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JOINCS (Journal of Informatics, Network, and Computer Science)
Published by Universitas Muhammadiyah Sidoarjo
ISSN : -     EISSN : 25415123     DOI : https://doi.org/10.21070/joincs
Core Subject : Science,
Computer Science & IT
JOINCS publishes original research papers in computer science and related subjects in system science, with consideration to the relevant mathematical theory. Applications or technical reports oriented papers may also be accepted and they are expected to contain deep analytic evaluation of the proposed solutions. JOINCS also welcomes research contributions on the traditional subjects such as : Theory of automata, algorithms and its complexity. But not limited to contemporary subjects such as: • Big Data • Internet of thing (IoT) • Parallel & distributed computing • Computer networks and its security • Neural networks • Computational learning theory • Database theory & practice • Computer modelling of complex systems • Decentralized Systems • Information Management in the Enterprise Context • Database related technical solutions for Information Quality • Information Quality in the context of Computer Science and Information Technology • Game Techology • Information System
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 3 Documents
 1 
Search results for , issue "Vol. 9 No. 1 (2026): April" : 3 Documents clear
A Robust Hybrid CNN–LSTM Framework for High-Accuracy Zero-Day Intrusion and Ransomware Detection Using the UGRansome Dataset: A Robust Hybrid CNN–LSTM Framework for High-Accuracy Zero-Day Intrusion and Ransomware Detection Using the UGRansome Dataset Farah Hatem Khorsheed; Enas Abbas Abed; Zainab Hassan Mohammed; Walaa Badr Khudhair Alwan
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 9 No. 1 (2026): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v9i1.1688

Abstract

The rapid evolution of cyber-attacks—particularly zero-day intrusions and ransomware—has intensified the need for intelligent and resilient detection systems capable of handling imbalanced, high-dimensional network traffic. This research proposes a robust hybrid deep learning framework combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for enhanced anomaly detection using the UGRansome dataset, a realistic benchmark designed for ransomware and zero-day behavior analysis. The methodology integrates advanced preprocessing, including categorical encoding, feature normalization, and Synthetic Minority Over-sampling Technique (SMOTE) to alleviate class imbalance. The hybrid architecture leverages CNN layers for spatial feature extraction and LSTM layers for modeling temporal dependencies, enabling improved detection of emerging and stealthy threats. Experimental results demonstrate superior performance compared to standalone deep learning baselines, achieving 97.89% accuracy, 0.999 macro AUC, and strong detection capability across minority classes. Confusion matrix visualizations and classification metrics confirm the model’s robustness and generalization. The findings highlight the potential of hybrid deep learning models for proactive cybersecurity defense and establish a foundation for future intelligent intrusion detection systems
Federated Learning for Privacy-Preserving Big Data Analytics in Distributed Systems: Federated Learning for Privacy-Preserving Big Data Analytics in Distributed Systems Ahmed Gheni Dawood; Ekhlas Muthanna Turki
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 9 No. 1 (2026): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v9i1.1699

Abstract

Federated Learning (FL) is an important concept in big data analytics because it has changed the way collaborative model training can be done on devices that are decentralized while ensuring user privacy, an essential requirement in an accurate evidence-based and regulated environment with even stricter requirements from regulations like GDPR, HIPAA, CCPA and future laws on data sovereignty. This paper analyzed FL in depth. It described foundational concepts, architectural approaches, algorithmic approaches, real-world and practical applications and challenges in distributed systems. Key issues such as communication overhead, data heterogeneity, security risks, fairness, scalability, energy efficiency and compliance with regulations were also discussed and analyses were provided on any underpinning implications on FL performance. Seven tables provide comprehensive overviews of the algorithms, datasets, metrics of performance and applications, while nine figures in unique styles visualize trends, comparisons and data analytics to aid readability. Applications were provided in healthcare, IoT, financial sectors, smart cities and autonomous systems which lend evidence to the promise of FL as a revolutionary technology for privacy-respecting related analytics. Future directions for integrating FL highlights potential synergies with emergent technology such as quantum computing, blockchain, edge artificial intelligence and federated generative models, with supported rationales and inferences when necessary. This work provides a comprehensive and definitive reference point to enhance the scope and level of enquiry for researchers and practitioners who are trying to advance the development of distributed machine learning in sensitive situations to ultimately support the emergence of secure, scalable, ethical, and privacy-preserving analytics, which can drive future paradigm shifts
Development of an Automated Attendance System Based on Facial Recognition Using Convolutional Neural Networks (CNN) for Kaca Super Jaya MSME: Pengembangan Sistem Kehadiran Otomatis Menggunakan Pengenalan Wajah Menggunakan Convolutional Neural Network (CNN) terhadap UMKM Kaca Super Jaya Syaeful Anas Aklani; Jetset; Suwarno Suwarno
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 9 No. 1 (2026): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v9i1.1692

Abstract

Attendance management is a critical component of human resource administration, yet conventional methods such as manual sign-in sheets and card-based systems are often inefficient, error-prone, and vulnerable to manipulation. This study aims to design and implement an automatic attendance system based on face recognition using Convolutional Neural Networks (CNN) for UMKM Kaca Super Jaya. The proposed system replaces manual attendance by enabling real-time, contactless, and automated attendance recording through facial identification. An applied research approach with qualitative methods was employed, involving system development, direct observation, and structured interviews with users. The CNN model was trained using facial image datasets under various conditions, including different lighting levels, facial expressions, and viewing angles, to improve robustness and accuracy. The system architecture integrates a camera as input, a CNN-based face recognition model, a backend server, and a web-based dashboard for attendance monitoring and reporting. Experimental results show that the system achieved an average face recognition accuracy of 96%, demonstrating reliable performance even under suboptimal lighting and non-frontal face angles. The implementation significantly reduced attendance processing time, minimized human error, and lowered the potential for fraudulent practices such as proxy attendance. These findings indicate that CNN-based face recognition is an effective and practical solution for enhancing attendance management efficiency and accuracy in small and medium enterprises.

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