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All Journal Techno.Com: Jurnal Teknologi Informasi Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik JAIS (Journal of Applied Intelligent System) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING CSRID (Computer Science Research and Its Development Journal) Abdimasku : Jurnal Pengabdian Masyarakat JoMMiT : Jurnal Multi Media dan IT Journal of Computing Theories and Applications Jurnal Informatika: Jurnal Pengembangan IT SWAGATI: Journal of Community Service Journal of Multiscale Materials Informatics Journal of Future Artificial Intelligence and Technologies
Noor Ageng Setiyanto, Noor Ageng
Universitas Dian Nuswantoro
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences Other

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Journal : Journal of Multiscale Materials Informatics

 1 
A Machine Learning Model for Evaluation of the Corrosion Inhibition Capacity of Quinoxaline Compounds Setiyanto, Noor Ageng; Azies, Harun Al; Sudibyo, Usman; Pertiwi, Ayu; Budi, Setyo; 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.v1i1.10429

Abstract

Investigating potential corrosion inhibitors via empirical research is a labor- and resource-intensive process. In this work, we evaluated various linear and non-linear algorithms as predictive models for corrosion inhibition efficiency (CIE) values using a machine learning (ML) paradigm based on the quantitative structure-property relationship (QSPR) model. In the quinoxaline compound dataset, our analysis showed that the XGBoost model performed the best predictor of other ensemble-based models. The coefficient of determination (R2), mean absolute percentage error (MAPE), and root mean squared error (RMSE) metrics were used to objectively assess this superiority. To sum up, our study offers a fresh viewpoint on the effectiveness of machine learning algorithms in determining the ability of organic compounds like quinoxaline to suppress corrosion on iron surfaces.
Machine Learning-Assisted Discovery and Optimization of Sodium-Ion Batteries: A Review Trisnapradika, Gustina Alfa; Al Azies, Harun; Akrom, Muhamad; Sudibyo, Usman; Setiyanto, Noor Ageng
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.v3i1.15954

Abstract

Sodium-ion batteries (SIBs) have emerged as a promising alternative to lithium-ion batteries due to the natural abundance, low cost, and wide geographic availability of sodium resources. However, their practical implementation is hindered by challenges such as lower energy density, slower ion diffusion, and limited cycle stability. In recent years, machine learning (ML) has been increasingly applied to accelerate the discovery, design, and optimization of SIB materials and systems. This review provides a comprehensive overview of ML applications in sodium-ion battery research, including electrode material discovery, electrolyte optimization, performance prediction, and degradation analysis. Various ML techniques, such as supervised learning, unsupervised learning, and deep learning, are discussed in relation to their roles in materials informatics. Additionally, challenges such as data scarcity, model interpretability, and transferability are critically analyzed. Finally, future perspectives on integrating ML with high-throughput experiments and quantum computing are highlighted to guide next-generation sodium-ion battery research.
Co-Authors Abdussalam Abdussalam Achmad Wahid Kurniawan Achmad Wahid Kurniawan Aprilyani Nur Safitri Ayu Pertiwi Ayu Pertiwi Bambang Mahardhika Poerbo Waseso Budi, Setyo Cahaya Jatmoko Christy Atika Sari Daurat Sinaga De Rosal Ignatius Moses Setiadi Desi Purwanti Kusumaningrum Dhendra Marutho Dyarsa Singgih Pamungkas Eko Hari Rachmawanto Erlin Dolphina Erna Zuni Astuti Erwin Yudi Hidayat Feri Agustina Hanny Haryanto Harun Al Azies Herawati, Wise Hidayat, Novianto Hidayat, Novianto Nur Khafiizh Hastuti Kurnia, Lisa Lukman Sandjaya Moch Anjas Aprihartha Moch Arief Soeleman Muhamad Akrom Muhammad Naufal Muhammad Naufal, Muhammad Muljono Novianto Hidayat Prabowo, Wahyu Aji Eko Prabowo, Wahyu Eko Aji Pujiono Pujiono Pulung Nurtantio Andono Putra Sumarjono, Cornellius Adryan Rana, Bastion Jader Safitri, Aprilyani Nur Setiawan, Marcell Adi Sinaga, Daurat Sudibyo, Usman Suprayogi Suprayogi Supriadi Rustad T. Sutojo Tirta Indra Wibowo Trisnapradika, Gustina Alfa Usman Sudibyo Wise Herawati