Professor in Remote Sensing Technology

His publication record demonstrates profound expertise in satellite image processing and analysis (Optical and Synthetic Aperture Radar (SAR)), signal processing, and Artificial Intelligence/Machine Learning applications, specifically Deep Learning architectures like CNN. His research focus is highly applied, covering urban flood potential mapping, post-disaster burn area identification, vegetation index analysis, and land-cover change monitoring. His multidisciplinary leadership, centered on remote sensing and AI, provides a robust foundation for the vision and research direction of the VIPReS research group. His collaborative track record with other members further highlights a well-established synergy within the team.

Recent Research Contributions

Wind Power Prediction by Using Wavelet Decomposition Mode Based NARX-Neural Network

An efficient algorithm for cardiac arrhythmia classification using ensemble of depthwise separable convolutional neural networks

A New ECG Data Processing Approach to Developing an Accurate Driving Fatigue Detection Framework with Heart Rate Variability Analysis and Ensemble Learning

Monitoring the Distribution of Mangrove Area using Synthetic Aperture Radar (SAR) and Optic Remote Sensing Data Fusion based on Deep Learning in Kotabaru Regency, Indonesia

CNN-Random Forest Hybrid Method for Phenology-Based Paddy Rice Mapping Using Sentinel-2 and Landsat-8 Satellite Images

Three-Dimensional Convolutional Neural Network on Multi-Temporal Synthetic Aperture Radar Images for Urban Flood Potential Mapping in Jakarta

Assessing Burned Area Detection in Indonesia Using the Stacking Ensemble Neural Network (SENN): A Comparative Analysis of C- and L-Band Performance

Eye Based Drowsiness Detection System for Driver

White Matter Microstructure Differences Between Congenital and Acquired Hearing Loss Patients Using Diffusion Tensor Imaging (DTI) and Machine Learning