DrJAG.ID Research Institute is the research and publication home of Dr. Jimmy Agung Gunawan, studying how Retrieval-Augmented Generation (RAG), Generative AI (GAI/GenAI), Machine Learning (ML), Internet of Things (IoT), and (not limited to) Robotic/Automation hold up once they leave the lab and meet regulated, real-world release cycles in the context of Release Management IT.
Each focus area below is tied to work the institute has published or is actively running — not a general capabilities list.
Fine-tuning and retrieval, paired: building on-premise generative AI backed by Retrieval-Augmented Generation, so regulated teams can query their own institutional knowledge without sending it to a third party.
Anomaly and malware detection models built for zero-day resilience in network intrusion detection — machine learning applied where the cost of a false negative is a breach.
Instrumenting the network and server layer AI systems actually run on — from on-premise data centers to cloud and VMware-based infrastructure, with disaster recovery built in from the start.
Automating the release pipeline itself — agile, microservice-based DevSecOps with security integrated throughout, measured by how much lead time it actually saves.
Peer-reviewed and indexed work, linked directly to its DOI or repository record.
A privately hosted, fine-tuned generative AI paired with Retrieval-Augmented Generation, built as an AI-library for software release management.
View the DOI record ↗Empirical evidence on how AI-augmented DevSecOps practices affect lead time inside regulated, agile release management.
Read the article ↗A cognitive approach to network intrusion detection, covering anomaly and malware detection built for zero-day resilience.
View the DOI record ↗The full doctoral dissertation, openly available through Sepuluh Nopember Institute of Technology's repository.
Open the repository record ↗Indexed profiles: ORCID, ResearchGate, Google Scholar, ScopusID, and SciProfiles.
DrJAG.ID Research Institute is led by Dr. Jimmy Agung Gunawan, whose background spans server and network operations, disaster recovery, and security-integrated software delivery, before turning that operational grounding into formal research on AI-augmented DevSecOps.
That path runs through Linux, Solaris, FreeBSD, and Windows environments; disaster recovery across Microsoft, Linux, cloud, and VMware infrastructure; and hands-on work with ISPs and networking equipment providers — the kind of experience that shapes research questions around what actually breaks when AI meets a production release pipeline.
Current work sits at the intersection of retrieval-augmented and generative AI, machine learning for security, IoT-connected infrastructure, and the automation of DevSecOps itself — always tested against the constraints of regulated, agile software teams rather than idealized lab conditions.
The institute publishes through peer-reviewed venues and open repositories, and welcomes conversations on collaboration, consulting, and applied research partnerships.
Whether it's applied AI, a DevSecOps case study, or an IoT deployment that needs a research lens, the institute is open to hearing about it.
Contact the institute