Software Engineering and Applied AI Research Laboratory (SEAI Lab)
The Software Engineering and Applied AI Research Laboratory (SEAI Lab) is a scientific research group based at Politeknik Enjinering Indorama (PEI), Purwakarta, Indonesia. Operating under the university research directorate (LPPM PEI, Lembaga Penelitian dan Pengabdian Masyarakat), the laboratory investigates algorithmic methods, applied machine learning, deep learning models for clinical health screening, automated software testing architectures, and intelligent decision-support systems.
Predictive modeling, clinical dataset classification (e.g., pediatric stunting risk assessment), decision trees, ensemble learning, and epidemiological clustering.
Automated code generation, AI-assisted vulnerability testing, static analysis, refactoring workflows, and LLM-driven software engineering automation.
Integrated WebGIS spatial data pipelines, public service analytics, intelligent campus middleware, and COBIT 2019 enterprise governance models.
- Machine Learning
- Deep Neural Networks
- LLM Agentic Systems
- Software Architecture and Testing
- Health Informatics
- COBIT 2019 IT Governance
- Geospatial WebGIS
- Distributed Data Systems
For the complete publication trajectory (210+ citations), visit: Musawarman on Google Scholar and National SINTA Profile.
The lab mentors undergraduate research assistants and capstone engineering students working on applied ML, automated software testing, and WebGIS architectures.
As part of our ongoing research roadmap, the SEAI Lab leverages advanced Frontier Foundation Models (including Anthropic Claude) to power several computational pipelines:
- Automated Code Synthesis and Static Verification: Benchmarking LLM reasoning for vulnerability detection and automated unit test suite synthesis in mission-critical software systems.
- Scientific Literature Synthesis: Accelerating multi-disciplinary literature reviews across public health datasets and software engineering methodologies.
- Clinical Decision-Support Models: Developing prompt-engineered reasoning pipelines to augment supervised ML algorithms in healthcare diagnostics (e.g., stunting intervention systems).
- Academic Code Refactoring and Agentic Workflows: Investigating autonomous agent architectures for software maintenance and documentation.
Access to the Claude Team plan for Scientists provides our research lab with high-throughput inference, collaborative Project Workspaces, and the extended context window necessary for analyzing extensive clinical health datasets, complex software codebases, and multi-agent AI experiments.
Dedicated GPU-accelerated computing workstations for deep learning model training, hyperparameter optimization, and data preprocessing pipelines.
Continuous Integration / Continuous Deployment (CI/CD) testbed, Android emulation test suites, and enterprise database staging servers for applied systems.
Laboratory: Software Engineering and Applied AI Research Laboratory (SEAI Lab)
Principal Investigator: Musawarman, S.Kom., M.M.S.I.
Official Institutional Email: musawarman@pei.ac.id
Institution: Politeknik Enjinering Indorama (PEI)
Department: D4 Teknologi Rekayasa Perangkat Lunak (TRPL)
Address: Kembangkuning, Ubrug, Jatiluhur, Purwakarta, Jawa Barat 41152, Indonesia
Research Directorate: LPPM PEI (lppm.pei.ac.id)
Faculty Profile: D4 TRPL Faculty Directory
National SINTA ID: 6683490 (MUSAWARMAN)
Google Scholar: QaUXcBgAAAAJ