cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
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
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 59 Documents
 2   3   4   5   6 
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

Comparison of the K-Nearest Neighbor and Random Forest Methods in Classifying the Best Selling Medicines at Khan Pharmacy Matang Glumpang Dua Putri, Anya Regina; Rozzi Kesuma Dinata; Maryana
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 2 (2026): Journal of Advanced Computer Knowledge and Algorithms - April 2026 (In Press)
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i2.25183

Abstract

Khan Matang Glumpang Dua Pharmacy faces difficulties in analyzing drug sales patterns that affect inventory efficiency and customer satisfaction. The need to anticipate demand and reduce the risk of stockouts or excess stock requires an effective classification system for best-selling drugs. This study aims to test the K-Nearest Neighbor (KNN) and Random Forest methods to perform and find the best classification model. The data used in this study consisted of 382 data points. This study compared two classification models on pharmacy sales data. The K-Nearest Neighbor (KNN) model was tested using the parameter k=3, while the Random Forest model was tested with 100 trees and a max depth of 5. The results showed that the KNN and Random Forest (RF) algorithms. The Random Forest (RF) model outperformed KNN on all metrics: RF achieved an Accuracy and F1-Score of 94.81%, while KNN recorded an Accuracy of 93.51% and an F1-Score of 93.44%.
Classification of Hospital Stay Duration for Schizophrenia Patients at RSUD Muyang Kute Using a Combination of C4.5 and Particle Swarm Optimization Putri Agustina Dewi; Munirul Ula; Said Fadlan Anshari
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 2 (2026): Journal of Advanced Computer Knowledge and Algorithms - April 2026 (In Press)
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i2.25930

Abstract

Schizophrenia is a chronic mental disorder that often requires inpatient care, so an increase in the number of patients can lead to limited bed capacity in psychiatric wards. This study aims to classify the length of hospital stay for schizophrenia patients to support room requirement planning at RSUD Muyang Kute using the C4.5 algorithm optimized with Particle Swarm Optimization (PSO). The dataset consists of 657 medical records of inpatient schizophrenia cases from February 2023 to March 2025, categorized into three length-of-stay classes: short (1–5 days), medium (6–10 days), and long (>10 days). The C4.5 algorithm is used to construct a decision tree model based on historical data, while PSO is employed as an optimization method to improve the model configuration. The evaluation uses classification accuracy and Mean Absolute Percentage Error (MAPE) for room demand estimation. The results show that both the C4.5 and C4.5–PSO models achieve similarly high accuracy on the test data, while the manual MAPE calculation for room demand estimation yields a value of 52.66%. In contrast, the MAPE calculated by the system is 0.00% in the test scenario because all classes in the test data are correctly predicted. The web-based decision support system developed using Python and Streamlit is able to automatically provide predictions of length of stay and estimates of the required number of psychiatric beds at RSUD Muyang Kute.
Comparison of Coffee Bean Sales Predictions at the Ketiara Coffee Traders Cooperative (KOPEPI) Using Linear Regression and Random Forest Methods Syadzwina, Nada; Defry Hamdhana; Ar Razi
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 2 (2026): Journal of Advanced Computer Knowledge and Algorithms - April 2026 (In Press)
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i2.25974

Abstract

Most of Indonesia's land is used for agriculture and plantations because it is an agrarian country. Harvests or agricultural products can be exported to help the country's economic recovery. Coffee, the most traded tropical crop in the world, is one of the most valuable commodities. Approximately 25 million farming households contribute up to 80% of global coffee production (FAO Organizational 2023). Indonesia's coffee industry continues to experience significant annual growth. To optimize their production and distribution, Indonesian coffee producers must understand coffee bean sales trends. This study compares two methods for predicting coffee bean sales at the KOPEPI Ketiara Aceh Tengah Cooperative using Linear Regression and Random Forest methods. The research methods used in this study are data collection and system design. The results show a comparison of the Linear Regression and Random Forest methods in predicting coffee bean sales. Linear regression provides fairly good accuracy for the price variable with low MAPE values (3.35%–4.55%) and MAE that is still within reasonable limits, but produces large prediction errors for the export variable with high MAPE (67.84%–80.65%) and large MAE (5982–7960). In contrast, Random Forest shows superior performance with very low MAPE (2.69%–3.46%) and smaller MAE (4275–6038) on price variables, as well as more stable and consistent export predictions even though the MAPE values are still quite high (54.25%–84.97%). Overall, Random Forest is a more appropriate model to use because it provides accurate price predictions and more consistent export performance compared to Linear Regression.
A Comparative Analysis of Machine Learning Models for Climate Change Prediction and Climate Risk Assessment Ahmad Farhad Rajab Zada
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 2 (2026): Journal of Advanced Computer Knowledge and Algorithms - April 2026 (In Press)
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i2.26648

Abstract

Climate change represents one of the most pressing challenges facing humanity, and accurate prediction models are essential for effective risk assessment and policy formulation. This systematic literature review employs thematic analysis to examine and compare machine learning (ML) models applied to climate change prediction and climate risk assessment, synthesizing findings from 40 peer-reviewed studies published between 2015 and 2024. Five major thematic clusters were identified: (1) deep learning architectures for temperature and precipitation forecasting; (2) ensemble methods for extreme weather event prediction; (3) hybrid physics-informed neural networks; (4) spatiotemporal models for sea-level rise and glacier dynamics; and (5) ML-based climate risk assessment frameworks for socioeconomic impact modeling. Findings reveal that Long Short-Term Memory (LSTM) networks and Transformer-based architectures consistently outperform traditional statistical models for long-range climate forecasting, while gradient boosting methods excel in regional risk classification tasks. Physics-informed neural networks demonstrate superior interpretability and generalization in data-scarce environments. The review identifies significant research gaps including model interoperability, uncertainty quantification, and the integration of socioeconomic variables. Future research should focus on federated learning approaches and explainable AI frameworks to enhance transparency and stakeholder trust.
AI-based Phishing Attacks on University Networks: A Systematic Literature Review and Defense Framework Saidamin Sajid; Abdul Wajid Fazil; Musawer Hakimi
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 2 (2026): Journal of Advanced Computer Knowledge and Algorithms - April 2026 (In Press)
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i2.26650

Abstract

Phishing continues to be a major issue not only affecting the internet users but also being a big problem for the cybersecurity world; university networks are the most probable attack targets due to their open infrastructures, diverse user population, and limited security resources. The breakthrough in artificial intelligence (AI), notably large language models, has not only made phishing attacks more sophisticated and realistic but also envisioning new defense techniques based on machine learning and natural language processing. This current research report is a systematic literature review (SLR) of 53 academic studies that examine the dual aspect of AI in promoting and hindering phishing attacks in higher education institutions (HEIs). The review reveals three prominent points: emails sent by AI are increasingly real and adaptable; AI-based detection systems are very effective in laboratory-like conditions but struggle against new and adversarial attacks; and human factors like lack of user awareness and slow incident reporting are still the main vulnerabilities. The research then proposes a multi-layered defense framework that includes infrastructure strengthening, AI detection, human-centered awareness training, incident response mechanisms, and governance policies. This framework provides a practical roadmap for HEIs to boost their cybersecurity resilience and play a part in the sustainable growth of the university.

Page 6 of 6 | Total Record : 59

 2   3   4   5   6 

Filter by Year

2024 2026


Reset
Filter By Issues
All Issue Vol. 3 No. 2 (2026): Journal of Advanced Computer Knowledge and Algorithms - April 2026 (In Press) Vol. 3 No. 1 (2026): Journal of Advanced Computer Knowledge and Algorithms - January 2026 Vol. 2 No. 4 (2025): Journal of Advanced Computer Knowledge and Algorithms - October 2025 Vol. 2 No. 3 (2025): Journal of Advanced Computer Knowledge and Algorithms - July 2025 Vol. 2 No. 2 (2025): Journal of Advanced Computer Knowledge and Algorithms - April 2025 Vol 2, No 1 (2025): Journal of Advanced Computer Knowledge and Algorithms - January 2025 Vol 1, No 4 (2024): Journal of Advanced Computer Knowledge and Algorithms - October 2024 Vol 1, No 3 (2024): Journal of Advanced Computer Knowledge and Algorithms - July 2024 Vol 1, No 2 (2024): Journal of Advanced Computer Knowledge and Algorithms - April 2024 Vol 1, No 1 (2024): Journal of Advanced Computer Knowledge and Algorithms - January 2024