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Journal of Computer Science Advancements
Published by Yayasan Adra Karima Hubbi
ISSN : 30263379     EISSN : 3024899X     DOI : https://doi.org/10.70177/jsca
Core Subject : Science,
Computer Science & IT
Journal of Computer Science Advancements is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of science, engineering and information technology. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the Journal of Computer Science Advancements follows the open access policy that allows the published articles freely available online without any subscription.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 120 Documents
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BIG DATA ANALYTICS FOR SUSTAINABLE GREEN SUPPLY CHAIN MANAGEMENT OPTIMIZATION MODELS Zain Nizam; Rashid Rahman; Muhammad Arif Abdul Hakim
Journal of Computer Science Advancements Vol. 4 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i2.3789

Abstract

The growing need for sustainable practices in global supply chains has driven the adoption of Big Data Analytics (BDA) to optimize performance and reduce environmental impact. Traditional supply chain management systems often fail to balance operational efficiency with sustainability goals, leading to increased waste and resource inefficiency. Big Data Analytics, by providing real-time insights, predictive models, and data-driven decision-making, offers a solution to this challenge. This research explores the application of BDA in the optimization of Sustainable Green Supply Chain Management (GSCM) models, focusing on how data-driven strategies can enhance both environmental and operational performance. The study employs a mixed-methods approach, combining case studies, performance metrics, and interviews with key industry stakeholders to assess the impact of BDA on supply chain efficiency, resource utilization, and waste reduction. The results show that BDA significantly improves key performance indicators, including a 20% increase in resource efficiency, a 25% reduction in waste, and a 15% decrease in operational costs. The study concludes that BDA is a crucial enabler for sustainable supply chains, providing organizations with the tools to optimize operations while minimizing their environmental footprint.
ENTERPRISE INFORMATION SYSTEMS ARCHITECTURE SUPPORTING E BUSINESS AND E GOVERNMENT DIGITAL TRANSFORMATION Ryan Teo; Fatimah Fahurian; Triyugo Winarko
Journal of Computer Science Advancements Vol. 4 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i2.3782

Abstract

The digital transformation of both e-business and e-government is increasingly dependent on the effective implementation of Enterprise Information Systems (EIS) architecture. As organizations seek to optimize their operations, enhance transparency, and improve service delivery, the need for robust EIS architectures has become critical. These architectures facilitate the integration of diverse systems, ensuring interoperability, scalability, and security. This study investigates how EIS architecture supports the digital transformation efforts in e-business and e-government, focusing on its impact on operational efficiency, data management, and stakeholder trust. A qualitative research design is employed, utilizing case studies, interviews, and document analysis from both public and private sector organizations. The findings reveal that EIS architecture significantly enhances the operational efficiency of both e-business and e-government, improving data security and reducing administrative bottlenecks. Institutions with fully integrated EIS reported improvements in service delivery, stakeholder satisfaction, and overall transparency. The research concludes that adopting modern EIS architectures is essential for successful digital transformation, particularly in sectors where data integrity and governance are paramount.
ARTIFICIAL INTELLIGENCE MODELS FOR PREDICTIVE ANALYTICS USING BIG DATA MINING TECHNIQUES Soleman Soleman; Ahmed Al Harthy; Mirza Ilhami
Journal of Computer Science Advancements Vol. 4 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i3.4104

Abstract

Rapid digital transformation has generated unprecedented volumes of heterogeneous data, creating significant opportunities for predictive analytics while simultaneously increasing challenges related to data quality, scalability, computational complexity, and decision reliability. Conventional predictive models frequently experience performance degradation when processing high-dimensional and continuously evolving Big Data environments. This study aimed to develop and evaluate an integrated Artificial Intelligence framework that combines advanced Big Data mining techniques with hybrid machine learning models to improve predictive accuracy, computational efficiency, and analytical robustness. Quantitative computational research was conducted using large-scale structured and semi-structured datasets processed through data preprocessing, feature engineering, dimensionality reduction, ensemble learning, deep learning, distributed computing, and hyperparameter optimization. Model performance was assessed using accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve, computational time, memory utilization, and scalability. Experimental results demonstrated that the proposed hybrid framework achieved 98.63% prediction accuracy, an AUC-ROC of 0.995, substantially reduced computational time, lower memory consumption, and superior scalability compared with conventional machine learning and deep learning approaches. Statistical analyses confirmed significant performance improvements across all principal evaluation metrics. Findings indicate that integrating intelligent data mining with Artificial Intelligence enhances predictive capability by optimizing the complete analytical pipeline rather than individual algorithms alone, providing a scalable, efficient, and reliable framework for predictive analytics across diverse Big Data application domains.
BEYOND THE PERIMETER: ASSESSING THE IMPACT OF ZERO TRUST ARCHITECTURE ON NETWORK LATENCY AND SECURITY RESILIENCE IN LARGE-SCALE ENTERPRISE ENVIRONMENTS Hadi Mardiyanto; Zainal Syahlan; Isnadi Isnadi; Safiullah Aziz
Journal of Computer Science Advancements Vol. 4 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i2.3859

Abstract

Enterprise networks increasingly confront sophisticated cyber threats and complex operational demands, rendering traditional perimeter-based security models inadequate. Zero Trust Architecture (ZTA) has emerged as a paradigm that emphasizes continuous verification, granular access control, and micro-segmentation to enhance security resilience across hybrid and large-scale environments. This study investigates the dual impact of ZTA on network latency and security outcomes, providing empirical insights into performance-security trade-offs.The research aims to evaluate how ZTA implementation affects network latency, throughput, and packet integrity while quantifying improvements in security resilience, including reductions in unauthorized access and lateral threat propagation. Insights from this study are intended to inform enterprise decision-making regarding optimized ZTA deployment. A mixed-methods approach was employed, combining quantitative measurements of latency, throughput, and packet loss across six enterprise networks with qualitative security assessments, including penetration testing and attack simulations. Data were analyzed using statistical techniques and thematic evaluation to identify patterns and interdependencies. Findings indicate that ZTA increases network latency moderately (3–7 ms) and reduces throughput minimally, while significantly enhancing security resilience, with a 70–85% reduction in successful unauthorized access attempts. Correlation analysis reveals a positive trade-off between performance impact and security improvements, emphasizing the importance of configuration optimization. Results confirm that ZTA provides robust protection without critically impairing network performance, offering practical guidance for large-scale enterprise adoption and informing future security-policy strategies.
SMART FARMING SYSTEMS USING INTERNET OF THINGS AND WIRELESS SENSOR NETWORKS Nana Sujana; Carlos Fernandez
Journal of Computer Science Advancements Vol. 4 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i4.4293

Abstract

Agricultural systems increasingly face challenges related to water scarcity, climate variability, resource inefficiency, and growing demands for sustainable food production, creating an urgent need for precise and responsive farming technologies. This study aimed to evaluate the effectiveness of an integrated smart farming system using the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) for environmental monitoring, precision irrigation, resource optimization, and agricultural decision-making. A field-based experimental design was conducted across 24 agricultural plots, comprising 12 IoT–WSN-assisted plots and 12 conventionally managed control plots. Environmental sensors continuously monitored soil moisture, temperature, humidity, and microclimatic conditions, while network and agricultural performance were evaluated using communication reliability, water consumption, response time, and crop productivity indicators. Results showed that IoT–WSN-assisted management reduced irrigation water consumption by approximately 27%, improved soil moisture stability, shortened response times to environmental changes, and increased crop yields compared with conventional management. High packet delivery, network availability, and data completeness supported reliable real-time decision-making. The study concludes that smart farming effectiveness depends on an integrated sensing–communication–processing–decision–action cycle that transforms reliable field data into timely agricultural interventions, offering a scalable framework for improving resource efficiency, productivity, and sustainable agricultural management under increasingly dynamic environmental conditions worldwide.
EXPLAINABLE AI FRAMEWORK FOR ROOT CAUSE ANALYSIS OF MARINE WELDING DEFECTS: INTEGRATING PROCESS PARAMETERS AND MICROSTRUCTURAL FAILURE MODES Mohammad Syaifi
Journal of Computer Science Advancements Vol. 4 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i4.4458

Abstract

Marine welding defects threaten structural reliability because conventional inspection and black-box prediction models can identify discontinuities without adequately explaining their physical origins. This study aimed to develop an Explainable Artificial Intelligence framework for root-cause analysis by integrating welding process parameters with microstructural failure modes. An experimental-computational design was applied to 120 AH36 marine-steel specimens produced by Gas Metal Arc Welding under controlled variations in current, voltage, travel speed, wire-feed rate, shielding-gas flow, and heat input. Weld quality was evaluated using visual inspection, ultrasonic testing, metallography, scanning electron microscopy, and Vickers microhardness, while Random Forest, XGBoost, and neural-network models were compared and interpreted using SHAP. Parameter variation enabled AI explanations to be checked against metallurgical evidence and defect morphology. XGBoost achieved the best performance, with 90.0% accuracy, a macro F1-score of 0.886, and an AUROC of 0.956. Heat input, shielding-gas flow, travel speed, and HAZ microhardness emerged as the dominant explanatory variables. Porosity was mainly associated with inadequate shielding, incomplete fusion and penetration with insufficient effective heat input, and cracking with elevated HAZ hardness. The study concludes that physically validated XAI can extend defect classification toward transparent, mechanism-informed diagnosis and provide a stronger basis for corrective decision-making in safety-critical marine welding.
DECONSTRUCTING THE NETWORK PERIMETER: A CONTINUOUS AUTHENTICATION FRAMEWORK FOR ZERO TRUST ARCHITECTURE IN CLOUD-NATIVE ENVIRONMENTS Abdillah Imam Julianto; Amimul Ummah Bay; Mahendra Mahendra
Journal of Computer Science Advancements Vol. 4 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i4.4352

Abstract

Cloud-native computing has weakened the traditional network perimeter by distributing identities, workloads, applications, and data across dynamic infrastructures where successful login no longer guarantees continuing trust. This study develops and evaluates a continuous authentication framework for Zero Trust Architecture that dynamically reassesses trust throughout active sessions. A design science approach combined with controlled experimentation was employed using 1,200 authenticated sessions, including 800 legitimate and 400 adversarial scenarios involving token hijacking, session replay, privilege escalation, compromised devices, lateral movement, and anomalous API activity. The framework integrated identity confidence, behavioral consistency, device posture, contextual risk, workload telemetry, and resource sensitivity into adaptive trust scoring and graduated enforcement. Results showed that continuous authentication achieved a 93.0% detection rate, 91.9% precision, 93.0% recall, and a 92.4% F1-score, while reducing mean compromised-session exposure from 11.8 to 1.9 minutes. Moderate increases in latency and computational overhead remained operationally acceptable, and false-positive rates did not increase significantly. Improvements were strongest against post-authentication compromise and identity misuse. The study concludes that Zero Trust is more effectively implemented as a dynamic, revocable, and context-sensitive trust process than as repeated static authentication. Continuous authentication therefore provides a practical mechanism for protecting both human and machine identities in decentralized cloud-native environments.
CONCEPTUALIZING THE APPLICATION OF ARTIFICIAL INTELLIGENCE (AI) IN THE MECHANISM FOR GENERATING VESSEL AIS DATA ANOMALY ANALYSIS REPORTS Ade Tya Guritna; Pebrianto Eko Nugroho; Sapto Budiarso
Journal of Computer Science Advancements Vol. 4 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i4.4375

Abstract

Automatic Identification System data generate continuous spatiotemporal information that supports maritime surveillance, yet converting detected vessel anomalies into reliable analytical reports remains challenging because statistical abnormality does not always indicate operational significance. This study aimed to conceptualize and evaluate an Artificial Intelligence-supported mechanism for generating evidence-grounded vessel AIS anomaly analysis reports. A Design Science Research approach was employed through AIS preprocessing, trajectory reconstruction, anomaly detection, contextual interpretation, explainability, evidence packaging, controlled natural-language generation, and expert validation. Findings showed that the anomaly-detection component achieved satisfactory predictive performance, while report quality improved substantially after provenance, contextual validation, and uncertainty controls were incorporated. Expert assessments indicated stronger factual consistency, traceability, contextual relevance, and uncertainty communication in the refined reports, while case analyses demonstrated that AI could distinguish observed AIS facts from model inference more effectively when constrained by structured evidence. The study concludes that generative AI should function as an evidence-constrained analytical communicator rather than an autonomous interpreter of vessel intent or maritime risk. The proposed architecture contributes an integrated framework for transparent, auditable, and human-verified AIS anomaly reporting and provides a foundation for future validation across diverse maritime environments, vessel classes, and operational contexts under realistic human-in-the-loop maritime decision-support conditions across waters.
ANALYSIS OF ARTIFICIAL INTELLIGENCE-BASED FLIGHT RISK ASSESSMENT AS A DECISION SUPPORT SYSTEM FOR ENHANCING NAVAL AVIATION SAFETY Arif Heri Nugroho; James Firman Gohan Siagian; Raka Dwi Audrus
Journal of Computer Science Advancements Vol. 4 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i4.4420

Abstract

Naval aviation safety requires reliable assessment of interacting environmental, technical, human, and organizational risks that may not be adequately represented through conventional methods. This study aimed to analyze an artificial intelligence-based flight risk assessment model as a decision support system for enhancing naval aviation safety while preserving professional human judgment. A mixed-methods case study design was employed using 240 non-sensitive historical flight-safety records and questionnaire responses from 30 aviation personnel. The analysis combined machine-learning classification, descriptive statistics, inferential testing, semi-structured interviews, and triangulation. The results showed strong predictive performance, with 91.7% accuracy, 89.8% precision, 87.6% recall, 88.7% F1-score, and 0.93 ROC-AUC. AI-generated risk scores were strongly associated with established risk categories (r = 0.81, p < 0.001), while environmental conditions represented the strongest predictive factor. User assessments indicated high perceived usefulness and interpretability, although decision-support confidence was comparatively lower. The study concludes that artificial intelligence can strengthen naval aviation safety by integrating complex risk information and supporting evidence-based assessment. Effective implementation requires explainability, data quality, continuous validation, human oversight, and institutional governance to ensure that AI complements rather than replaces qualified aviation professionals. The findings support a human-centered safety architecture in which algorithmic outputs remain advisory, transparent, and contextually evaluated.
AI-POWERED INTEGRATED MARITIME SURVEILLANCE: COMBINING SHORE-BASED RADAR, AIS, AND UAV COMPUTER VISION FOR AUTOMATED VESSEL TRACKING IN KOARMADA I OPERATIONS Budi Santoso Simanullang; Didi Efendi; Dany Wira Nugraha; Ahmad Faisol
Journal of Computer Science Advancements Vol. 4 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v4i4.4522

Abstract

Maritime surveillance complexity has increased due to expanding vessel activities, security challenges, and the demand for accurate real-time maritime domain awareness. This research aims to develop an AI-powered integrated surveillance framework combining shore-based radar, Automatic Identification System (AIS), and Unmanned Aerial Vehicle (UAV) computer vision for automated vessel tracking in Koarmada I operations. The study employs a mixed-methods technological development approach involving multi-source maritime data integration, artificial intelligence modeling, performance evaluation, and expert validation. The findings reveal that the integrated AI framework improves vessel detection accuracy, identification reliability, and tracking continuity compared with individual surveillance approaches. Radar contributes extensive spatial detection, AIS enhances vessel identification, and UAV computer vision provides visual verification capabilities. The integrated model demonstrates significant potential for reducing information gaps and strengthening maritime situational awareness. The research concludes that AI-driven sensor fusion represents an effective strategy for advancing naval surveillance systems by transforming fragmented maritime data into actionable operational intelligence. The proposed framework contributes to maritime security technology development and provides a foundation for future autonomous surveillance applications within complex operational environments. Future implementation requires broader datasets, adaptive algorithms, and extensive operational testing to ensure robustness across diverse maritime conditions and support sustainable defense information system transformation.

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