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All Journal Jurnal Sastra Aksara KAIBON ABHINAYA : JURNAL PENGABDIAN MASYARAKAT Jurnal Masyarakat Maritim Mabny : Journal of Sharia Management and Business Jurnal Impresi Indonesia Jurnal Akuntansi Manajemen (JAKMEN) Jurnal Masyarakat Mengabdi Nusantara Journal of Educational Management Research MULTIPLE: Journal of Global and Multidisciplinary Jurnal Pendidikan Agama Islam GEMBIRA (Pengabdian Kepada Masyarakat) Babakti : Journal of Community Engangement International Conference on Islamic Studies Digitus : Journal of Computer Science Applications Novatio : Journal of Management Technology and Innovation
Abdurrohman
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Journal : Digitus : Journal of Computer Science Applications

 1 
Adaptive and User-Centered HCI for Intelligent Technologies: A Global Perspective Abdurrohman
Digitus : Journal of Computer Science Applications Vol. 2 No. 4 (2024): October 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v2i4.854

Abstract

Human-Computer Interaction (HCI) within intelligent systems plays a critical role in shaping user experience, particularly through effective design, usability, and accessibility. This narrative review aims to synthesize current research trends and challenges in designing inclusive and adaptive HCI environments. Literature was gathered from Scopus and Google Scholar using keywords such as "Human-Computer Interaction," "Intelligent Systems," "Usability," "Accessibility," and "User-Centered Design." Articles were selected based on inclusion criteria focusing on recent, peer-reviewed studies that explore empirical, review, and case-based methodologies. The results highlight that effective user interface design is rooted in multimodal, emotionally aware, and cognitively efficient interactions. AI-enhanced features and adaptive layouts contribute to a more intuitive experience, particularly in healthcare and smart vehicle environments. Usability assessments, including the System Usability Scale and A/B testing, further validate user engagement and system effectiveness. Accessibility remains a crucial yet underrepresented theme, with a significant disparity in inclusive design for vulnerable populations. Notably, best practices from countries with strong accessibility policies underscore the importance of integrating users with disabilities into the design process. The discussion points to systemic factors—such as regulatory frameworks, digital literacy, and funding priorities—as both barriers and enablers of progress. To bridge existing gaps, the study recommends further longitudinal, cross-cultural, and inclusive research. Strengthening digital education and accessibility policies is key to enhancing user-centered innovation in intelligent systems.
Hybrid Deep Learning Models for Intrusion Detection in Cloud Networks: A Benchmark-Based Comparative Study Abdurrohman; Arainy, Corizon Sinar
Digitus : Journal of Computer Science Applications Vol. 2 No. 1 (2024): January 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v2i1.1116

Abstract

The increasing complexity of cyber threats targeting cloud infrastructures demands advanced and adaptive intrusion detection systems (IDS). This study explores the application of deep learning (DL) models—Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and a hybrid CNN+BiLSTM architecture—for detecting network intrusions using benchmark datasets CIC-IDS2017 and UNSW-NB15. This study contributes by demonstrating how hybrid CNN+BiLSTM architectures enhance intrusion detection accuracy on benchmark datasets, offering low latency and improved recall for rare attack classes, thereby validating their suitability for real-time cloud security deployment. Results show that hybrid CNN+BiLSTM models outperform standalone CNN and LSTM architectures in detection performance, achieving accuracies up to 97.4% on CIC-IDS2017 and 96.85% on UNSW-NB15, while maintaining acceptable latency for real-time deployment. The hybrid model also demonstrates superior F1-scores for rare attack classes and lower false positive rates. The discussion highlights the importance of dataset quality, feature engineering, and the role of adversarial training and model optimization in enhancing robustness. In conclusion, this work affirms the value of hybrid DL architectures for cloud-based IDS and suggests future directions in federated learning, adaptive retraining, and deployment in edge environments.
Co-Authors Abd Hakim, Abd Abdurrohman, Oman Ahmad Soderi Akhmad Shunhajir Aminuyati Anis Fuad Salam Anjelis Ratu Wulan Anwar T Sitorus Arainy, Corizon Sinar Azizah, Reyhana Nur Baehaqi Chandra, Muhammad Robby Corizon Sinar Arainy Daniel Happy Putra Diantoro, Karno Dwi Fitrianingsih Elvianora, Vivi Fatma Lestari Heri Hermansyah Hikmat, Imat Iman Saifullah Indar Riyanto Karno Diantoro M, Atep Effiana Taufik Muhammad Farid Muhammad Ikrar Yamin Najibur Rohman Nisa Ulyantika, Sofia Nourma Ulva Nurul Lizam Rinaldo Romlah Sakrim Salam, Anis Fuad Samroh Saromah Siskandar Sitorus, Anwar T Soderi, Ahmad Sri Wahyuni Syafitri, Rahma Tajudin Takwallo Tamaulina Br Sembiring Wijaya Putri, Melyna Wulan, Anjelis Ratu Yamin, Muhammad Ikrar Zakaria, Ricky Muhamad