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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 6 Documents
 1 
Search results for , issue "vol. 4 no. 4 (2026)" : 6 Documents clear
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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