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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
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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 51 Documents
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 (In Press)
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.