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All Journal International Journal of Advances in Data and Information Systems Abdimasku : Jurnal Pengabdian Masyarakat JoMMiT : Jurnal Multi Media dan IT Jurnal Teknoif Teknik Informatika Institut Teknologi Padang MANGGALI Journal of Multiscale Materials Informatics
Safitri, Aprilyani Nur
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

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Journal : Journal of Multiscale Materials Informatics

 1 
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.
Quantum Convolutional Neural Networks: Architectures, Applications, and Future Directions: A Review Trisnapradika, Gustina Alfa; Safitri, Aprilyani Nur; Hidayat, Novianto Nur; Akrom, Muhamad
Journal of Multiscale Materials Informatics Vol. 2 No. 2 (2025): Oktober
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v2i2.15154

Abstract

Quantum Convolutional Neural Networks (QCNNs) have emerged as one of the most promising architectures in Quantum Machine Learning (QML), enabling hierarchical quantum feature extraction and offering potential advantages over classical CNNs in expressivity and scalability. This study presents a Systematic Literature Review (SLR) on QCNN development from 2019 to 2025, covering theoretical foundations, model architectures, noise resilience, benchmark performance, and applications in materials informatics, chemistry, image recognition, quantum phase classification, and cybersecurity. The SLR followed PRISMA guidelines, screening 214 publications and selecting 47 primary studies. The review finds that QCNNs consistently outperform classical baselines in small-data and high-dimensional regimes due to quantum feature maps and entanglement-driven locality. Significant limitations include noise sensitivity, limited qubit availability, and a lack of standardized datasets for benchmarking. The novelty of this work lies in providing the first comprehensive synthesis of QCNN research across theory, simulations, and real-hardware deployment, offering a roadmap for research gaps and future directions. The findings confirm that QCNNs are strong candidates for NISQ-era applications, especially in physics-informed learning.
Quantum Machine Learning Models, Limitations, and Opportunities in the NISQ Era: A Review Akrom, Muhamad; Safitri, Aprilyani Nur; Hidayat, Novianto Nur; Prabowo, Wahyu Aji Eko; Budi, Setyo
Journal of Multiscale Materials Informatics Vol. 3 No. 1 (2026): April (In Progress)
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v3i1.15955

Abstract

Quantum machine learning (QML) has emerged as a promising interdisciplinary field that integrates principles of quantum computing with machine learning techniques to address complex computational challenges. By leveraging quantum phenomena such as superposition and entanglement, QML aims to enhance learning efficiency, improve model performance, and enable the exploration of high-dimensional feature spaces that are intractable for classical methods. This paper presents a comprehensive review of recent developments in QML, covering fundamental concepts, algorithmic taxonomies, data encoding techniques, implementation challenges, and real-world applications. Key approaches, including quantum support vector machines (QSVM), variational quantum circuits (VQC), and quantum neural networks (QNN), are systematically analyzed. Furthermore, critical challenges, including noisy intermediate-scale quantum (NISQ) limitations, barren plateaus, data encoding bottlenecks, and the lack of demonstrated quantum advantage, are discussed in detail. The review also highlights emerging applications in material informatics, energy systems, healthcare, and optimization problems. Finally, future research directions are outlined, emphasizing the need for advancements in quantum hardware, scalable algorithms, hybrid frameworks, and standardized benchmarking. This work aims to provide a structured perspective on the current state of QML and to identify opportunities in deploy it effectively in solve real-world problems.
Co-Authors Achmad Wahid Kurniawan Akrom, Muhamad Ayu Pertiwi Budi, Setyo Harun Al Azies Herawati, Wise Herowati, Wise Herowati, Wise Herowati Hidayat, Novianto Hidayat, Novianto Nur Imanuel Harkespan Moch Anjas Aprihartha Muhammad Naufal Noor Ageng Setiyanto, Noor Ageng Prabowo, Wahyu Aji Eko Sudibyo, Usman Supriadi Rustad Trisnapradika, Gustina Alfa