cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Novianita Rulandari
Contact Email
journal@idscipub.com
Phone
+6282115151339
Journal Mail Official
journal@idscipub.com
Editorial Address
Gondangdia Lama Building 25, RP. Soeroso Street No.25, Jakarta, Indonesia, 10330
Location
Kota adm. jakarta pusat,
Dki jakarta
INDONESIA
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 45 Documents
 1   2   3   4   5 
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.
Blockchain and IoT Integration for Secure Healthcare Data Management: A Narrative Review Setiawan, Adi Wahyu
Digitus : Journal of Computer Science Applications Vol. 3 No. 1 (2025): January 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i1.855

Abstract

The convergence of blockchain and Internet of Things (IoT) technologies has the potential to revolutionize secure data management in healthcare systems. This narrative review investigates how this integration addresses critical issues such as data privacy, interoperability, energy efficiency, and systemic barriers in healthcare. Literature was gathered from databases including Scopus, Web of Science, and Google Scholar, using keyword combinations such as "Blockchain AND IoT AND Healthcare" and "Cybersecurity AND IoT AND Data Privacy". Studies published between 2018 and 2024 were included based on clear methodological standards and relevance to healthcare applications. The review reveals that blockchain significantly enhances the security, transparency, and accessibility of personal health data while enabling efficient remote monitoring through IoT integration. It also highlights that smart contracts and AI-augmented systems optimize operations, reduce delays, and lower costs. However, challenges such as poor infrastructure, low digital literacy, and fragmented regulations impede widespread adoption. Notably, the impact of these factors varies across developed and developing countries. These findings suggest that policy reforms, increased investment in infrastructure, and public education are vital to advancing technological uptake. The study concludes that blockchain-IoT systems represent a strategic innovation in healthcare but require holistic, cross-sector collaboration to realize their transformative potential.
The Role of Edge Computing in Secure and Scalable IoT Systems: A Global Perspective Arainy, Corizon Sinar
Digitus : Journal of Computer Science Applications Vol. 3 No. 1 (2025): January 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i1.856

Abstract

Edge computing has emerged as a pivotal paradigm for optimizing performance, privacy, and deployment within Internet of Things (IoT) ecosystems. This narrative review aims to synthesize the latest scholarly insights into how edge computing addresses key challenges in latency reduction, data security, and resource orchestration. Drawing on a structured literature search from major academic databases, the review analyzed empirical and theoretical contributions spanning various edge-IoT implementations. The findings indicate that edge computing enhances system responsiveness by relocating data processing to proximity of data sources, leading to improved latency and throughput. In applications such as smart cities and remote healthcare, this shift enables more efficient bandwidth usage and timely decision-making. Moreover, privacy-centric technologies including federated learning, blockchain, and zero-trust architectures have proven effective in mitigating data security risks across distributed environments. Despite these advantages, systemic challenges persist, particularly regarding policy, infrastructure, and organizational readiness. Deployment in developing countries often encounters limitations due to regulatory ambiguity and insufficient digital capacity. Successful strategies observed globally emphasize the importance of hybrid cloud-edge-fog architectures and localized deployment models aligned with regional capabilities. This study underscores the need for collaborative public-private innovation, policy reform, and inclusive digital infrastructure development to fully realize the benefits of edge computing in diverse IoT contexts.
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.
Privacy-Preserving Machine Learning: Technological, Social, and Policy Perspectives Ramadhani, Indri Anugrah; Gunawan, Budi
Digitus : Journal of Computer Science Applications Vol. 3 No. 3 (2025): July 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i3.882

Abstract

As machine learning and data mining applications increasingly permeate sensitive domains, concerns over data privacy have intensified. This narrative review aims to synthesize current knowledge on privacy-preserving techniques in artificial intelligence, exploring the technological, socio-cultural, and economic-policy dimensions that shape their implementation. The review employed literature from databases including Scopus, IEEE Xplore, and PubMed, using keywords such as "privacy-preserving," "machine learning," and "differential privacy" to select peer-reviewed articles based on defined inclusion and exclusion criteria. The results reveal that differential privacy and federated learning are leading frameworks offering robust solutions for secure computation without compromising analytical performance. Deep learning models demonstrated strong accuracy, particularly when applied to complex datasets such as healthcare records. However, effectiveness is often impeded by systemic issues, including fragmented regulations and uneven infrastructural capacity. Moreover, socio-cultural factors like digital mistrust and limited awareness among users—especially older populations—pose additional barriers. Economic constraints and inconsistent international policy enforcement further complicate adoption across sectors. This review concludes that successful implementation of privacy-preserving technologies depends not only on algorithmic innovation but also on supportive regulatory, cultural, and financial ecosystems. It calls for integrated policy frameworks, targeted public education, and international cooperation to address existing barriers and advance the responsible use of AI in privacy-sensitive applications.
Infrastructure Driven DevOps in Regulated Markets: A Case Study of Indonesia’s Financial Sector Sucipto, Purwo Agus
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.v2i1.949

Abstract

In regulated industries such as finance and healthcare, organizations must navigate the competing demands of digital innovation and strict compliance requirements. This study investigates how infrastructure localization enables the adoption of DevOps practices in Indonesia’s compliance heavy sectors. Drawing on qualitative case studies of BCA and Bank Jago, the research examines how local cloud infrastructure, regulatory policies, and platform strategies converge to support agile software delivery. The methodology involves comparative analysis using publicly available institutional documents, cloud provider rollouts, and compliance frameworks. The study evaluates DevOps maturity through organizational strategies, toolchains, and infrastructure readiness while mapping them against regulatory standards such as ISO/IEC 27001 and the Personal Data Protection Act. The findings indicate that local cloud infrastructure helps reduce latency and legal risks, thereby supporting secure CI/CD pipelines. BCA illustrates the benefits of using enterprise-level platform engineering with OpenShift, while Bank Jago showcases the flexibility of cloud-native DevOps through rapid CI/CD deployment. Furthermore, the study discusses the balance between innovation and compliance, stressing the role of platform engineering, multi-cloud strategies, and Compliance as Code in minimizing vendor lock-in and regulatory risks. The conclusion underscores Indonesia’s hybrid DevOps strategy as a blueprint for other emerging markets. Integrating infrastructure, policy, and talent development enables institutions to balance agility with governance, promoting scalable and compliant digital transformation in regulated sectors.
Balancing Cadence and Flow: Evaluating Agile Frameworks for Optimal Software Delivery Outcomes Yuni T, Veronika
Digitus : Journal of Computer Science Applications Vol. 2 No. 2 (2024): April 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v2i2.950

Abstract

This study compares the impacts of Scrum and Kanban on software quality, team sustainability, and project predictability within Agile project management. As Agile adoption expands across industries, organizations face the challenge of selecting methods that fit their operational needs and team dynamics. By drawing on empirical case studies and literature, this research highlights the practical differences between Scrum’s cadence-based framework and Kanban’s flow-based model. A comparative analysis was conducted using data from major implementations (e.g., Adobe, John Deere, BBC Worldwide), supported by Agile maturity studies and academic evaluations. Metrics examined include defect reduction, cycle time, velocity stability, lead time, and team stress levels. Scrum demonstrated strong outcomes in early-stage quality improvement and structured delivery. Kanban, in contrast, offered stronger long-term flow consistency and fewer customer-reported defects. Furthermore, hybrid approaches such as Scrumban emerged as practical alternatives that balance predictability with adaptability. Results indicate that both frameworks yield significant benefits when implemented with high team autonomy and cultural alignment. While Scrum enhances predictability through time boxed sprints, Kanban facilitates flexibility and continuous delivery. The study highlights the critical role of implementation quality and Agile maturity in determining success. In conclusion, method choice should reflect organizational context, with growing support for hybrid adoption. This research provides actionable insights for Agile teams and decision makers seeking to align methodology with project goals, workforce dynamics, and customer expectations.
Sentiment as Signal: Detecting Political Misinformation in Indonesia’s 2024 Election via Lexicon Based NLP Dewi, Ratna Kusuma; Nugroho, Aryo
Digitus : Journal of Computer Science Applications Vol. 2 No. 3 (2024): July 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v2i3.951

Abstract

The 2024 Indonesian presidential election witnessed heightened political discourse on social media, accompanied by an alarming rise in misinformation. This study explores the use of lexicon augmented sentiment analysis as a method to detect hoax content in electoral conversations across Twitter, TikTok, and Meta platforms. By combining sentiment polarity analysis with weak supervision and partial manual validation, we developed a hybrid model tailored to Bahasa Indonesia. Using around 50,000 social media posts combined with a verified hoax index from MAFINDO, we examined how sentiment changed over time within political hashtags. We found that sentiment sharply declined after major events like debates and result announcements. Importantly, posts with very negative tone were 3–9 times more likely to contain misinformation, with 18% directly matching confirmed hoaxes. The hybrid model improved classification accuracy from 64% to 78%, showing its practical potential. The results confirm that sentiment polarity particularly extreme negativity can serve as a leading indicator for misinformation outbreaks. By aligning lexicon based sentiment scores with external verification sources, this framework enables scalable and semi automated detection of political hoaxes in low resource language settings. Ethical considerations in data handling, platform compliance, and demographic inclusivity are emphasized throughout the methodology. This research contributes to computational political analysis by validating a practical, replicable model for electoral misinformation detection. Future work should extend toward multimodal detection, real time dashboards, and participatory collaborations with fact checkers and regulatory bodies.
Redefining Speed and Stability: A Meta Analysis of CI/CD Performance through DORA Metrics Sugianto
Digitus : Journal of Computer Science Applications Vol. 3 No. 1 (2025): January 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i1.952

Abstract

Continuous Integration and Delivery (CI/CD) has transformed modern software development, enabling faster, more reliable delivery cycles. This article investigates the impact of CI/CD on software delivery speed and stability through a meta analytical review of benchmark studies and industry metrics, with a focus on the DORA framework’s Four Key Metrics: Deployment Frequency, Lead Time for Changes, Change Failure Rate (CFR), and Mean Time to Recovery (MTTR). Utilizing data from DORA, CircleCI, GitLab, and other industry reports, the study applies systematic methods to compare elite and non elite performance bands. Results indicate that mature CI/CD implementation significantly enhances deployment frequency and reduces lead times, while simultaneously improving system stability through lower CFR and faster recovery times. Elite performers exemplify how frequent, stable deployments can be achieved through automation, observability, and standardized tooling. Industry-wide evidence indicates that these principles are broadly applicable across various organizational contexts. Discussion highlights existing barriers to CI/CD adoption, including legacy infrastructure, cultural inertia, and toolchain fragmentation. To address these, the article emphasizes the role of GitOps and platform engineering in streamlining CI/CD operations. Emerging trends such as AI integration, Software Bill of Materials (SBOM), and advanced observability are also identified as future enablers of delivery excellence. In conclusion, CI/CD maturity is strongly correlated with elite performance in software delivery. DORA metrics offer a reliable framework for assessment and continuous improvement. Organizations seeking to scale their DevOps effectiveness must align their practices with these benchmarks while leveraging emerging tools and cultural strategies to sustain delivery excellence.

Page 3 of 5 | Total Record : 45

 1   2   3   4   5 

Filter by Year

2023 2025


Reset
Filter By Issues
All Issue Vol. 3 No. 4 (2025): October 2025 Vol. 3 No. 3 (2025): July 2025 Vol. 3 No. 2 (2025): April 2025 Vol. 3 No. 1 (2025): January 2025 Vol. 2 No. 4 (2024): October 2024 Vol. 2 No. 3 (2024): July 2024 Vol. 2 No. 2 (2024): April 2024 Vol. 2 No. 1 (2024): January 2024 Vol. 1 No. 1 (2023): October 2023