Muammar Sadrawi, B.S., M.S., Ph.D.

Research Interest

Non-invasive signal generator
Human-computer interaction
Brain-heart interplay

Biography

Dr. Muammar Sadrawi, S.T., M.Sc. acquired his bachelor’s degree in the Department of Mechanical Engineering, Universitas Syiah Kuala, Indonesia, in 2009. He received his master’s and Ph.D. degrees in Mechanical Engineering at Yuan Ze University, Taiwan, in 2013 and 2018, respectively. Dr. Sadrawi has expertise in artificial intelligence (AI) and data science in sensing and condition monitoring systems.

Research Activity and Highlighted Project

Year	Source	Title	Partners/Funding Agency
2025/2026	National	Nutrient profiling pangan siap saji di Indonesia untuk pencegahan PTM	DIKTI
2025/2026	Institution	Metabolic Engineering of Komagataella phaffii for Conversion of Alcohol Fermentation to Lactic Acid Fermentation Using CRISPR-Cas9	i3L
2024/2025	Institution	Tracing Cultural Factors Affecting Consumer Behavior in Artificial Intelligence for Market Research and Sentiment Analysis Applications	i3L
2022/2023	Institution	Real-time Early Warning System Prototype using Birth and Death of Infant Data in West Java Province	i3L
2020-2021	International	Non-Invasive Cardiovascular and Cerebral Hemodynamics	New Era AI Robotic Inc., Taipei, Taiwan

Selected Publication

Hasuki, W., Agustriawan, D., Parikesit, A. A., Sadrawi, M., Firmansyah, M., Whisnu, A., Natasya, J., Mathew, R., Florensia, I., Napitupulu, Rizqia, N., Ratnasari, P., Arli, A., Parikesit, M., Sadrawi, M., Firmansyah, A., Whisnu, J., Natasya, R., Mathew, I., & Ratnasari. (2023). Development of Artificial Neural Network Model for Medical Specialty Recommendation. Pertanika Journal of Science and Technology, 31. https://doi.org/10.47836/pjst.31.6.05

Himawan, M., & Sadrawi, M. (2023). Development of Long QT Syndrome Detection Using SciPy. Indonesian Journal of Life Sciences, 1–8. https://doi.org/10.54250/ijls.v5i02.178

Mathunjwa, B., Lin, Y.-T., Lin, C.-H., Abbod, M., Sadrawi, M., & Shieh, J.-S. (2023). Automatic IHR-based sleep stage detection using features of residual neural network. Biomedical Signal Processing and Control, 85, 105070. https://doi.org/10.1016/j.bspc.2023.105070