Fakultas

Roadmap

The IR-NLP Laboratory at the Faculty of Computer Science, Universitas Indonesia aims to build a sustainable and responsible Indonesian Language AI ecosystem through foundational research, domain-driven innovation, and real-world collaboration.

The IR-NLP Laboratory at the Faculty of Computer Science, Universitas Indonesia aims to build a sustainable and responsible Indonesian Language AI ecosystem through foundational research, domain-driven innovation, and real-world collaboration.

The roadmap is structured into three progressive layers of research maturity:

  1. Foundational Research & Language Infrastructure
  2. Domain-Specific AI Systems
  3. Integrated Platforms & Societal Impact

1. Foundational Research & Language Infrastructure

This layer establishes the scientific and technical backbone of the laboratory.

Core Research Areas:

  1. Information Retrieval and ranking systems
  2. Computational Linguistics for Indonesian and local languages
  3. Large-scale language modeling
  4. Informal and historical language processing
  5. Low-resource language modeling
  6. Knowledge representation and knowledge graphs

Research Priorities:

  1. Development of high-quality annotated datasets
  2. Benchmark creation for Indonesian NLP tasks
  3. Explainable and trustworthy AI methods
  4. Bias evaluation and responsible AI frameworks
  5. Retrieval-Augmented Generation (RAG) architectures

2. Domain-Specific AI Systems

This layer focuses on applying core IR and NLP expertise to high-impact societal domains.

AI for Public Health & Well-Being

  1. Depression and emotion detection from text
  2. Harmful content monitoring
  3. Population-level mental health trend analysis
  4. Decision-support prototypes for health stakeholders

AI for Resilience & Sustainability

  1. Disaster-related information extraction
  2. Social media monitoring for flood and weather events
  3. Trustworthy prediction frameworks (including uncertainty estimation)
  4. Interpretable disaster knowledge modeling

AI for Justice & Community Advocacy

  1. Legal information retrieval systems
  2. AI-assisted legal question answering
  3. Policy and court decision summarization
  4. Public-access legal knowledge systems

3. Integrated Platforms & Societal Impact

This layer integrates research outputs into cohesive systems with measurable impact.

Platform Development Goals:

  1. Modular AI components that can be combined across domains
  2. Scalable architecture for deployment experimentation
  3. Human-in-the-loop evaluation frameworks
  4. Ethical governance and transparency mechanisms