DAY 1
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Moderator
Keynote 1
Educational Backgrounds
Research Interests
Achievements
Wayan Agus Arimbawa, Ph.D.
Moderator
Lecturer,
Universitas Mataram
Keynote 1
Online
Educational Backgrounds
Research Interests
Network technology, deep learning, machine learning, IoT and data analysis, and applications of Artificial Intelligence.
Achievements
Prof. Rung Ching Chen
Keynote Speaker
Professor,
Chaoyang University of Technology, Taiwan
QA Session Keynote 1
Questioner : Ryan
Affiliation : Universitas Mataram
Question : Is there a multi-objective framework that provides a better approach to agentic AI and computing?
QA Session Keynote 1
Questioner : Hazriani
Affiliation : Universitas Handayani
Question :
Thank you very much for the insightful presentation
1. What is the relationship between Agentic-AI and Context-Aware Ubiquitous Computing tech?
2. How about the computation issues? How do the issues handled in you ADAS project?
QA Session Keynote 1
Questioner : Wayan Agus Arimbawa, Ph.D.
Affiliation : Universitas Mataram
Question :
how agentic AI will become in the future? not only on the car side or at home but for all consumer side? as now the AI equipment is being cheaper for users
Moderator
Keynote 2
Educational Backgrounds
Research Interests
English Language Teaching, Tourism and Hospitality Education, Tourism Communication, Sustainable Tourism, Tourism Interpretation, Tourism Training and Competency Development, and Digital Tourism Marketing.
Achievements
She is a Master Trainer for International and ASEAN Standard In-Company Trainers, a GSTC-certified professional in Sustainable Tourism, a Certified ASEAN National Trainer, a Certified Hospitality Educator, and a licensed professional tour guide. She has also participated in international professional development programs in Singapore, Taiwan, the UK, Australia, and Indonesia.
Lecturer,
Poltekpar Lombok
Endang Sri Wahyuni, S.Pd., M.Pd., CHE.
Moderator
Keynote 2
Offline
Prof. Marc Hasselwander
Keynote Speaker
Research Associate and Lecturer,
Technische Universitat Berlin, Germany
Dr Marc Hasselwander is a Research Associate and Lecturer at Technische Universität Berlin, and a Visiting Researcher at the University of Oxford (Sep–Nov 2025). He studies consumer behavior and the acceptance of new technologies in the transport sector, with a particular focus on the Global South. His publications feature empirical work from Latin America, Africa, and Asia.
Position: Research Associate and Lecturer
Organization: Technical University of Berlin
QA Session Keynote 2
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QA Session Keynote 2
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QA Session Keynote 2
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DAY 2
Moderator
Keynote 3
Educational backgrounds
Professional Experience
Rizky B. Adam, S.STP., M.H., MPubAdmin (Mgmt)
Moderator
Head of the Department of Communication, Informatics, and Statistics, Lombok Barat
Keynote 3
Online
Dr. Simon Hodson
Executive Director,
CODATA, France
Keynote Speaker
Research Interests
Data Policy, Research Data Management, FAIR Data, Open Science, Research Data Infrastructure, and Data Interoperability, with particular expertise in developing policies, frameworks, and international initiatives to advance FAIR and open research data.
Expertise: FAIR Data, Open Science, Research Data Policy, and Data Infrastructure and Interoperability.
Key Achievements
QA Session Keynote 3
Questioner : Budi Nugroho
Affiliation : BRIN
Question : For research institution like us which having so many kind of data with very huge size amount of data, is there any suggestion from you to handling the massive dataset with possibly the most practical methodology or technique without overwhelmed by the legacy standard available?
QA Session Keynote 3
Questioner : Rizky B. Adam
Affiliation : Department of Communication, Informatics, and Statistics, Lombok Barat
Question : is there any standardization of data committee in France? that we can imply in Indonesia
QA Session Keynote 3
Questioner : Esa Prakasa
Affiliation : BRIN
Question :
QA Session Keynote 3
Questioner : Wiwin Suwarningsih
Affiliation : BRIN
Question : Dr. Simon Hodson, for the excellent presentation. Given that Agentic AI can autonomously discover, access, and combine data from different domains, could CDIF evolve from a framework for data interoperability into an interoperability layer for AI agents? Specifically, what standards or mechanisms are needed to prevent autonomous agents from misinterpreting heterogeneous data or making decisions based on semantically incompatible datasets?
Moderator
Keynote 4
Educational Backgrounds
Research Interests
Artificial intelligence adoption, information systems and database development with a focus on fish metabolite databases, and spatiotemporal analysis for predicting water body.
Research Experience
Dr. Ira Maryati, S.TP., M.P
Moderator
Young Researcher, Member of Information Retrieval Research Group,
National Research and Innovation Agency (BRIN),
Republic of Indonesia
Keynote 4
Offline
Educational Backgrounds
Research Interests
Natural Language Processing, Speech Recognition / Automatic Speech Recognition (ASR), AI/ML, IT Project Management, and Research Methodology
Achievements
Dr. Dipl.Ing (FH) Asril Jarin, M.Sc
Senior Researcher, Member of NLP Research Group,
National Research and Innovation Agency (BRIN),
Republic of Indonesia
Keynote Speaker
QA Session Keynote 4
Wiwin suwarningsih
Thank you, Dr. Asril, for this excellent and insightful presentation. You highlighted that responsible Agentic AI requires continuous assurance throughout the entire lifecycle, including data provenance, model robustness, monitoring, human oversight, and mechanisms such as action logs and rollback. My question is: how can we operationalize these safeguards when an AI agent is given increasing autonomy, particularly in high-impact domains such as healthcare? Is it possible to define a measurable threshold that determines when an agent can move from ‘recommend’ to ‘write/modify’ or even ‘execute/control’ without human approval?
QA Session Keynote 4
Kuncahyo Setyo Nugroho
Thank you, Dr. Asril, for the insightful presentation. You emphasized that responsible Agentic AI should consider the entire lifecycle, from data and intelligence to agency and ultimately to societal impact. Given your expertise in NLP, particularly in Speech Processing, I would like to connect this with the issue of low-resource languages. In Indonesia, many local languages have very limited digital data and are rarely represented in AI models. If an Agentic AI system performs very well for high-resource languages but has limited understanding of these local languages, it may not only produce inaccurate responses, but could also make inappropriate decisions or actions. So, can we still consider such an AI system responsible and inclusive? And how should low-resource language capabilities be incorporated into the assurance and autonomy of Agentic AI?
(Kuncahyo, BINUS University)
QA Session Keynote 4
Questioner : Prof. Marc Hasselwander
Affiliation : Technische Universitat Berlin, Germany
Question : What is the current status of Indonesia as research agency that has good insight? and how prepared Indonesia to be the pioneer or governing and regulating agentic AI?
QA Session Keynote 4
Questioner : Ryan
Affiliation : Mataram University
Question : what is the highly efficient way to improve skill or capability for the future experts so the skills will not be dull in AI era that now experts also use AI that will make the “comfortable” for the tools?
peraturan paper presentation
onsite/offline