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All Journal Jurnal Ilmu Komputer dan Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Teori dan Aplikasi Fisika Journal of Multiscale Materials Informatics Advance Sustainable Science, Engineering and Technology (ASSET)
Muhamad Akrom
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Astronomy Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Physics

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Transformative Insights into Corrosion Inhibition: A Machine Learning Journey from Prediction to Web-Based Application Dzaki Asari Surya Putra; Nicholaus Verdhy Putranto; Nibras Bahy Ardyansyah; Gustina Alfa Trisnapradika; Muhamad Akrom
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 1 (2025): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v18i1.1303

Abstract

This study focuses on the exploration and evaluation of machine learning (ML) models to analyze expired pharmaceutical data for their potential use as corrosion inhibitors. Additionally, the entire modeling process is integrated into a user-friendly platform through a Streamlit service-assisted corrosion inhibitor website, facilitating broader accessibility and practical application. The models are trained offline to ensure accurate performance, eliminating the need for users to retrain the models themselves. This approach simplifies the user experience by offering a ready-to-use prediction service directly on the website platform. Among the various ML models implemented, XGB demonstrated the highest performance with an R2-score of 0.99999999. Given that many chemists are not familiar with informatics coding, the researchers developed a Streamlit-based website that includes tools to customize the models. The end product of this work is a corrosion inhibitor experimentation tool that eliminates the need for users to code, making advanced ML techniques accessible to a broader audience within the chemistry community.
Harnessing Quantum SVR on Quantum Turing Machine for Drug Compounds Corrosion Inhibitors Analysis Akbar Priyo Santosa; Muhammad Reesa; Lubna Mawaddah; Muhamad Akrom
Advance Sustainable Science Engineering and Technology Vol. 6 No. 3 (2024): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i3.601

Abstract

Corrosion is an issue that has a significant impact on the oil and gas industry, resulting in significant losses. This is worth investigating because corrosion contributes to a large part of the total annual costs of oil and gas production companies worldwide, and can cause serious problems for the environment that will impact society. The use of inhibitors is one way to prevent corrosion that is quite effective. This study is an experimental study that aims to implement machine learning (ML) on the efficiency of corrosion inhibitors. In this study, the use of the Quantum Support Vector Regression (QSVR) algorithm in the ML approach is used considering the increasingly developing quantum computing technology with the aim of producing better evaluation matrix values ​​than the classical ML algorithm. From the experiments carried out, it was found that the QSVR algorithm with a combination of (TrainableFidelityQuantumKernel, ZZFeatureMap/ PauliFeatureMap, and linear entanglement) obtained better Root Mean Square Error (RMSE) and model training time with a value of 6,19 and 92 compared to other models in this experiment which can be considered in predicting the efficiency of corrosion inhibitors. The success of the research model can provide a new insights of the ability of quantum computer algorithms to increase the evaluation value of the matrix and the ability of ML to predict the efficiency of corrosion inhibitors, especially on a large industrial scale.
INVESTIGATION OF NATURAL EXTRACTS AS GREEN CORROSION INHIBITORS IN STEEL USING DENSITY FUNCTIONAL THEORY Muhamad Akrom
Jurnal Teori dan Aplikasi Fisika Vol. 10 No. 1 (2022): Jurnal Teori dan Aplikasi Fisika
Publisher : Department of Physics, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtaf.v10i1.289

Abstract

One of the materials with low corrosion resistance is steel when it interacts with a corrosive environment. The use of green inhibitors is able to provide good corrosion inhibition performance with high inhibition efficiency on steel. Green inhibitors which in their compound structure contain heteroatom groups (such as O, N, S, P) and aromatic rings are efficiently used as corrosion inhibitors in steel. This paper provides an important comparative overview for the development of green inhibitors of natural extracts in steel. The study of DFT at the atomic level based on molecular orbitals, chemical quantum parameters, and adsorption characteristics showed results that were in accordance with experimental results. The distribution of electron density through the Frontier Molecular Orbitals (FMO) plot illustrates the prediction of active sites through the distribution of the HOMO-LUMO region of inhibitor molecules that interact with the steel surface. To get the correlation between the electronic properties of the inhibitor molecule and the corrosion inhibition potential, calculate the quantum chemical parameters such as ionization potential (I), electron affinity (A), global hardness (?), absolute electronegativity (?), global softness (?) , the transferred electron fraction (?N), global electrophilicity (?) and electron return donation (?Eback-donation) indicate the reactivity of inhibitor molecules which have excellent potential to interact and bind strongly to metal surfaces, thus potentially producing high inhibitory efficiency. The mechanism of corrosion inhibition can be through chemical adsorption and/or physical adsorption by forming a complex compound between the inhibitor molecule and the steel surface to protect it from the corrosive environment. The development of future studies should be able to display the mechanism of interaction and inhibition of inhibitor molecules in more detail and systematically at the atomic level on several metal surfaces such as Fe, Al, Cu, and others.
ANALISIS KOMPARATIF ARSITEKTUR DEEP LEARNING UNTUK PENGENALAN ISYARAT SIBI DINAMIS Uly Atmi Azizah; Muhamad Akrom
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7297

Abstract

Communication is a fundamental right that often becomes a barrier for the deaf community when interacting with the general public. This limitation restricts equal access to education and information. In Indonesia, the Indonesian Sign Language System (SIBI) is the formal standard taught in Special Schools (SLB), yet the implementation of automatic translators for dynamic signs remains suboptimal. Previous studies have often focused on static signs, which are unable to capture temporal gestures. To address this, this study compares three spatio-temporal deep learning architectures for dynamic SIBI sign recognition. The three models compared include the landmark-based Stacked Bi-LSTM using MediaPipe, as well as the visual-based (appearance-based) ResNet50+Bi-LSTM and MobileNetV2+Bi-LSTM. This study utilizes a private dataset consisting of 989 videos covering 11 sign classes. The dataset was partitioned into 60% training, 20% validation, and 20% testing data using a stratified split. The landmark-based Stacked Bi-LSTM achieved the highest accuracy of 98.48%, outperforming ResNet50+Bi-LSTM (97.98%) and MobileNetV2+Bi-LSTM (95.96%). This model also proved to be the most efficient, with 19 times fewer parameters than ResNet50 and 2 times fewer than MobileNetV2, as well as an inference time 53 times faster than ResNet50. The Stacked Bi-LSTM is demonstrated to be the optimal architecture for SIBI recognition, offering the highest accuracy and best efficiency.
Investigation of an amino acid compound as a corrosion inhibitor via ensemble learning Adhe Lingga Dewi; Muhamad Akrom
Journal of Multiscale Materials Informatics Vol. 1 No. 2 (2024): October
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v1i2.11053

Abstract

In this study, we evaluate the performance of various machine learning models, including Random Forest (RF), Bagging (BAG), AdaBoost (ADA), Artificial Neural Network (ANN), and Support Vector Machine (SVM), using metrics such as R², Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The results indicate that AdaBoost (ADA) achieves the highest performance with an R² of 0.999, RMSE of 2.32, and MAE of 2.24, making it the most accurate model with the smallest prediction errors. Bagging (BAG) also performs exceptionally well, with an R2 of 0.996, RMSE of 3.09, and MAE of 2.92. The Artificial Neural Network (ANN) exhibits a high R2 of 0.999, though RMSE and MAE values are not provided. Random Forest (RF) and Support Vector Machine (SVM) show good performance with R² values of 0.982 and 0.970, respectively, but are outperformed by the ensemble methods. The findings underscore the superiority of ensemble techniques, particularly AdaBoost, in achieving high predictive accuracy and minimal errors in this context.
Co-Authors Adhe Lingga Dewi Akbar Priyo Santosa Dzaki Asari Surya Putra Gustina Alfa Trisnapradika Lubna Mawaddah Muhammad Reesa Nibras Bahy Ardyansyah Nicholaus Verdhy Putranto Uly Atmi Azizah