cover
Contact Name
Sujacka Retno
Contact Email
sujacka@unimal.ac.id
Phone
+6282295574747
Journal Mail Official
jacka@unimal.ac.id
Editorial Address
Jl. Batam. Kampus Bukit Indah. Gedung Prodi Teknik Informatika. Blang Pulo, Lhokseumawe, Aceh
Location
Kota lhokseumawe,
Aceh
INDONESIA
Journal of Advanced Computer Knowledge and Algorithms
ISSN : -     EISSN : 30318955     DOI : http://doi.org/10.29103/jacka.v1i1.14530
Core Subject : Science,
JACKA journal published by the Informatics Engineering Program, Faculty of Engineering, Universitas Malikussaleh to accommodate the scientific writings of the ideas or studies related to informatics science. JACKA journal published many related subjects on informatics science such as (but not limited to): Adversarial Machine Learning: Addressing security concerns and developing algorithms robust to adversarial attacks. Anomaly Detection Algorithms: Identifying unusual patterns or outliers in data. Automated Machine Learning (AutoML): Developing algorithms that automate the machine learning model selection and hyperparameter tuning. Automated Planning and Scheduling: Developing algorithms for autonomous decision-making and task scheduling. Bayesian Networks: Utilizing probability theory to model and analyze uncertain systems. Computer Vision: Developing algorithms for image and video analysis, enabling machines to interpret visual information. Constraint Satisfaction Problems (CSP): Designing algorithms to solve problems subject to constraints. Deep Learning: Developing algorithms for neural networks with multiple layers to model complex patterns. Distributed AI Algorithms: Implementing AI algorithms that can work across multiple interconnected devices or nodes. Ensemble Learning: Combining multiple models to improve overall system performance. Evolutionary Algorithms: Utilizing principles of natural selection for optimization and problem-solving. Experiential Learning Algorithms: Designing algorithms that improve performance through experience and learning. Expert Systems: Creating rule-based systems that emulate human expertise in specific domains. Explainable AI (XAI): Developing algorithms that provide transparency and explanations for AI decisions. Fuzzy Logic: Implementing logic that deals with uncertainty and imprecision in decision-making. Genetic Algorithms: Implementing algorithms inspired by genetic evolution for optimization tasks. Knowledge Representation and Reasoning: Creating structures and algorithms to represent and manipulate knowledge. Machine Learning Algorithms: Designing algorithms that enable systems to learn from data and make predictions. Multi-agent Systems: Designing algorithms for systems with multiple interacting agents. Natural Language Processing (NLP): Creating algorithms that understand and process human language. Neuroevolution: Combining evolutionary algorithms with neural networks for optimization. Optimization Algorithms: Developing algorithms focused on improving the performance, efficiency, or decision-making of systems by finding optimal solutions to problems. Pattern Recognition: Developing algorithms to identify patterns within data. Reinforcement Learning: Designing algorithms that learn through trial and error, often applied in decision-making systems. Robotics Algorithms: Designing algorithms for autonomous navigation, manipulation, and decision-making in robots. Semantic Web Technologies: Implementing algorithms for structuring and retrieving information on the web. Sentiment Analysis Algorithms: Analyzing text data to determine sentiment or emotion. Speech Recognition: Developing algorithms to convert spoken language into text. Swarm Intelligence: Developing algorithms based on collective behavior, as seen in swarms or colonies. Time Series Forecasting Algorithms: Predicting future values based on historical data patterns.
Articles 6 Documents
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The Role of Deep Learning in Advancing Computer Vision Applications: A Comprehensive Systematic Review Khadem, Najibullah; Nashir, Asmatullah; Rahmatyar, Shamsullah
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 1 (2026): Journal of Advanced Computer Knowledge and Algorithms - January 2026
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i1.24732

Abstract

Deep learning has emerged as a transformative technology in computer vision, enabling significant advancements in tasks such as image classification, object detection, segmentation, and anomaly detection across diverse domains, including healthcare, agriculture, robotics, and industrial automation. Despite these advancements, challenges related to model interpretability, data scarcity, generalization, computational demands, and real-time deployment remain significant barriers. This study aims to systematically review and analyze recent developments in deep learning techniques applied to computer vision, identify associated challenges and research gaps, and propose potential directions to enhance the efficiency, robustness, and applicability of these systems. A comprehensive literature search was conducted across multiple reputable databases, including ScienceDirect, SpringerLink, IEEE Xplore, MDPI, and Wiley Online Library, focusing on peer-reviewed articles published between 2018 and 2025. Thematic analysis and descriptive synthesis were applied to extract insights regarding deep learning architectures, application domains, datasets, key findings, and limitations. Results indicate that Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Transformer-based architectures, and hybrid models have significantly advanced computer vision applications. However, issues such as interpretability, data scarcity, and computational complexity persist. Future directions include lightweight architectures, transfer learning, federated learning, explainable AI, and multi-modal approaches. In conclusion, while deep learning has substantially improved computer vision capabilities, addressing current limitations is essential for broader real-world adoption and multi-domain applicability.
Number Theoretic Foundations of Cryptography: From Congruence Theory to RSA Atiq, Bahadur; Fakhri, Nooruddin; Wahdat, Zia
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 1 (2026): Journal of Advanced Computer Knowledge and Algorithms - January 2026
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i1.24807

Abstract

Number theory, particularly modular arithmetic and congruence theory, constitutes the mathematical backbone of modern cryptography. Foundational results such as Euler’s theorem, Fermat’s little theorem, and the Chinese Remainder Theorem (CRT) have long shaped secure communication by providing the theoretical infrastructure for computational techniques like modular exponentiation. Building on these classical insights, this paper explores the deep interplay between number-theoretic foundations and cryptographic applications, tracing their role from traditional public-key systems (RSA, ElGamal, and Diffie-Hellman) to cutting-edge post-quantum paradigms. We emphasize the centrality of congruences in enabling efficient modular exponentiation, ensuring the scalability and security of large-scale data transmission. Beyond classical protocols, the study critically examines security assumptions in light of emerging quantum threats, particularly Shor’s algorithm, which undermines conventional systems and necessitates the urgent development of resilient post-quantum methods. Recent advancements in lattice-based, code-based, and multivariate cryptography are reviewed, highlighting their mathematical underpinnings and practical readiness. Furthermore, a comparative analysis of congruence-based cryptosystems is presented, focusing on computational complexity, efficiency trade-offs, and real-world deployment in blockchain, digital signatures, and the Internet of Things (IoT). By bridging classical number theory with contemporary cryptographic challenges, this paper offers both theoretical insight and applied perspective, underscoring the enduring significance and evolving nature of congruence theory in safeguarding digital communication systems.
Exploring the Economic Impact of Banking Digitalization through Statistical and Computational Methods Qarizada, Abdulkhaliq; Sazish, Baryali
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 1 (2026): Journal of Advanced Computer Knowledge and Algorithms - January 2026
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i1.25018

Abstract

In the era of the Fourth Industrial Revolution, banking digitalization has emerged as a pivotal driver of economic development, fostering efficiency, financial inclusion, and technological innovation. The increasing adoption of mobile banking, online transactions, digital payment systems, and financial technologies (FinTech) has reshaped traditional financial systems and influenced macroeconomic outcomes such as GDP growth, investment, and employment. The purpose of this study is to systematically analyze the impact of banking digitalization on economic development, with a particular focus on the role of computational and machine learning techniques in assessing digital financial inclusion. A systematic literature review (SLR) methodology was employed, covering peer-reviewed studies published between 2020 and 2025. Relevant literature was retrieved from reputable databases including IEEE Xplore, ScienceDirect, Wiley Online Library, and MDPI, using keywords such as “banking digitalization,” “digital financial inclusion,” “economic development,” and “machine learning in banking.” The review process followed a transparent screening and selection protocol based on PRISMA guidelines, resulting in 21 studies that met the inclusion criteria. The selected studies employed a range of methodologies, including panel regression, Bayesian modeling, fuzzy multi-criteria decision-making (MCDM), artificial neural networks (ANN), and SEM–ANN hybrid approaches, allowing for comprehensive analysis of quantitative and computational perspectives. The results reveal that banking digitalization exerts a strong and positive influence on economic development. Digital financial inclusion significantly contributes to GDP growth, investment, and employment, particularly in emerging economies with supportive infrastructure and policies. Moreover, computational and machine learning techniques enhance the precision of evaluating digitalization impacts, enabling predictive insights into economic outcomes and labor market dynamics. In conclusion, banking digitalization serves as a transformative mechanism for promoting sustainable economic growth. Strategic investment in digital infrastructure, human capital development, and robust regulatory frameworks is essential to maximize the socioeconomic benefits of digital finance. The integration of advanced computational techniques further supports evidence-based decision-making, ensuring that digital banking contributes effectively to inclusive and resilient economic development.
Examining Cybersecurity Factors Affecting the Adoption and Institutionalization of Internet of Things Technologies in Developing Countries Hakimi, Musawer; Abdul Wajid Fazil; Zainullah Matin
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 1 (2026): Journal of Advanced Computer Knowledge and Algorithms - January 2026
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i1.25505

Abstract

The Internet of Things promises transformative benefits for developing countries, ranging from fairly mundane efficiency improvements to markedly enhanced service delivery, yet actual adoption and long-term institutionalization remain slow and decidedly uneven, largely because of persistent security challenges. Privacy breaches, weak authentication, network vulnerabilities, and generally low levels of trust repeatedly emerge as decisive barriers, particularly in resource-constrained environments where even small failures can, in fact, undermine confidence quite severely. This study addresses the gap in synthesizing the security determinants that influence both the adoption and the deeper embedding of IoT technologies. A systematic literature review, guided by PRISMA, was conducted across IEEE Xplore, Scopus, Web of Science, SpringerLink, ACM Digital Library, and Taylor & Francis Online, identifying 25 peer-reviewed studies published between 2020 and 2025. Data extraction focused on security determinants, sectoral focus, regional distribution, and adoption patterns, so the analysis would retain a clear and coherent scope. Deductive coding covering privacy, authentication, and network security was combined with inductive themes related to trust and risk perception, and the findings were synthesized through frequency counts, thematic analysis, and cross-tabulation. Results highlight four dominant security clusters: privacy and confidentiality, trust and risk perception, authentication and access control, and network or infrastructure security. Privacy concerns were most frequently reported, followed quite closely by trust, authentication, and network vulnerabilities. Healthcare and education sectors appear most sensitive to privacy, while Asia dominates the evidence base, with Africa and Latin America still underrepresented. The study concludes that security concerns, while sometimes manageable in pilot phases, become critical barriers to scaling and institutionalization, so policymakers must priorities robust governance, trust-building, and capacity development to realize IoT’s potential in developing-country contexts.
Enhancing Security in Software-Defined Networks Using Artificial Intelligence Techniques Rafiullah Haqmal; Mohammad Wasim Safi; Fida Mohammad
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 1 (2026): Journal of Advanced Computer Knowledge and Algorithms - January 2026
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i1.25755

Abstract

Software-defined networking (SDN) has transformed network architectures through centralised control and programmability; yet this very centralization quite convincingly a double-edged sword exposes a set of critical vulnerabilities, including controller-targeted distributed denial-of-service attacks, exploits at the southbound interface, and flow rule manipulations that traditional defences continue to struggle to counter effectively. What is more, the rapid proliferation of IoT, 5G, and cloud integrations has only amplified these risks rendering static security mechanisms markedly inadequate in the face of continually evolving threats. This systematic review aims to investigate the efficacy of artificial intelligence techniques particularly machine learning and deep reinforcement learning in enhancing SDN security. Indeed, by adopting a rigorous systematic literature review methodology, the study will synthesise peer-reviewed works from major academic databases published between 2014 and 2025, critically evaluating AI-driven approaches for threat detection, anomaly identification, and automated mitigation while simultaneously identifying integration challenges such as scalability, real-time performance, and adversarial robustness. What is more, expected outcomes include the development of a comprehensive taxonomy of AI applications in SDN security, comparative insights into their reported performance (quite useful for highlighting strengths and weaknesses), and the identification of evidence-based research gaps. For that matter, the review will conclude by proposing future research directions oriented towards resilient and adaptive frameworks capable of safeguarding next-generation software-defined networks.
Cover, Editorial Board, Acknowledgement and Table of Contents JACKA
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 1 (2026): Journal of Advanced Computer Knowledge and Algorithms - January 2026
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i1.26109

Abstract

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