Puspa Setia Pratiwi, S.Kom., IM SME, Ph.D.
Faculty of Bioinformatics
- Department of Bioinformatics
Research Interest
- Health Information and Knowledge Management systems
- Ontological technologies in Bioinformatics
- Artificial Intelligence for Life Science
Biography
Dr. Puspa Setia Pratiwi obtained her Bachelor of Computer Science in 2009 from the University of Indonesia, master’s degree from University of Malaya in 2012, and Ph.D. in Computer Science from Queensland University of Technology in 2021. Prior to joining i3L, she had worked in the United Nations Office at Geneva, Bukalapak (as a System Analyst), and at James Cook University, Federation University Australia, YARSI University, and President University as a Lecturer. Dr. Pratiwi had also worked as a Lab Demonstrated at the University of Queensland in 2017 for the Software Process course.
Award
Selected Publication
Pratiwi, P. S. (2021). Knowledge-based approach for personalisation in health e-coaching system: Case study on physical activity promotion for individuals with brain impairments (Doctoral dissertation, Queensland University of Technology). |
Pratiwi, P. S., Xu, Y., Li, Y., Trost, S. G., Clanchy, K. M., & Tjondronegoro, D. W. (2018). User profile ontology to support personalization for e-Coaching systems. In Proceedings of the CIKM 2018 Workshops: co-located with 27th ACM International Conference on Information and Knowledge Management (CIKM 2018). Sun SITE Central Europe (CEUR). |
Pratiwi, P. S., Tjondronegoro, D. W., Xu, Y., Li, Y., Trost, S., & Clanchy, K. (2018). Automated e-Coaching system architecture framework for promoting physical activity. In 2018 International Conference on Intelligent Autonomous Systems (ICoIAS) (pp. 88-91). IEEE. |
Pratiwi, P. S., & Tjondronegoro, D. (2017). Towards personalisation of physical activity e-coach using stage-matched behaviour change and motivational interviewing strategies. In 2017 IEEE Life Sciences Conference (LSC) (pp. 5-8). IEEE. |
Utomo, C. P., Pratiwi, P. S., Kardiana, A., Budi, I., & Suhartanto, H. (2014). Best-parameterized sigmoid elm for benign and malignant breast cancer detection. In International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2014. |
