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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. 3 (2025): July 2025" : 5 Documents clear
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.
Decentralized Identity in FinTech: Blockchain Based Solutions for Fraud Prevention and Regulatory Compliance Yuni T, Veronika; Soderi, Ahmad
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.v3i3.955

Abstract

The FinTech sector is facing escalating threats from identity theft and digital fraud, with global losses exceeding US$42 billion annually. This study explores how blockchain based identity systems particularly Verifiable Credentials (VC), Decentralized Identifiers (DID), and selective disclosure protocols can enhance digital security, reduce onboarding time, and ensure compliance with evolving global standards. A qualitative and comparative methodology was applied, analyzing data from regulatory bodies (FTC, FATF, NIST), industry case studies, and technical frameworks (OpenID4VC, SD JWT, W3C). Results reveal that blockchain identity solutions reduce fraud risk by preventing synthetic identity use, while significantly improving authentication success rates through biometric and passkey based logins. Reusable KYC models integrated with VC/DID frameworks cut onboarding durations from weeks to days, demonstrating substantial operational efficiency. Furthermore, alignment with GDPR, eIDAS 2.0, and AML/CFT standards confirms the regulatory readiness of these systems. The findings suggest that decentralized identity offers a viable, scalable alternative to traditional identity verification, enabling secure, privacy preserving, and user controlled authentication. Despite challenges such as integration complexity and regulatory fragmentation, the strategic advantages in security and compliance position blockchain identity systems as essential tools for the future of FinTech.
Latency Aware Edge Architectures for Industrial IoT: Design Patterns and Deterministic Networking Integration Harriz, Muhammad Alfathan
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.v3i3.958

Abstract

This study explores the design patterns and latency budgets required for real time performance in edge based Industrial Internet of Things (IIoT) systems. As industrial applications increasingly demand ultra low latency for control loops and automation tasks, cloud computing architectures fall short in meeting strict timing requirements. The research investigates architectural configurations such as on premises edge computing, hybrid edge↔cloud frameworks, and 5G Multi access Edge Computing (MEC), all integrated with deterministic networking technologies like Time Sensitive Networking (TSN). The methodology includes modeling latency partitions across communication, computation, and execution layers, evaluating IIoT protocols such as OPC UA PubSub and MQTT Sparkplug B, and measuring metrics like end to end latency, jitter, and deadline miss percentages under realistic workloads. Results confirm that edge architectures, when combined with TSN and real-time operating environments, can achieve latency budgets as low as approximately 1 millisecond (ms) for servo loops and between 6–12 ms for machine vision tasks. These values highlight the feasibility of meeting industrial automation requirements. The conclusion underscores the importance of matching communication technologies wired TSN versus 5G URLLC according to environmental constraints and specific application requirements. It also emphasizes the role of hybrid architectures and standardized protocols in enabling scalable, interoperable, and deterministic IIoT systems. This work contributes a validated framework for deploying real time industrial systems capable of meeting the performance thresholds of Industry 4.0.
Real Time Traffic Engineering with In Band Telemetry in Software Defined Data Centers Nugroho, Aryo; Juwari; Marthalia, Lia
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.v3i3.974

Abstract

As data centers scale to accommodate dynamic workloads, real-time and fine-grained traffic engineering (TE) becomes critical. Software Defined Networking (SDN) offers centralized control over data flows, yet its effectiveness is constrained by traditional telemetry mechanisms that lack responsiveness. In-Band Network Telemetry (INT) addresses this gap by embedding real-time path metrics directly into packets, enabling adaptive traffic control based on live network conditions. This study implements and evaluates INT in a programmable Clos fabric using P4 enabled switches. It compares three TE strategies: static ECMP, switch assisted CONGA, and INT informed INT HULA. The simulation incorporates synthetic and trace based data center workloads, including elephant flows and incast scenarios. Performance is assessed using flow completion time (FCT), queue depth, link utilization, and failure recovery speed. INT metadata sizes (32–96 bytes) are also analyzed to quantify overhead vs. performance trade offs. Results indicate that INT HULA consistently outperforms ECMP and CONGA. It reduces FCT by up to 50%, decreases queue occupancy by a factor of three, increases link utilization by more than 25%, and shortens reroute times from 85 ms to 20 ms. These gains are achieved with manageable telemetry overhead and without requiring hardware changes. INT’s real time visibility also improves decision making in centralized SDN controllers and supports hybrid TE architectures. In conclusion, INT fundamentally enhances SDN based TE by enabling closed loop, real time optimization. Its integration with programmable data planes and potential for AI based control loops positions it as a cornerstone of next generation data center networks.
Balancing Performance, Cost, and Sustainability in Software Engineering Munthe, Era Sari; Marthalia, Lia
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.v3i3.1075

Abstract

The environmental impact of Information and Communication Technology (ICT) has become a global concern, especially with the increasing energy consumption of data centers, artificial intelligence, and software systems. This narrative review explores how green computing and sustainable software engineering practices can address these environmental challenges. Using a systematic search across Scopus, IEEE Xplore, Web of Science, and Google Scholar, the review identifies best practices in integrating sustainability across the software lifecycle. Key findings reveal that energy-efficient coding, optimized database systems, and green AI strategies can significantly reduce energy use and carbon emissions. Cloud and serverless architectures offer additional sustainability potential when paired with proper energy monitoring tools. The review also highlights how educational reforms and organizational governance play essential roles in promoting eco-conscious practices. However, challenges persist. These include limited awareness among practitioners, lack of standardized metrics for software sustainability, and weak cross-disciplinary collaboration. Regional disparities also influence adoption, with Europe leading due to stronger policy frameworks, while Asia and North America show mixed trends. This study concludes that integrating sustainability into software engineering requires both technical innovations and systemic reforms. Future research should focus on empirical validation of sustainability frameworks, development of standard evaluation metrics, and promotion of interdisciplinary approaches. Sustainable ICT practices are not only an environmental necessity but also a strategic imperative for the future of digital innovation.

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