMG. The results of the research show that project-based learning (MBKM) can influence Problem Solving for students in the Bachelor of Educational Technology study program at Bina Mandiri University, Gorontalo. This can be seen from the achievement of the problem solving method indicator for the Educational Technology S1 PRODI students, which was initially 75%, increasing to 87% after participating in project-based learning (MBKM) for one semester. The conclusion of this research is that project-based learning (MBKM) has a positive impact on improving problem solving skills in undergraduate students in educational technology so that it can be applied to subsequent learning processes.

With the rising demand for ubiquitous internet access, there is a growing need for technologies that allow users to remain connected to their home broadband network even when outside the house. This paper explores several proposed solutions, including advanced routers, VPNs, mobile applications, mesh networking, and hybrid cloud routers, that enable remote access to home networks. These technologies offer the potential to provide seamless, secure, and affordable connectivity, especially for individuals in rural or remote areas This innovation offers significant implications for rural internet connectivity and broader networking technology advancements. Additionally, the paper explores the societal and economic impacts of these technologies, particularly in underserved regions. By reducing dependence on expensive mobile data and providing secure, continuous internet access, these solutions can help bridge the digital divide in rural and remote areas. Improved connectivity fosters opportunities for education, remote work, and economic development, enhancing quality of life. Furthermore, advancements in hybrid cloud routers and mesh networking have the potential to drive innovation in the telecommunications industry, leading to more robust, scalable, and cost-effective networking solutions. The research underscores the need for further development and widespread implementation of these technologies to address existing internet accessibility challenges.

The rapid advancement of Industrial Internet of Things (IIoT) technology necessitates robust authentication solutions to ensure security, scalability, and efficiency. This project, titled "Enhancing Security in Industrial IoT: Authentication Solutions Leveraging Blockchain," examines various blockchain-based authentication methods for IIoT and identifies their strengths and weaknesses. Despite the enhanced security and decentralized nature of blockchain, issues such as scalability, high latency, and computational load persist. To address these challenges, we propose the integration of Multi-Factor Authentication (MFA) as a supplementary solution. MFA can distribute the authentication load, enhance flexibility and security, and reduce latency by utilizing quick-to-verify factors. Moreover, MFA ensures high availability and scalable storage and processing through cloud services, seamlessly integrating with existing systems to provide a superior user experience. This comprehensive approach not only mitigates the inherent limitations of blockchain technology in IIoT but also reinforces the overall security framework, ensuring resilient and efficient authentication mechanisms. The results demonstrate significant improvements in system performance and user satisfaction, establishing MFA as a viable enhancement to blockchain-based IIoT security solutions.

The Internet of Things (IoT) phenomenon is centered around linking various devices and objects to the Internet, enabling them to communicate, collect, and exchange data [1]. The IoT needs strong, lightweight, and secure authorization schemes to regulate many devices with varying levels of ability. Quantum-resilient federated Multi-Factor Authentication (QRF-MFA) is a solution presented in this paper to address the above-discussed issues. Featuring quantum-resistant cryptographic protocols, high-speed and low-energy Physically Unclonable Functions (PUFs), decentralized identity management, and optimized communication protocols, QRF-MFA provides a complete solution for secure cross-domain device identification and authentication. This is done by leveraging blockchain technology for immutable and transparent management of identities yet limiting on-chain storage overhead. It also provides secure, lightweight communication well-suited for resource constrained IIoT devices, and it is designed for fog and edge computing environments as well. QRF-MFA eliminates the challenges of current methods by combining security, efficiency, and scalability and delivering a resilient and future-ready solution to secure IIoT authentication.  

This paper explores the Time-Based One-Time Password (TOTP) authentication mechanism enhanced with lightweight cryptographic algorithms, presenting it as an advanced solution to the limitations of traditional OTP systems. There are a lot of applications and systems where this mechanism is applied. For example, bank applications, e-commerce websites, access control system, healthcare system, etc. TOTP generates dynamic, time-sensitive passwords using the current time and a secret key processed through a cryptographic hash function, significantly improving security by reducing vulnerabilities to code reused and interception. The adoption of lightweight algorithms ensures that TOTP can be efficiently implemented on resource-constrained devices, such as those on the Internet of Things (IoT) ecosystem. Despite its benefits, TOTP faces challenges including synchronization issues between client devices and servers, and a trade-off between computational efficiency and security strength. This paper discusses the implications of these challenges and evaluates how TOTP, with appropriate design considerations, can provide a robust, secure, and efficient authentication method suitable for various applications, from digital banking to IoT environments.

As Internet of Things (IoT) devices proliferate, edge computing has become essential for reducing latency and improving data privacy. This paper explores federated learning as a method to enhance the efficiency and security of edge computing systems. We implement and evaluate federated models in various IoT environments, demonstrating how federated learning can reduce data transfer and computation load while maintaining accuracy in data analysis.

This study examines advanced image processing techniques to improve satellite imagery for use in disaster management and recovery efforts. Through methods like deep learning-based image segmentation and noise reduction, we enhance image clarity and detail, allowing for better decision-making in emergency response. The results indicate a significant improvement in identifying affected areas, aiding faster and more accurate response.

Blockchain technology presents a decentralized and transparent solution for secure voting systems. This paper provides a comparative analysis of consensus protocols, such as Proof of Work (PoW), Proof of Stake (PoS), and Delegated Proof of Stake (DPoS), to evaluate their effectiveness in a secure voting environment. Results show that PoS offers optimal security with reduced energy consumption, making it suitable for public elections.

This study investigates the effectiveness of machine learning models in identifying fraudulent financial transactions in real-time. Using a large dataset of transactions, we compare the accuracy, precision, and speed of various models, including logistic regression, random forests, and neural networks. Our findings suggest that ensemble methods yield higher detection rates while minimizing false positives, thus providing a promising approach to financial fraud prevention.

In the ever-changing digital world, strong security protocols are essential. As a vital line of defence against unwanted access, blockchain uses several verification techniques to boost security. This article investigates the use of blockchain technology to tackle privacy, security, and usability issues. By reducing the dangers associated with conventional centralised systems, blockchain's decentralised and immutable structure offers a secure platform for storing and verifying authentication credentials. This method increases user trust by using smart contracts to guarantee transparent and unchangeable authentication procedures. The suggested blockchain-based method strengthens security and enhances privacy by removing sources of failure and decreasing dependence on outside verification. Furthermore, user-centric design and expedited procedures improve the system's usability by making secure authentication more approachable and less obtrusive. This paper offers a thorough examination of the suggested system, stressing its benefits, possible drawbacks, and directions for future investigation. The results indicate that blockchain technology presents a viable solution to ensure that digital authentication frameworks combine privacy, security, and usability.

Quantum computing has the potential to revolutionize machine learning by offering exponential speed-up for specific algorithms. This study explores the theoretical and practical implications of using quantum computing to optimize machine learning models, such as in training neural networks. The findings provide insights into the possible improvements in computational efficiency, particularly for large datasets and complex models.

The Zachman Framework is one of the methods that can be used to design systems in a structured and comprehensive manner. This study aims to apply the Zachman Framework to design the academic system at SMA Al Kautsar Bandar Lampung, resulting in a blueprint that serves as a guide for system development. The research method involves mapping each perspective in the Zachman Framework, namely Planner (scope), Owner (business model), Designer (system model), and Builder (technology implementation). The findings indicate that the Zachman Framework ensures that all essential aspects of the system are considered, resulting in a structured enterprise architecture design that is efficient and meets stakeholder needs. Furthermore, this framework has proven effective in integrating data and synergizing business functions in the academic section, supporting optimal operations. The implications of this study suggest that the Zachman Framework can serve as an alternative solution for designing and developing an organized, directed, and easily implementable academic system, thus improving the efficiency of business processes in educational institutions.

The rise of smart cities brings increased interconnectivity, but also new security vulnerabilities, especially among IoT devices. This study investigates methods for improving cybersecurity in smart cities by implementing IoT device management protocols. We examine approaches such as network segmentation and secure authentication to mitigate common threats, thus providing a safer environment for urban digital infrastructure.

The growth of big data has driven the need for efficient data processing methods, especially in cloud computing environments. This study evaluates distributed computing frameworks like Apache Hadoop and Apache Spark for optimizing big data processing. By analyzing different configurations, we demonstrate how distributed systems can significantly reduce processing time and improve resource utilization, making them ideal for handling complex datasets in cloud environments.

Network intrusion detection is crucial for maintaining the integrity of real-time systems. This paper evaluates various machine learning algorithms, including support vector machines (SVM) and decision trees, for real-time intrusion detection. Through extensive testing on simulated datasets, the study highlights the advantages of automated detection in reducing response times and enhancing network security.

With the rapid expansion of the Industrial Internet of Things (IIoT), integrating devices, machines, and systems to optimize operations and enable data-driven decision-making, ensuring robust security measures is essential. While blockchain has shown the potential to upgrade traditional authentication methods in IIoT environments, vulnerabilities persist. This paper introduces two innovative methods to enhance blockchain-based authentication in IIoT: first, integrating AI-driven anomaly and threat detection into the blockchain authentication scheme; second, implementing Ethereum smart contracts for enhanced authentication with a two-factor authentication (2FA) system and GFE algorithms. By combining AI for anomaly detection with decentralized smart contracts and blockchain-based 2FA, and leveraging GFE algorithms to enhance blockchain capabilities, the proposed scheme aims to significantly fortify security measures. This integration offers a resilient defense against evolving threats, ensuring transparency, adaptability, and heightened security in IIoT applications.

Information Technology (IT) service management plays a critical role in supporting the efficiency and effectiveness of business intelligence processes within modern organizations. This study aims to evaluate IT service governance in Bank Rakyat Indonesia using the Information Technology Infrastructure Library (ITIL) framework. This approach focuses on improving operational efficiency, service management, and aligning IT services with business needs. The research method involves a detailed analysis of service processes and activities based on ITIL stages, ranging from service strategy to continuous improvement. The findings indicate that ITIL implementation significantly enhances IT service management and promotes business intelligence effectiveness within the organization. Recommendations for further development are also provided to improve the quality and speed of IT services in the future.

In recent years, virtual assistants have become an integral part of everyday life, simplifying routine tasks and allowing users to focus on more important matters. This research aiming to design GiggleGate, a virtual desktop assistant integrated with both face and speech recognition technology to enhance authentication security. The objective is to develop an authentication system that not only verifies user identity but also provides a more intuitive experience and seamless interaction. The research employs a development methodology to create and implement the system, which integrates face recognition via OpenCV and speech recognition via a Python library. The findings indicate that the integration of these technologies enhances security and user experience by offering dual-factor authentication. The system is expected to contribute to more secure and accessible virtual assistant applications, offering both a practical and efficient solution for users. The implications of this study suggest that the combination of face and speech recognition can provide an effective means to protect user privacy and improve the overall functionality of desktop assistants.

With quantum computing’s rise, traditional cryptographic methods face new security challenges. This paper evaluates the effectiveness of various quantum algorithms, such as Shor’s and Grover’s algorithms, on current encryption standards. Our findings highlight the vulnerabilities in conventional cryptography and suggest new standards and protocols to maintain data security in a quantum-driven era.

The Industrial Internet of Things (IIoT) enhances the connectivity and efficiency of living lifestyles. However, it also comes with significant security vulnerabilities. Traditional authentication methods are often inadequate, leading to IIoT devices opened to security threats. This paper proposes a comprehensive security framework integrating blockchain, cryptographic techniques, smart contracts, and deep learning-based Intrusion Detection Systems (IDS) to tackle the mentioned issue. Blockchain ensures data integrity and prevents tampering through a decentralized ledger. A decentralized device identity management system enhances user verification, while secure communication protocols using Hash-based Message Authentication Codes (HMAC) safeguard data integrity. Smart contracts automate transactions, providing transparent, secure record-keeping without a central authority. The deep learning-based IDS, utilizing Contractive Sparse Autoencoder (CSAE) and Attention-Based Bidirectional Long Short-Term Memory (ABiLSTM) networks, effectively detects cyber threats. Evaluation metrics, including precision, recall, F1-score, and False Acceptance Rate (FAR), demonstrate high accuracy and low false alarm rates across datasets. This framework addresses the need for secure, efficient, and scalable authentication in IIoT, combining blockchain's security features with advanced cryptographic and anomaly detection techniques, offering robust defence against cyber threats.

Social media platforms contain vast amounts of data that can reveal public sentiment on various topics. This research explores the application of deep learning techniques, particularly convolutional neural networks (CNN) and recurrent neural networks (RNN), to analyze sentiment within social media text. The results indicate that these models achieve high accuracy in sentiment classification, making them valuable tools for companies seeking to understand public opinion.

This study aims to compile an Information Technology (IT) Master Plan for SMK Ma'arif 1 Metro using The Open Group Architecture Framework (TOGAF) method. This method is applied to design a structured and integrated information technology infrastructure to support operations and learning in schools. The results of the study indicate that the main business processes in schools, such as student registration, curriculum management, and financial administration, are still carried out manually, making them prone to errors and inefficiencies. Through TOGAF, an information system architecture is designed that includes the development of the Academic Information System (SIAKAD) and other school management applications. In addition, improving network infrastructure and cybersecurity are priorities in the proposed technology architecture. Implementation strategies and change management are also formulated to ensure the success of the digital transformation. This IT Master Plan is expected to improve the efficiency of school operations and the quality of learning, as well as support the achievement of more optimal educational goals in the digital era.