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All Journal Jurnal Simetris Jurnal Transformatika Scientific Journal of Informatics Jurnal Informatika Universitas Pamulang International Journal of Advances in Data and Information Systems Abdimasku : Jurnal Pengabdian Masyarakat JoMMiT : Jurnal Multi Media dan IT MANGGALI Journal of Multiscale Materials Informatics
Achmad Wahid Kurniawan
Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

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Machine Learning and Density Functional Theory Investigation of Corrosion Inhibition Capability of Ionic Liquid Safitri, Aprilyani Nur; Akrom, Muhamad; Al Azies, Harun; Pertiwi, Ayu; Kurniawan, Achmad Wahid; Herowati, Wise; Rustad, Supriadi
International Journal of Advances in Data and Information Systems Vol. 6 No. 1 (2025): April 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i1.1372

Abstract

This study investigated the corrosion inhibition potential of ionic liquid compounds using a QSPR-based machine learning predictive model combined with DFT calculations. The Gradient Boosting (GB) model was identified as the most effective predictor, demonstrating excellent accuracy with a high R² value of 0.98. Additionally, the model exhibited low RMSE (0.95), MAE (0.84), and MAD (0.94) values. The predicted corrosion inhibition efficiencies (CIE) for three new ionic liquid compounds (IL1, IL2, and IL3) were 88.95, 90.82, and 93.16, respectively, which aligned well with experimental data. By integrating DFT simulations into the data updating process, facilitated by machine learning, the approach proved invaluable for identifying new corrosion inhibitors. This work highlighted the continuous refinement of data related to the corrosion inhibition effects of ionic liquid compounds.
Pelatihan Computational Thinking Melalui Soal Tantangan Bebras untuk Siswa SD Herowati, Wise Herowati; Sudibyo, Usman; Budi, Setyo; Kurniawan, Achmad Wahid; Safitri, Aprilyani Nur
Manggali Vol 4 No 1 (2024): Manggali
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat, Universitas Ivet

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31331/manggali.v4i1.2941

Abstract

Proses penyampaian ilmu pengetahuan salah satunya ada di proes pendidikan dasar yang diterapkan pada proses persekolahan. Salah satu yang mendukung untuk mengembangkan pengetahuan adalah menggunakan konsep Computational Thinking (CT) melalui soal-soal Bebras. Pengetahuan mengenai soal bebras dapat membantu guru dan siswa dalam mengembangkan kemampuan dan kreatifitas dalam berpikir kritis dan melakukukan analisis dalam menyelesaikan masalah. Metode pembelajaran berbasis simulasi yang digunakan dalam proses pelatihan yang diharapkan dapat membantu guru dan siswa dalam memvisualisasikan konsep secara lebih baik dan jelas serta diharapkan pula siswa mendapatkan keterampilan yang baru dengan tidak takut jika melakukan kesalahan.
Machine Learning-Assisted Prediction of Oxygen Evolution Reaction (OER) Activity for Catalyst Discovery: A Review Herowati, Wise; Akrom, Muhamad; Sutojo, Totok; Kurniawan, Achmad Wahid
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.v3i1.15917

Abstract

The Oxygen Evolution Reaction (OER) is a fundamental process in electrochemical water splitting, playing a crucial role in sustainable hydrogen production. However, its intrinsically sluggish kinetics, involving complex four-electron transfer steps, remain a major bottleneck for efficient energy conversion. In recent years, Machine Learning (ML) has emerged as a powerful approach to accelerate catalyst discovery by enabling data-driven prediction of OER activity and reducing reliance on costly experimental and density functional theory (DFT) calculations. This review systematically summarizes recent advances in ML-assisted OER research, focusing on key aspects including dataset construction, descriptor engineering, model development, and performance evaluation. Various ML techniques, ranging from traditional algorithms such as Random Forest and Support Vector Machines to advanced deep learning approaches, are critically discussed in the context of catalyst screening and activity prediction. Particular attention is given to the role of physicochemical descriptors, including adsorption energies and electronic structure parameters, in governing model performance and interpretability. Furthermore, this review highlights current challenges, such as data scarcity, lack of standardization, and limited model generalization, while discussing emerging trends including active learning, explainable AI, and integration with high-throughput simulations. By providing a comprehensive overview, this work aims to guide future research toward the development of robust, interpretable, and scalable ML frameworks for accelerating the discovery of efficient OER catalysts.
Co-Authors Astuti, Yani Parti Astuti, Yani Parti Astuti, Yani Parti Ayu Pertiwi Budi Widjajanto Budi, Setyo Harun Al Azies Herawati, Wise Herowati, Wise Herowati, Wise Herowati Hidayat, Novianto Ida Farida Ignatius Moses Setiadi, De Rosal Kurniawan, Setyo Budi Moch Anjas Aprihartha Muljono Muljono Noor Ageng Setiyanto, Noor Ageng Prabowo, Wahyu Aji Eko Safitri, Aprilyani Nur Setiawan, Aries Sudibyo, Usman Sudibyo, Usman Sudibyo, Usman Supriadi Rustad T. Sutojo Trisnapradika, Gustina Alfa Yuniarsi Rahayu