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All Journal International Journal of Financial, Accounting, and Management Journal of Economics and Business UBS International Journal of Education, Social Studies, And Management (IJESSM) Unram Journal of Community Service (UJCS) Jurnal Minfo Polgan (JMP) Educational Journal of Islamic Management (EJIM) Jurnal Indonesia Sosial Sains Research Horizon Jurnal Inovasi Global Jurnal Pengabdian Tangerang Selatan Data : Journal of Information Systems and Management Digitus : Journal of Computer Science Applications Novatio : Journal of Management Technology and Innovation
Marthalia, Lia
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Journal : Digitus : Journal of Computer Science Applications

 1 
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.
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.
Personalized Causal Targeting in E-commerce: An Uplift Modeling Approach for Campaign Optimization Marthalia, Lia
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.v2i1.1090

Abstract

Evaluations of e-commerce marketing campaigns frequently depend on summary metrics like conversion and click-through rates, which fail to reveal the true causal effect of promotional activities. This study employs uplift modeling to estimate the individual-level causal impact of marketing interventions, clarifying where such approaches outperform traditional metrics, using both a simulated internal dataset and the Dunnhumby Complete Journey data. The objective is to identify which customer segments are causally influenced by marketing actions and to inform more precise targeting strategies. We implemented logistic regression, T Learner, and Causal Forest models to estimate individual treatment effects. Derived features include behavioral (recency, frequency, engagement), transactional (AOV, loyalty tier), and campaign based variables (channel, timing, offer type). Evaluation metrics include Uplift AUC, Qini Curve, and Precision@10%. Ethical safeguards such as pseudonymization and fairness audits were integrated throughout. Results show that Causal Forest significantly outperforms baseline models, achieving the highest uplift AUC and Precision@10%. Key drivers of uplift include campaign channel, customer recency, and loyalty tier. Segment analyses reveal that marketing effectiveness varies by lifecycle stage, device type, and region. Moreover, integrating uplift insights into real time marketing automation systems enables dynamic optimization of campaigns. In conclusion, uplift modeling offers a more robust framework for understanding and maximizing the causal impact of marketing strategies. It improves resource allocation, enhances personalization, and ensures marketing efforts are both effective and ethically responsible.
Co-Authors Andrianto, Deny Tomy Anisah Arie Bowo, Ferdian Aryo Nugroho Budi Gunawan Daniel Happy Putra Dewi, Ratna Kusuma Diantoro, Karno Era Sari Munthe Firman Dera Fitriyani, Syabrina Hanifah, Anisah Mutiara Zahra Hudiyah, Dewi Khoirul Anwar Kurdi, Amirudin KUSNADY, DIDING Lubena Lubena, Lubena Martinus Tukiran Muharam, Hari Nulhakim, Lukman Prayudo, Gito Prihatini Prihatini, Prihatini Salsabila, Anisa Felisia Sapri Simanjuntak, Vera Clara Siswanto, Fraditya Hafiz Sophiandi, Dede SUNARNO Sutarto, Bambang Sutisna, Ade Jaya Widayat, Tri Agung Yuni T, Veronika