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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi dan Manajemen Informatika Jurnal Algoritma Eksergi: Chemical Engineering Journal JAIS (Journal of Applied Intelligent System) JOIN (Jurnal Online Informatika) JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of New Media Technology Sebatik Jurnal ULTIMATICS KOMPUTIKA - Jurnal Sistem Komputer METIK JURNAL Building of Informatics, Technology and Science Jurnal Pengabdian Masyarakat Sains Indonesia (Indonesian Journal Of Science Community Services) International Journal of Advances in Data and Information Systems Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pendidikan, Sains, Geologi, dan Geofisika (GeoScienceEd Journal) JoMMiT : Jurnal Multi Media dan IT Jurnal Algoritma Journal of Computing Theories and Applications NERO (Networking Engineering Research Operation) Journal of Multiscale Materials Informatics Jurnal Inovasi Pendidikan dan Pembelajaran
Akrom, Muhamad
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Physics Public Health Social Sciences

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Hybrid Quantum Neural Network for Predicting Corrosion Inhibition Efficiency of Organic Molecules Herowati, Wise; 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.v2i2.15132

Abstract

Corrosion inhibition efficiency (IE%) prediction plays a central role in the computational discovery of high-performance organic inhibitors. Classical machine learning has shown promising results; however, its performance often deteriorates when learning non-linear interactions between quantum chemical descriptors. Meanwhile, quantum machine learning (QML) provides enhanced expressivity through quantum feature mapping but remains limited by NISQ-era hardware. In this study, we propose a Hybrid Quantum Neural Network (HQNN) integrating classical dense layers with variational quantum circuits (VQC) to predict the inhibition efficiency of organic corrosion inhibitors. Using a curated dataset of 660 molecules with DFT descriptors, the HQNN achieves an RMSE of 3.41 and R² of 0.958, outperforming classical regressors and pure VQC. The results demonstrate that hybrid quantum models offer a balanced trade-off between quantum advantage and practical feasibility in materials informatics.
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.
Enhancing the Predictive Accuracy of Corrosion Inhibition Efficiency Using Gradient Boosting with Feature Engineering and Gaussian Mixture Model Amri, Sahrul; Akrom, Muhamad; Trisnapradika, Gustina Alfa
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11560

Abstract

Prediction The development of Quantitative structure property relationship (QSPR) models for predicting corrosion inhibition efficiency (IE) often faces challenges due to small datasets, which heightens the risk of overfitting and results in less reliable performance assessments. This research creates an entirely leakage-free modeling framework by combining per-fold preprocessing, augmentation of training-only data, and rigorous Leave-One-Out Cross-Validation (LOOCV). A set of 20 pyridazine derivatives was evaluated using 12 quantum-chemical descriptors, including HOMO, LUMO, ΔE, dipole moment, electronegativity, hardness, softness, and the electron-transfer fraction. An initial assessment showed that all baseline models lacking augmentation Gradient Boosting, Random Forest, SVR, and XGBoost demonstrated limited predictive power (R² < 0.20), revealing the dataset's inherently low information complexity.To enhance representation in the feature space, a multi-scale Gaussian Mixture Model (GMM) was used to generate chemically valid synthetic samples, with all components trained solely on the training subset from each LOOCV fold. This strategy consistently improved model performance. The two most successful configurations, XGBoost + GMM v2 and Random Forest + GMM v3, reached R² values of 0.4457 and 0.4108, respectively, along with significant decreases in RMSE, MAE, and MAPE. These findings illustrate that GMM-based generative augmentation effectively captures multicluster structures within the descriptor space while expanding the chemical variability domain in a controlled way.While the resulting R² values remain inadequate for high-precision quantitative predictions, the proposed methodology provides a solid basis for early-stage evaluation of corrosion inhibitors in situations with limited data. Future research will aim to integrate advanced DFT-derived descriptors, molecular graph representations, and tests against larger external datasets to enhance model generalizability.
Co-Authors Achmad Wahid Kurniawan Adiprasetya, Vicenzo Frendyatha Aldiansah, Ilham Pratama Alfa Trisnapradika, Gustina Amri, Sahrul Ardyansyah, Nibras Bahy Ariyanto, Noval Ayu Pertiwi Budi, Setyo De Rosal Ignatius Moses Setiadi Desrina Desrina Diah Rahayu Ningtias, Diah Rahayu Dian Arif Rachman Ditha Sabrani, Amalia Gumelar, Rizky Syah habiburrahman, Lalu Hariati, Endang Harun Al Azies Herawati, Wise Herowati, Wise Hidayat, Ari Hidayat, Novianto Hidayat, Novianto Nur Hilmy, Nur Amalina Rahmaputri HOTIMAH, NURUL Huryati, Huryati Husnawati Amini, Titik Ignasius, Darnell Imam Muslim, Rachmat Islahudin Jalaluddin Jalaluddin Jannah, Rauhun Kurniawan, Azril Lingga Dewi, Adhe Mawaddah, Lubna Miyati, Hijiul Moch Anjas Aprihartha Muhamad Fadil Muhammad Naufal, Muhammad Muhammad Sanusi Mulyana, Yudha Musafir, Musafir Noor Ageng Setiyanto, Noor Ageng Nur Safitri, Aprilyani Prabowo, Wahyu Aji Eko Putra, Dzaki Asari Surya Putranto, Nicholaus Verdhy Rahman, Irfan Fauzia Rana, Bastion Jader Rizky Pratama, Muhammad Hafiz Rosyid, Muhammad Reesa Safitri, Aprilyani Nur Sahrul Kadri, Agus Santosa, Akbar Priyo setiawan, nabila putri Sheilla Rully Anggita Sudibyo, Usman Sumarjono, Cornellius Adryan Putra Supriadi Rustad T. Sutojo Trinapradika, Gustina Alfa Trisnapradika, Gustina Alfa Umam, Saripul Wahyu Astuti, Ristina Widia Wati, Via Yani, Rindi Zihab, Zihab Zulfiani, Nila