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Scientific Journal of Computer Science
Published by PT. Teknologi Futuristik Indonesia
ISSN : -     EISSN : 31103170     DOI : https://doi.org/10.64539/sjcs
Core Subject : Science,
Computer Science & IT
The Scientific Journal of Computer Science (SJCS) (e-ISSN: 3110-3170) is a peer-reviewed and open-access scientific journal, managed and published by PT. Teknologi Futuristik Indonesia in collaboration with Universitas Qamarul Huda Badaruddin Bagu and Peneliti Teknologi Teknik Indonesia. The SJCS dedicated to publishing high-quality research across all areas of computer science, with a particular focus on emerging technologies that are shaping the future of computing. SJCS invites original research, review papers, and studies that involve practical applications, simulations, and theoretical advancements. The journal scope includes, but is not limited to: Artificial Intelligence and Machine Learning Data Science and Big Data Cybersecurity and Cryptography Cloud Computing and Distributed Systems Software Engineering Human-Computer Interaction Computer Vision and Natural Language Processing Internet of Things (IoT) Blockchain Technologies Robotics and Automation Computational Biology and Bioinformatics All fields related to computer science SJCS aims to advance the development of innovative computing systems that contribute to technological progress across industries.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 18 Documents
 1   2 
An Enterprise Agentic Architecture Framework for Agentic AI Governance and Scalable Autonomy Venkiteela, Padmanabham
Scientific Journal of Computer Science Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i1.2026.368

Abstract

The rise of agentic artificial intelligence is changing how businesses operate, manage systems, and oversee digital workflows. These systems are different from normal automation or standalone AI models because they rely on structured thinking secure tool usage advanced teamwork between multiple agents, and ongoing feedback in complex environments with hybrid and multi-cloud systems. But there is a major issue businesses don’t have a clear framework to and use and expand agentic AI while staying compliant. This document tackles that problem by presenting the Enterprise Agentic Architecture Framework. This is a detailed multi-layered reference model built to help large organizations safely and use and manage agentic AI on a bigger scale. EAAF is built on six key layers: infrastructure, enterprise integration, orchestration and coordination, governance and safety, agent intelligence, and agent interaction. A central Control Plane ties all these layers together. The Control Plane plays a major role in managing policies, identity, scheduling, observability, and controlling the lifecycle of individual agents as well as multi-agent systems. Tests on real-world enterprise cases like Opportunity-to-Order automation, DevOps and AIOps pipelines, integration workflows, and collaboration across multiple agents in different domains show that EAAF improves autonomy, ensures reliable reasoning, boosts efficiency in execution, and strengthens operational resilience. Tests reveal significant boosts such as workflows running 3 to 10 times faster, cutting the average resolution time (MTTR) by 60 to 80 percent, and clear improvements in safety guided by policies. To sum up, EAAF acts as a key framework to build future enterprise AI systems. It ensures safe autonomy, sets up consistent architecture, and organizes agent-driven operations for critical tasks.
Exploring Quantum Machine Learning in Solving Complex Optimization Problems: Algorithms and Insights Nawaz, Uzma; Saeed, Zubair; Atif, Kamran
Scientific Journal of Computer Science Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i1.2026.396

Abstract

Optimization problems across domains such as logistics, finance, and artificial intelligence often involve complex and NP-hard formulations that are computationally challenging for classical algorithms due to scalability and efficiency limitations. The study aims to systematically investigate the role of Quantum Machine Learning (QML) in addressing complex optimization problems and to analyze its advantages over traditional optimization techniques. A comprehensive survey and comparative analysis of key QML algorithms, including Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), Quantum Neural Networks (QNNs), and Quantum Support Vector Machines (QSVMs), is conducted by examining their working principles, optimization capabilities, and real-world applications. The findings indicate that QML algorithms demonstrate significant potential in exploring large solutions spaces efficiently, achieving faster convergence, and providing improved optimization performance compared to classical approaches, although challenges such as quantum noise, scalability, and hardware limitations remain. The novelty of this study lies in providing a unified and critical comparative framework that integrates multiple QML optimization algorithms, highlights their practical feasibility, and identifies key research gaps hindering their real-world deployment. The implications of this research provide valuable insights for researchers and practitioners in selecting appropriate QML techniques and emphasize the need for advancements in hybrid quantum -classical systems, algorithms design, and quantum hardware to enable practical large-scale optimization.
IoMT Device Performance in Chronic Disease Management: A Mixed-Methods Clinical Validation in a Public LMIC Healthcare System Silva Atencio, Gabriel
Scientific Journal of Computer Science Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i1.2026.406

Abstract

The Internet of Medical Things (IoMT) holds promise for chronic disease management, yet evidence from low- and middle-income countries (LMICs) remains scarce, limiting equitable digital health policy development. No previous studies have clinically validated IoMT device performance against electronic health records (EHRs) within Central American public healthcare systems, leaving assumptions about consumer device adequacy untested in resource-constrained settings. This mixed-methods study evaluated IoMT implementation in Costa Rica's Caja Costarricense de Seguro Social (CCSS) system, examining (1) diagnostic accuracy stratification between consumer and clinical-grade devices, (2) healthcare system interoperability challenges, and (3) cost implications of false alerts. Among 50 chronic disease patients, consumer wearables demonstrated 43% sensitivity for cardiac event detection versus 92% for clinical-grade devices (p<0.001). Only 25% of consumer devices integrated with CCSS EHRs versus 100% of clinical-grade devices, requiring 22 minutes of manual data entry per encounter. False positives occurred in 12% of consumer-device alerts, costing an average of $35 per event. Qualitative analysis revealed that 45% of participants overestimated consumer-device diagnostic capabilities. These findings challenge assumptions about universal consumer-technology applicability in LMICs and support tiered implementation frameworks. As Costa Rica prepares for its Digital Health Act 2025, evidence-based device categorization, interoperability investments, and patient education are essential for equitable IoMT integration.
Breaking Class Imbalance Barriers in Intrusion Detection Systems: A Clustering-Based Hybrid Framework Hambali, Moshood Abiola; Bako, Nahum Zhema; Dalhatu, Mu’awuya; Ishaq, Ashraf
Scientific Journal of Computer Science Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i1.2026.378

Abstract

Intrusion Detection Systems (IDS) deal with issues concerning the ever-escalating level of sophistication observed within cyber threats. Nonetheless, IDS performance is deteriorated by class imbalance and excessively high-dimensional features, which cause biased classifier training towards major traffic patterns. Thus, this research introduces an innovative hybrid clustering IDS approach that utilizes MiniBatchKMeans clustering and ensemble machine learning strategies to mitigate these challenges. The suggested IDS approach utilizes the Synthetic Minority Over-sampling Technique for addressing class imbalance problems, Fast Correlation-Based Filter for reducing high-dimensional features, and Hyperopt Tree-structured Parzen Estimator for optimizing clustering and machine classifiers' parameters. Four supervised machine classifiers — Decision Tree classifier, Random Forest classifier, Extra Trees classifier, and XGBoost classifier— were trained and validated on the NSL-KDD IDS dataset. Additionally, experimental analysis indicated a superior detection accuracy for all classifiers, for which the best-optimized XGBoost classifier and best-optimized Random Forest classifier provided 99.57% and 99.51% accuracy, respectively. The proposed clustering-optimized machine IDS approach provided substantial improvements for identifying minority class attacks, along with sustainability and high generalization capabilities. The obtained outcomes support the research premise concerning the efficacy of cluster-aware sampling and ensemble optimizations for designing more balanced, accurate, and adaptive IDS systems for effectively protecting against ever-escalating real-life threats within the cyberworld.
A Self-Reflection Mechanism for Reducing Hallucination in Vietnamese Legal Question Answering Systems Pham, Thi Vuong; Phan, Nguyet Minh; Cao, Bao Quynh; Truong, Cong Phuc; Nguyen, Thanh Duy; Tien, Minh Vy; Nguyen, Ngoc Son
Scientific Journal of Computer Science Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i1.2026.380

Abstract

Legal question answering is essential for compliance, dispute resolution, and everyday HR decision-making, yet large language models may produce persuasive but incorrect legal statements when supporting evidence is incomplete. While Retrieval-Augmented Generation and graph-based retrieval can ground responses in statutes and structured relations, Vietnamese legal QA often lacks an explicit, automated quality-control step that scores an answer, decides whether it should be refined, and checks that citations are actually supported. In this paper, we propose a self-reflection mechanism that adds an iterative generate–evaluate–refine loop to a Graph-RAG pipeline for Vietnamese labor-law questions. Each draft is evaluated with a hybrid score that combines how closely the answer matches retrieved legal context with a model-derived confidence estimate, and the system iterates until it reaches a quality threshold or a stopping limit. On a Vietnamese Labor Law benchmark, the approach improves accuracy from 81.5% to 86.7% and reduces hallucination from 18.7% to 9.3%, with only a modest increase in end-to-end latency in typical use. We also examine component contributions and remaining failure cases, finding that pairing contextual alignment with confidence produces more stable answers than relying on a single signal. These results indicate that self-reflection can serve as a lightweight, deployment-friendly safety layer for high-stakes legal QA without requiring additional labeled data or model fine-tuning, and it can be adapted to other Vietnamese legal domains that demand transparent, article- and clause-level evidence.
Hybrid Deep Learning Model for Fake News Detection on Social Media Using CNN-GRU on X formerly known as Twitter Muhammad, Lawan Jibril; Mohammed, Isa Umar; Sani, Nura Muhammad
Scientific Journal of Computer Science Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i1.2026.400

Abstract

The spread of fake news on social media platforms has created a dilemma for the world community by spreading false information and eroding public confidence. Fake news spreads quickly and seriously harms society. Predicting and identifying fake news is crucial for preserving the integrity of information ecosystems in the wake of an epidemic of multiple high-profile disinformation efforts. In order to detect fake news, this work suggests a hybrid deep learning algorithm called Convolutional Neural Network - Gated Recurrent Unit (CNN-GRU), which combines the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) learning algorithms in an efficient manner. Models for identifying fake news were developed using deep learning-based methods, such as CNN, GRU, and CNN-GRU deep learning algorithms. Four standard performance metrics—accuracy, precision, recall, and F1-score—were used to evaluate the models. Nevertheless, the CNN-GRU deep learning-based detection model outperformed models created with CNN and GRU, achieving the maximum accuracy of 98.77%, 98.68%, 98.73%, and 98.71% for precision, recall, and F1-score, respectively. With a combined accuracy of 98.77%, precision of 98.68%, recall of 98.73%, and F1-score of 98.71%, the CNN-GRU deep learning-based false news detection model performs better than the two other deep learning-based models.
A Convolutional Neural Network Framework for Intelligent Intrusion Detection Oise, Godfrey Perfectson; Olanrewaju, Babatunde Seyi; Orukpe, Oshoiribhor Austin; Pius, Kevin Chinedu; Airhiavbere, Augustine Osazee
Scientific Journal of Computer Science Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i1.2026.404

Abstract

The rapid expansion of cloud computing, Internet of Things (IoT), and distributed network environments has significantly increased vulnerability to sophisticated cyber threats, exposing the limitations of traditional signature-based intrusion detection systems. Although deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown promising performance in intrusion detection, challenges related to validation transparency, statistical reliability, and interpretability remain inadequately addressed. This study proposes an intelligent CNN-based intrusion detection framework designed to improve detection accuracy, robustness, and model explainability. The framework is evaluated using the UNSW-NB15 benchmark dataset, which reflects realistic modern cyber-attack scenarios. A comprehensive preprocessing pipeline involving data cleaning, categorical encoding, feature normalization, and data reshaping is applied to enhance learning efficiency. To ensure unbiased evaluation, stratified k-fold cross-validation and an independent held-out test set are employed. Experimental results demonstrate that the proposed CNN achieves a test accuracy of 91.8%, with balanced precision, recall, and F1-score across benign and malicious traffic classes. Multi-class detection analysis further confirms the model’s capability to distinguish among diverse attack categories. Statistical validation using mean performance metrics, standard deviation, and confidence intervals demonstrates stable generalization performance. Additionally, Gradient-weighted Class Activation Mapping (Grad-CAM) is used to enhance interpretability by identifying network-level features that influence classification decisions. An ablation study further validates the effectiveness of key architectural components. The results indicate that the proposed framework provides a reliable, scalable, and interpretable solution for intelligent intrusion detection in modern high-dimensional network environments.
Performance Evaluation of Tree-Based Machine Learning Algorithms for Medical Relief Supply Demand Forecasting Villones, Roman Bariring; Vargas, Janette Templonuevo
Scientific Journal of Computer Science Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i1.2026.407

Abstract

Accurate demand forecasting is critical in medical relief supply chains where prediction errors can lead to stockouts, delayed response, or inefficient allocation of limited resources. While machine learning (ML) approaches have demonstrated superior predictive capabilities compared to traditional statistical methods and existing research often treats ML as a homogeneous category and rarely conducts systematic benchmarking within specific algorithm families. Furthermore, many studies rely on default model configurations that limiting the reproducibility and failing to fully assess its robustness under volatile demand conditions common in humanitarian logistics. This study addresses these gaps by systematically evaluating multiple tree-based machine learning algorithms for medical relief supply demand forecasting under a structured framework. The research integrates GridSearchCV as hyperparameter optimization, repeated K-fold cross-validation, and statistical significance testing to ensure fair comparison and robustness assessment. The findings indicate that advanced gradient boosting models outperform single-tree and simpler ensemble approaches in terms of predictive accuracy and stability. CatBoost consistently achieved the lowest prediction errors, the narrowest residual dispersion, and the most stable cross-validation performance. Although statistically comparable to other advanced boosting frameworks, CatBoost demonstrated superior robustness during volatile demand conditions and demand surges. These results provide both methodological and practical contributions by establishing a benchmarking framework and identifying a stable forecasting model that suitable for operational deployment in AI-driven medical relief inventory system.

Page 2 of 2 | Total Record : 18

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