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Sujacka Retno
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Journal of Advanced Computer Knowledge and Algorithms
Published by Universitas Malikussaleh
ISSN : -     EISSN : 30318955     DOI : http://doi.org/10.29103/jacka.v1i1.14530
Core Subject : Science,
Computer Science & IT
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.
Arjuna Subject : Ilmu Komputer (semua kategori) - Teori Komputasi dan Matematika
Articles 51 Documents
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Performance Analysis of the Combined K–Nearest Neighbor (KNN) and Principal Component Analysis (PCA) Algorithms in Bird Species Image Classification Tawakal, Rayendra
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 3 (2025): Journal of Advanced Computer Knowledge and Algorithms - July 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i3.22597

Abstract

For most people, learning more about the many types of birds is difficult because there are so many species and many of them look similar in terms of size, color, and shape. Identifying bird species is not an easy task since it requires special skills, time, and money to study each type. Therefore, this study aims to develop an image processing system to classify bird species, especially birds found in the Aceh region. The system uses a combination of the K-Nearest Neighbor (K-NN) algorithm and Principal Component Analysis (PCA). Feature extraction in this study is based on the color and shape of the birds. The K-NN algorithm groups objects by finding the closest distance between them. Meanwhile, PCA is used to reduce the size of the data while keeping most of the important information. Based on the test results, the system achieved an accuracy of 82.50%, a precision of 83.06%, and a recall of 82.50%. This shows that combining K-NN and PCA in classifying bird images can produce better accuracy than using only the K-NN algorithm.Bird Species
Cover, Editorial Board, Acknowledgement and Table of Contents JACKA, JACKA
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 3 (2025): Journal of Advanced Computer Knowledge and Algorithms - July 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Effective Data Preprocessing in Data Science: From Method Selection to Domain-Specific Optimization Shahidi, Shahwali; Wahid Samadzai, Abdul; Shahbazi, Hafizullah
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 4 (2025): Journal of Advanced Computer Knowledge and Algorithms - October 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i4.22886

Abstract

In the era of big data and artificial intelligence, data preprocessing has emerged as a critical step in the data science pipeline, influencing the quality, performance, and reliability of machine learning models. Despite its importance, the diversity of techniques, challenges, and evolving practices necessitate a structured understanding of this domain. This study conducts a systematic literature review (SLR) to explore current data preprocessing techniques, their domain-specific applications, associated challenges, and emerging trends. A total of 21 peer-reviewed articles from 2016 to 2024 were analyzed using well-defined inclusion and exclusion criteria, with a focus on machine learning and big data contexts. The results reveal that normalization, data cleaning, feature selection, and dimensionality reduction are the most commonly applied techniques. Key challenges identified include handling missing values, high dimensionality, and imbalanced data. Moreover, recent trends such as automated preprocessing (AutoML), privacy-preserving methods, and scalable preprocessing for distributed systems are gaining momentum. The review concludes that while traditional methods remain foundational, there is a shift toward adaptive and intelligent preprocessing strategies to meet the growing complexity of data environments. This study offers valuable insights for researchers and practitioners aiming to optimize data preparation processes in modern data science workflows
Challenges and Opportunities of Implementing Augmented Reality (AR) and Virtual Reality (VR) in Public Universities of Afghanistan Shahidi, Shahwali; Ali Frugh, Qurban; Kror Shahidzay, Amir
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 4 (2025): Journal of Advanced Computer Knowledge and Algorithms - October 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i4.22962

Abstract

This study explores the challenges and opportunities related to the implementation of Augmented Reality (AR) and Virtual Reality (VR) technologies in public universities in Afghanistan, with a focus on Kabul University. The research aims to assess the levels of awareness, perceived usefulness, readiness to adopt, and barriers affecting AR/VR integration in higher education. Using a quantitative research design, data were collected from 392 respondents comprising students, faculty, and administrative staff through a structured questionnaire. Descriptive analysis showed moderate awareness and positive perceptions of AR/VR’s potential benefits for enhancing learning. Inferential statistics revealed significant associations between respondents’ roles and their willingness to adopt AR/VR, as well as a strong positive relationship between digital literacy and perceived usefulness. Regression analysis identified awareness, digital literacy, and institutional support as key predictors of adoption readiness. The study highlights existing infrastructural and digital literacy challenges but emphasizes the promising potential of AR/VR to transform educational experiences in Afghan universities. The findings provide valuable insights for stakeholders aiming to promote innovative educational technologies in similar contexts.
Adoption of Cloud-Based Accounting Software in Afghanistan Medium-Sized Enterprises Hasas, Ansarullah; Wahid Samadzai, Abdul
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 4 (2025): Journal of Advanced Computer Knowledge and Algorithms - October 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i4.23156

Abstract

Cloud-based accounting software adoption has emerged as a critical factor in enhancing financial management and operational efficiency among medium-sized enterprises (MSEs) worldwide. In Afghanistan, where digital transformation is still evolving, understanding the factors influencing cloud accounting adoption is essential for driving sustainable business growth. This study investigates the awareness, motivations, barriers, and adoption patterns of cloud-based accounting software within Afghan MSEs across diverse sectors. Employing a quantitative research design grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology–Organization–Environment (TOE) frameworks, data were collected via structured online questionnaires from 50 key decision-makers in five representative companies. Descriptive and inferential statistical analyses were conducted using SPSS to examine adoption drivers and challenges. Findings reveal that while awareness and perceived benefits such as cost-efficiency and real-time access positively influence adoption, significant barriers including security concerns, inadequate infrastructure, and limited technical expertise hinder broader uptake. Sectoral differences further highlight variability in adoption readiness. The study underscores the importance of tailored strategies to enhance infrastructure, provide targeted training, and develop supportive regulatory frameworks to foster cloud accounting adoption in Afghanistan. These insights offer practical recommendations for policymakers and business leaders aiming to accelerate digital financial transformation within the country’s MSE sector. Limitations related to sample size and geographic focus are acknowledged, with suggestions for future research to explore rural contexts and longitudinal adoption trends.
Blockchain Applications in Network Engineering: From Secure Routing to Decentralized Identity Management Ranjbar, Obaidullah; Aziz Rastagari, Mohammad; Azimi, Sanayee; Danish, Jawad
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 4 (2025): Journal of Advanced Computer Knowledge and Algorithms - October 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i4.23670

Abstract

Blockchain technology has emerged as a promising solution to address critical security and privacy challenges in network engineering. Its decentralized, immutable, and transparent nature enables enhanced secure routing and decentralized identity management, which are vital for modern network infrastructures such as IoT, smart cities, and vehicular networks. This study systematically reviews recent literature from reputable databases, including IEEE Xplore, ScienceDirect, MDPI, ACM Digital Library, and Web of Science, covering publications from 2016 to 2024. Using a structured search and selection process, 29 relevant studies were analyzed to explore the effectiveness of blockchain-based secure routing mechanisms, features of decentralized identity frameworks, and the technical challenges related to blockchain integration. The findings reveal that blockchain significantly improves network security by preventing threats like route hijacking and Sybil attacks while empowering users with self-sovereign identity control. However, challenges such as energy consumption, latency, scalability, and interoperability remain critical obstacles. The review recommends further development of energy-efficient consensus algorithms, scalable architectures, and interoperability standards to enable broader adoption. Overall, blockchain presents a transformative approach for securing and managing complex network environments, provided that its limitations are carefully addressed in future research.
Novel Formulae for Digit Frequency Analysis in Natural Numbers: Positional Counting and Computational Applications Tahir Mohmand, Mohammad
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 4 (2025): Journal of Advanced Computer Knowledge and Algorithms - October 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i4.23794

Abstract

This research introduces a novel set of mathematical formulae for efficiently determining the frequency of individual digits within the set of natural numbers less than a given integer N. The study aims to derive a general closed-form expression that avoids iteration, based on the digit structure of N, and to develop a new positional operator for identifying digit placement within multi-digit numbers. The methodology is built on place-value analysis and the definition of a digit frequency function f(t;N), which incorporates a universal positional term fn and a helper function G(ai,t). The formula f(t;N) = fn + ∑G(ai,t) is proven to hold across numerical classes and is validated through extensive numerical testing up to 10^18. The research also introduces a novel mathematical operator, Ta = t-1, to determine the exact placement of a digit at the end of a number sequence. The results demonstrate a 92% improvement in computational efficiency compared to enumeration, with broad applications in number theory, coding, and pattern recognition. Additionally, the approach resolves a known non-linear radical system with exact solutions, showcasing the formulae's algebraic utility. In conclusion, this study contributes three new tools to mathematics: a closed-form digit frequency function, a terminal digit positional operator, and a novel solution method for radical equations.
Cover, Editorial Board, Acknowledgement and Table of Contents JACKA, JACKA
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 4 (2025): Journal of Advanced Computer Knowledge and Algorithms - October 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

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 (In Press)
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 (In Press)
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.

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