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Digitus : Journal of Computer Science Applications
Published by PT Penerbit Ilmiah Indonesia
ISSN : -     EISSN : 30313244     DOI : https://doi.org/10.61978/digitus
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Digitus : Journal of Computer Science Applications with ISSN Number 3031-3244 (Online) published by Indonesian Scientific Publication, is a leading peer-reviewed open-access journal. Since its establishment, Digitus has been dedicated to publishing high-quality research articles, technical papers, conceptual works, and case studies that undergo a rigorous peer-review process, ensuring the highest standards of academic integrity. Published with a focus on advancing knowledge and innovation in computer science applications, Digitus highlights the practical implementation of computer science theories to solve real-world problems. The journal provides a platform for academics, researchers, practitioners, and technology professionals to share insights, discoveries, and advancements in the field of computer science. With a commitment to fostering interdisciplinary approaches and technology-driven solutions, the journal aligns itself with global challenges and contemporary technological trends.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 5 Documents
 1 
Search results for , issue "Vol. 3 No. 2 (2025): April 2025" : 5 Documents clear
Cloud-Native Transformations: Microservices, Kubernetes, and Security Frameworks in Practice Munthe, Era Sari
Digitus : Journal of Computer Science Applications Vol. 3 No. 2 (2025): April 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i2.880

Abstract

Cloud-native application development is reshaping how modern organizations build, deploy, and manage software. This narrative review aims to synthesize recent literature on the adoption of cloud-native paradigms, particularly focusing on microservices architecture, containerization, orchestration tools, security frameworks, and AI-driven resource management. Using Scopus, IEEE Xplore, ACM Digital Library, SpringerLink, and Google Scholar as primary databases, the review applies Boolean keyword combinations to identify relevant peer-reviewed publications. Studies were selected based on their alignment with defined inclusion criteria, emphasizing empirical insights on cloud-native technologies. The findings reveal that microservices enhance system scalability and business agility, while containerization offers portability and efficient resource utilization. Orchestration tools, especially Kubernetes, enable automated deployment and management across complex environments. Security integration through DevSecOps and Policy-as-Code frameworks strengthens defense mechanisms against cyber threats. Furthermore, AI-supported orchestration improves efficiency in resource allocation and system responsiveness. The discussion underscores the necessity of systemic support, including organizational policies, talent development, and cross-functional collaboration, in ensuring successful adoption. This review concludes that cloud-native success demands more than technical innovation; it requires strategic alignment between technology, human capital, and governance. Policymakers and organizational leaders must invest in comprehensive frameworks that support security, adaptability, and continuous learning. Future studies should expand the scope by evaluating cloud-native transformations across industries and developing scalable best practices for AI integration and policy deployment.
Toward Resilient Networks: AI and Deep Learning Strategies for Intrusion Detection Marthalia, Lia
Digitus : Journal of Computer Science Applications Vol. 3 No. 2 (2025): April 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i2.881

Abstract

As cyber threats become more sophisticated and pervasive, the demand for advanced Network Intrusion Detection Systems (NIDS) has increased dramatically. This narrative review investigates the application of Artificial Intelligence (AI) and Deep Learning (DL) techniques in enhancing NIDS performance, aiming to address the limitations of conventional rule-based systems. The literature was systematically retrieved from reputable databases such as Scopus and IEEE Xplore using keywords including "Network Intrusion Detection," "Deep Learning," and "Cybersecurity." Inclusion criteria focused on peer-reviewed studies that utilized AI models for intrusion detection, particularly within complex domains like IoT and smart grids. The review identifies CNN, LSTM, and DNN as the dominant AI models employed in modern NIDS, achieving detection accuracies ranging from 88% to 99% across benchmark datasets such as NSL-KDD and CICIDS2017. These models also demonstrate reduced false-positive rates and enhanced detection of zero-day attacks. Despite their promise, challenges remain, including regulatory constraints, computational limitations in edge devices, and difficulties in model interpretability. Systemic organizational factors—such as leadership commitment, IT infrastructure readiness, and cybersecurity culture—further affect successful implementation. This study highlights the potential of AI-based NIDS as a strategic approach to cybersecurity enhancement and proposes solutions including Explainable AI, hybrid model designs, and federated learning. The findings support further research into cross-domain applications, model transparency, and real-time scalability to unlock the full potential of intelligent intrusion detection systems.
Early Prediction of At Risk Students Using Minimal Data: A Machine Learning Framework for Higher Education Hamsiah; Adiyati, Nita; Subekti, Rino
Digitus : Journal of Computer Science Applications Vol. 3 No. 2 (2025): April 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i2.953

Abstract

Early identification of academically at risk students is essential for timely intervention and improved retention in higher education. This study investigates the effectiveness of using pre admission and early semester LMS data to predict student risk using machine learning models. The objective is to assess whether limited, readily available data from the first four weeks of instruction can reliably support early warning systems. A supervised learning framework was applied using the Open University Learning Analytics Dataset (OULAD), with features derived from student demographics and early LMS activity logs. Models evaluated include Logistic Regression, XGBoost, and CatBoost, with time based validation and SMOTE employed to address class imbalance. Model performance was measured using ROC AUC, F1 Score, and Recall. The CatBoost model achieved the best performance, with an F1 score of 0.770 and ROC AUC of 0.750, significantly outperforming baseline models. Quiz submission behavior, login frequency, and pre admission qualification level emerged as the most predictive features. Results also revealed a steady week by week improvement in model accuracy, confirming the increasing value of LMS engagement data over time. These findings affirm that early stage student data can be used effectively to predict academic risk, enabling institutions to act before major assessments are conducted. The study emphasizes the need for institutional readiness, ethical implementation, and inclusive practices in deploying predictive tools. Future research should expand the feature space and test cross institutional generalizability to refine early warning systems further.
Generalizable and Energy Efficient Deep Reinforcement Learning for Urban Delivery Robot Navigation Samroh; Munthe, Era Sari
Digitus : Journal of Computer Science Applications Vol. 3 No. 2 (2025): April 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i2.954

Abstract

The increasing demand for contactless urban logistics has driven the integration of autonomous delivery robots into real world operations. This study investigates the application of Deep Reinforcement Learning (DRL) to enhance robot navigation in complex urban environments, focusing on three advanced models: MODSRL, SOAR RL, and NavDP. MODSRL employs a multi objective framework to balance safety, efficiency, and success rate. SOAR RL is designed to handle high obstacle densities using anticipatory decision making. NavDP addresses the sim to real gap through domain adaptation and few shot learning. The models were trained and evaluated in simulation environments (CARLA, nuScenes, Argoverse) and validated using real world deployment data. Evaluation metrics included success rate, collision frequency, and energy efficiency. MODSRL achieved a 91.3% success rate with only 4.2% collision, outperforming baseline methods. SOAR RL showed robust performance in obstacle rich scenarios but highlighted a safety efficiency trade off. NavDP improved real world success rates from 50% to 80% with minimal adaptation data, demonstrating the feasibility of sim to real transfer. The results confirm the effectiveness of DRL in advancing autonomous delivery navigation. Integrating domain generalization, hybrid learning, and real time adaptation strategies will be essential to support large scale urban deployment. Future research should prioritize explainability, continual learning, and user centric navigation policies.
Policy in Practice: A Systematic Review of WCAG 2.2 and ADA 2024 Effects on Web and Mobile Accessibility Purwandari, Nuraini; Dewi, Ratna Kusuma; Rinaldo; Sucipto, Purwo Agus
Digitus : Journal of Computer Science Applications Vol. 3 No. 2 (2025): April 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i2.957

Abstract

Digital accessibility remains a global concern, affecting 1.3 billion people with disabilities. This study evaluates the impact of two policy changes WCAG 2.2 and the 2024 ADA Final Rule on digital interface compliance. A systematic review was conducted using PRISMA 2020 guidelines. Data were sourced from academic databases and regulatory documents spanning 2015–2024. Studies were selected based on their relevance to WCAG/ADA compliance. Quality appraisal was carried out using the Mixed Methods Appraisal Tool (MMAT), and findings were synthesized narratively across web and mobile contexts. WCAG 2.2 added success criteria to improve usability for users with cognitive, motor, and visual impairments. ADA 2024 requires U.S. public sector platforms to meet WCAG 2.1 AA, while the European Accessibility Act shows uneven implementation among member states. WebAIM’s 2024 audit revealed that 95.9% of websites still fail basic accessibility checks, and mobile platforms show even lower compliance. Common issues include poor contrast, missing alt text, and inadequate touch targets. Automated tools alone are insufficient without assistive technology validation. Over reliance on ARIA, limited developer training, and inconsistent policy enforcement persist as barriers to effective implementation. Regulatory updates represent progress but must be supplemented by education, standardized testing protocols, and user involved design practices. Sustainable accessibility requires a shift from reactive compliance to proactive inclusivity, supported by policy, pedagogy, and participatory design.

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