Research Activities
Bridging machine learning and plant electrophysiology to decipher biological signaling and create applied intelligent systems.
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Primary PhD Research
Decoding Plant Bioelectric Potentials & Electrophysiology
Plants generate electrical signals (bioelectric potentials) in response to environmental stimuli like temperature shifts, light changes, mechanical wounding, and moisture stress. My primary PhD research focuses on developing and evaluating acquisition, signal-processing, and machine-learning approaches to model and classify these signals.
This involves:
- Noise and Drift Management: Evaluating preprocessing, filtering, and baseline-drift handling for slow, low-voltage biological signals.
- Multivariate Representation: Modeling spatial-temporal electrical patterns across multiple measurement sites using multisensor fusion.
- Time-Series Classification: Evaluating machine-learning and deep-learning approaches, including Transformer-based models, for noisy biological time-series data.
Core Research Themes
Plant Bioelectric Signals
Focuses on the acquisition, denoising, mathematical modeling, and classification of electrical potential differences in vascular plants. By exploring these bioelectric responses, we seek to build edge-sensing systems that treat plants as natural biosensors for agricultural and environmental monitoring.
Biological Time-Series Machine Learning
Studying machine-learning and deep-learning methodologies that can handle the specific difficulties of biological signals. Unlike synthetic or financial time-series, biological signals are slow, non-stationary, and exhibit severe baseline wander and high variance across individuals.
Multisensor Environmental Monitoring
Integrating environmental context data with plant electrophysiology to study how environmental conditions relate to bioelectric responses.
Applied Artificial Intelligence
Applying computer vision and tabular machine learning to diverse fields including medical diagnostics (such as malaria detection in microscopy smears using GAN-augmented datasets), educational marketing segmentation, landslide susceptibility mapping, and cybersecurity.
Research Experience and Funded Projects
The following 2024 projects are recorded in my comprehensive academic CV as funded research experience. Participation is listed without assuming a principal-investigator role.
Integration of Differentiated Learning Theory and Universal Design for Learning in Interactive Multimedia Learning for Low Vision Students through E-Learning Technology
Indonesian Ministry of Education, Culture, Research and Technology · 2024
Inclusive Education Innovation: Development of an Intelligent E-Learning System Based on Smart Content at Universitas Amikom Purwokerto
Indonesian Ministry of Education, Culture, Research and Technology · 2024
Optimization of Feature Extraction Algorithm and Visual-Based Recommendation in Image-Based Website Theme Recommendation System with Transfer Learning
Indonesian Ministry of Education, Culture, Research and Technology · 2024
Analysis of Factors Determining Student Satisfaction Using Decision Tree, Random Forest, SVM, and Neural Networks: A Comparative Study
Universitas Amikom Purwokerto · 2024
Optimizing Higher Education Performance Through Data Integration Using the Zachman Framework: A Case Study on LAM Infokom Accreditation Criteria
Universitas Amikom Purwokerto · 2024