Personal Pages

from world import netizens
print("Hello world!")
print("Greetings from Borneo Islands...!")

Research Agendas

We have experienced the outstanding performance of artificial intelligence surpassing human capability. Yet, they are unable to acknowledge fully that they do not know and learn the novel phenomenon continuously like humans. In my current research agenda, I am exploring a technique to enhance an agent to identify unknown categories and learn them continuously in domain-specific problem. This will make the agent become reliable and able to adapt to the open-world environment in the agent interest only. The applications can be applied to ecology, healthcare, smart cities, robotics, chatbot or intelligent systems. We named this as Domain-Specific Open-World Recognition.

We are interested in the domain-specific open-world recognition problems in Environmental Monitoring and Conservation, Lifelong Chatbot, Web Intelligence, and Healthcare.

Short Biography

Hi, greetings from Borneo Island! I am an assistant professor at Institut Teknologi Kalimantan at the Department of Informatics. Currently, I am pursuing my Ph.D. degree in Artificial Intelligence at the School of Digital Science, Universiti Brunei Darussalam under supervision of Dr. Owais Ahmed Malik and Dr. Ong Wee Hong.

I have been teaching several topics including algorithms and programming languages, data structures, numerical methods, machine learning, and software engineering. I have reviewed several articles in Springer and Elsevier Journals. My research interest includes:

• Open-World Lifelong Machine Learning
• Computer Vision
• Web Intelligence
• Natural Language Processing
• LLM
• Ecological and Environmental Informatics
• Healthcare Informatics

Main Projects

Open-World Lifelong Learning for Biodiversity Monitoring

In a world teeming with diverse ecosystems, continuous and adaptive biodiversity monitoring is vital, as biodiversity plays a crucial role in our sustainability as humans. Open-world lifelong learning offers an innovative approach to monitoring that evolves alongside the environment. Unlike traditional models, which require retraining with new data and struggle to identify unknown classes, open-world lifelong learning systems autonomously learn and adapt over time, recognizing and integrating new species and ecological changes without restarting from scratch. The primary challenge lies in mitigating catastrophic interference to achieve true open-world capability.

Chatbot for Scientific Knowledge Reliability

The project envisions a system where LLMs, guided by robust retrieval mechanisms through RAG, deliver scientifically validated, explainable, and context-aware responses. TrustAI seeks to bridge the gap between complex scientific literature and its practical application across diverse domains with continuous improvement.