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All Journal Abdimasku : Jurnal Pengabdian Masyarakat Journal of Multiscale Materials Informatics
Totok Sutojo
Universitas Dian Nuswantoro
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

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Pemanfaatan Aplikasi Artificial Intelligence (AI) Generatif untuk Pembelajaran Kreatif Guru PAUD dan SD Sugiyanto Sugiyanto; Yani Parti Astuti; Ifan Rizqa; Totok Sutojo; Heribertus Himawan; Adhitya Nugraha; Dewi Agustini Santoso
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 1 (2026): JANUARI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i1.3232

Abstract

Dalam perkembangan teknologi, setiap orang di dunia ini akan mengikutinya baik anak – anak maupun orang tua. Perkembangan teknologi juga tidak lepas dengan dunia Pendidikan dari berbagai jenjang. Jenjang pada dunia Pendidikan saat ini sudah dimulai sejak anak berusia 2 tahun yang disebut dengan Pendidikan Anak Usia Dini (PAUD). Setelah PAUD ada Kelompok Bermain(KB), kemudian Sekolah Dasar (SD) dan seterusnya yang semua orang sudah mengetahuinya. Dengan adanya Pendidikan di usia dini, maka seorang pendidik harus siap untuk mendampingi secara ekstra dalam pembelajarannya. Pendampingan ini dilakukan karena begitu pesatnya perkembangan teknologi yang sudah mempengaruhi anak – anak balita. Untuk itu seorang pendidik harus dibekali dengan pembelajaran yang dikaitkan dengan perkembangan teknologi. Teknologi yang dipakai yang saat ini dikenal dengan AI dan salah satunya Adalah AI Generatif. AI generatif ini bisa digunakan sebagai media pembelajaran karena menarik dan interaktif. Pada kegiatan ini akan dikenalkan berbagai macam AI Generatif sesuai dengan kebutuhannya. AI Generatif yang dimaksud diantaranya ChatGpt/Gemini/Copilot, Canva AI, DALL-E/Bing Image Creator/Leonardo.ai, Synthesia/Pictory dan MusicGen/Mubert/Soundraw. Dengan diadakannya pelatihan tentang AI Generatif diharapkan guru atau pendidik pada PAUD, KB dan juga SD bisa memberikan arahan dan pengetahuan kepada siswa agar pembelajaran lebih interaktif dan menarik.
Machine Learning-Assisted Prediction of Oxygen Evolution Reaction (OER) Activity for Catalyst Discovery: A Review Wise Herowati; Muhamad Akrom; Totok Sutojo; Achmad Wahid Kurniawan
Journal of Multiscale Materials Informatics Vol. 3 No. 1 (2026): April
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.
Investigation of Corrosion Inhibition Efficiency of Pyridine-Quinoline Compounds through Machine Learning Wise Herowati; Muhamad Akrom; Novianto Nur Hidayat; Totok Sutojo
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.v1i1.10448

Abstract

Corrosion in materials is a significant concern for the industrial and academic fields because corrosion causes enormous losses in various fields such as the economy, environment, society, industry, security, safety, and others. Currently, material damage control using organic compounds has become a popular field of study. Pyridine and quinoline stand out as corrosion inhibitors among a myriad of organic compounds because they are non-toxic, inexpensive, and effective in a variety of corrosive environments. Experimental investigations in developing various candidate potential inhibitor compounds are time and resource-intensive. In this work, we use a quantitative structure-property relationship (QSPR)-based machine learning (ML) approach to investigate support vector machine (SVR), random forest (RF), and k-nearest neighbors (KNN) algorithms as predictive models of inhibition performance. (Inhibition efficiency) corrosion of pyridine-quinoline derivative compounds as corrosion inhibitors on iron. We found that the RF model showed the best predictive ability based on the coefficient of determination (R2) and root mean squared error (RMSE) metrics. Overall, our study provides new insights regarding the ML model in predicting corrosion inhibition on iron surfaces.
Co-Authors Achmad Wahid Kurniawan Adhitya Nugraha Dewi Agustini Santoso Heribertus Himawan Ifan Rizqa Muhamad Akrom Novianto Nur Hidayat Sugiyanto Sugiyanto Wise Herowati Yani Parti Astuti