Rumpun ilmu di research center for quantum computing

Rumpun ilmu

1. Current Research Interest for
Materials

2. Model Optimization
Virtual sample generation in regression
Sutojo et al, 2023: A machine learning approach for corrosion small datasets:
https://www.nature.com/articles/s41529-023-00336-7
Akrom et al, 2024: A machine learning approach to predict the efficiency of
corrosion inhibition by natural product-based organic inhibitors:
https://iopscience.iop.org/article/10.1088/1402-4896/ad28a9/meta
Handling imbalance data in classification

3. Transformasi data
Budi et al, 2024:
Implementation of Polynomial Functions to Improve the Accuracy of Machine
Learning Models in Predicting the Corrosion Inhibition Efficiency of Pyridine-
Quinoline Compounds as Corrosion Inhibitors:
https://knepublishing.com/index.php/KnE-Engineering/article/view/15351
Sudibyo et al, 2024:
Separability-based Quadratic Feature Transformation to Improve Classification
Performance:
https://thesai.org/Publications/ViewPaper?
Volume=14&Issue=11&Code=IJACSA&SerialNo=69

4. Best model investigation
Akrom et al, 2023:
A combination of machine learning model and density functional theory method to
predict corrosion inhibition performance of new diazine derivative compounds:
https://www.sciencedirect.com/science/article/abs/pii/S2352492823010930
Akrom et al, 2023:
Data-driven investigation to model the corrosion inhibition efficiency of Pyrimidine-
Pyrazole hybrid corrosion inhibitors
https://www.sciencedirect.com/science/article/abs/pii/S2210271X2300289X
Akrom et al, 2023:
Machine learning investigation to predict corrosion inhibition capacity of new amino
acid compounds as corrosion inhibitors
https://www.sciencedirect.com/science/article/pii/S221171562300365X

5. Feature Engineering
SMILES (Simplified Molecular Input Line Entry
System)

6. Quantum Information
Q-Machine Learning

8. Q-Crypto