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All Journal Techno.Com: Jurnal Teknologi Informasi JAIS (Journal of Applied Intelligent System) JURIKOM (Jurnal Riset Komputer) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Inovasi Penelitian dan Pengabdian Masyarakat Journal of Multiscale Materials Informatics
Prabowo, Wahyu Aji Eko
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences Other

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Journal : Journal of Multiscale Materials Informatics

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Development of a Machine Learning Model to Predict the Corrosion Inhibition Ability of Benzimidazole Compounds Safitri, Aprilyani Nur; Trisnapradika, Gustina Alfa; Kurniawan, Achmad Wahid; Prabowo, Wahyu AJi Eko; Akrom, Muhamad
Journal of Multiscale Materials Informatics Vol. 1 No. 1 (2024): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v1i1.10464

Abstract

The purpose of this study is to use quantitative structure-property relationship (QSPR)-based machine learning (ML) to examine the corrosion inhibition capabilities of benzimidazole compounds. The primary difficulty in ML development is creating a model with a high degree of precision so that the predictions are correct and pertinent to the material's actual attributes. We assess the comparison between the extra trees regressor (EXT) as an ensemble model and the decision tree regressor (DT) as a basic model. It was discovered that the EXT model had better predictive performance in predicting the corrosion inhibition performance of benzimidazole compounds based on the coefficient of determination (R2) and root mean square error (RMSE) metrics compared DT model. This method provides a fresh viewpoint on the capacity of ML models to forecast potent corrosion inhibitors.
Evaluating Gate-Based Quantum Machine Learning Models on Quantum Chemistry Datasets Prabowo, Wahyu Aji Eko; Akrom, Muhamad
Journal of Multiscale Materials Informatics Vol. 2 No. 1 (2025): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v2i1.12950

Abstract

This study evaluates gate-based quantum machine learning (QML) models, including the Variational Quantum Classifier (VQC) and Quantum k-Nearest Neighbors (QkNN), on the QM9 quantum chemistry dataset for binary classification of molecular electronic properties. Using IBM Qiskit, both models were tested on simulators and real quantum hardware. Classical models (LightGBM, SVM, MLP) served as benchmarks. Results show classical models outperform quantum ones, with LightGBM achieving the highest AUC-ROC (0.901). However, VQC on simulators achieved a competitive AUC of 0.781, and real hardware still yielded performance above that of chance. Despite hardware constraints, quantum models demonstrated learning capability. The findings support hybrid quantum-classical systems as a promising near-term approach while quantum hardware continues to evolve
Co-Authors Achmad Wahid Kurniawan Adi, Ilham Arif Kuncoro Ajisaputra, Faris Prasetya Akrom, Muhamad Ayu Pertiwi Harun Al Azies Hidayat, Novianto Nur Hidayatulloh, Ryan Moch Anjas Aprihartha Muhammad Naufal, Muhammad Noor Ageng Setiyanto, Noor Ageng Safitri, Aprilyani Nur Sudibyo, Usman Trisnapradika, Gustina Alfa Wibawa, Vincentius Hadianta