Associate Professor in Artificial Intelligence and Signal Processing

Her publication record demonstrates profound expertise in biomedical signal processing, particularly EEG, and the application of machine learning for signal classification. Her research focus also extends to Natural Language Processing (NLP) with applications in automatic essay and short answer grading using modern models such as BERT, BiLSTM, and CNN. Her research contributions include the development of EEG signal feature extraction methods (FFT, Bispectrum, DWT) for emotion and consciousness detection, computer vision-based drowsiness detection systems, and AI-driven user behavior prediction. Her expertise in integrating AI, signal processing, and NLP provides a strong foundation for the research direction of SYNAPSE-AI (SIGNAL, NLP, and Artificial Intelligence Processing for Sensing and Environment). Her collaborative track record with domestic and international colleagues further highlights a productive research synergy oriented toward real-world applications in health, education, and smart environments.

Recent Research Contributions

Automatic Essay Grading for Bahasa Indonesia with Support Vector Machine and Latent Semantic Analysis

Cross-language plagiarism detection system using latent semantic analysis and learning vector quantization

Non-Invasive Classification of Blood Glucose Level Based on Photoplethysmography Using Time–Frequency Analysis

Automatic Essay Scoring for Indonesian Short Answers using Siamese Manhattan Long Short-Term Memory

Stress Detection Application based on Heart Rate Variability (HRV) and K-Nearest Neighbor (KNN)

Eye Based Drowsiness Detection System for Driver

Japanese Short Answer Grading for Japanese Language Learners Using the Contextual Representation of BERT