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All Journal Jurnal Algoritma JOIN (Jurnal Online Informatika) Building of Informatics, Technology and Science Jurnal Algoritma
Mawaddah, Lubna
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Computer Science & IT

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Optimizing Quantum Neural Networks for Predicting the Effectiveness of Drug Compounds as Corrosion Inhibitors Mawaddah, Lubna; Rosyid, Muhammad Reesa; Santosa, Akbar Priyo; Akrom, Muhamad
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5318

Abstract

Corrosion, caused by electrochemical reactions in corrosive environments, can degrade the quality and lifespan of materials, potentially leading to significant losses in various industrial sectors. One common strategy to reduce corrosion rates is by using corrosion inhibitors. A significant challenge in this field is the time-consuming and costly process of testing new corrosion inhibitors in the laboratory. Consequently, there is a need for more efficient and cost-effective methods to predict the effectiveness of potential corrosion inhibitors using machine learning techniques. This research addresses this problem by applying a quantum machine learning (QML) approach with quantum neural network (QNN) algorithms to evaluate the effectiveness of drug compounds as corrosion inhibitors. The study aims to optimize QNN models by investigating three different quantum circuit configurations to identify the most effective design. The results showed that Model-01, consisting of three layers, demonstrated the best performance with an MSE of 38.81, an RMSE of 6.23, and an MAE of 6.19, along with the shortest training time of 32 seconds, indicating an optimal balance between complexity and generalizability. Overall, this QML approach provides new insights into the predictive ability of QNN models in assessing the effectiveness of drug compounds as corrosion inhibitors, demonstrating the potential of quantum computing to enhance predictive accuracy and efficiency in investigating anti-corrosion materials
Investigasi Model Machine Learning Regresi Pada Senyawa Obat Sebagai Inhibitor Korosi Rosyid, Muhammad Reesa; Mawaddah, Lubna; Akrom, Muhamad
Jurnal Algoritma Vol 21 No 1 (2024): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.21-1.1598

Abstract

Korosi merupakan tantangan signifikan bagi daya tahan material, yang seringkali menyebabkan kerugian ekonomi yang besar. Penelitian ini memanfaatkan teknik Machine Learning (ML) untuk memprediksi efektivitas senyawa obat sebagai inhibitor korosi. Kami menggunakan lima algoritma ML yang menonjol: Regresi Linear, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest, dan XGBoost. Model-model ini dilatih dan dievaluasi menggunakan dataset yang terdiri dari 14 fitur molekuler dengan efisiensi inhibisi korosi (IE%) sebagai variabel target. Hasil pelatihan model awal mengidentifikasi Random Forest dan XGBoost sebagai yang berkinerja terbaik berdasarkan metrik seperti Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), dan R-squared (R²). Penyetelan hiperparameter lebih lanjut menggunakan GridSearchCV menunjukkan bahwa XGBoost, setelah penyetelan, secara signifikan mengungguli model lainnya, mencapai kesalahan terendah dan nilai R² tertinggi, menunjukkan akurasi prediktif yang superior untuk aplikasi ini. Temuan ini menegaskan potensi ML, khususnya XGBoost, dalam meningkatkan pemodelan prediktif inhibitor korosi, sehingga memberikan wawasan berharga bagi bidang ilmu korosi.
Investigasi Model Machine Learning Regresi Pada Senyawa Obat Sebagai Inhibitor Korosi Rosyid, Muhammad Reesa; Mawaddah, Lubna; Akrom, Muhamad
Jurnal Algoritma Vol 21 No 1 (2024): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.21-1.1598

Abstract

Korosi merupakan tantangan signifikan bagi daya tahan material, yang seringkali menyebabkan kerugian ekonomi yang besar. Penelitian ini memanfaatkan teknik Machine Learning (ML) untuk memprediksi efektivitas senyawa obat sebagai inhibitor korosi. Kami menggunakan lima algoritma ML yang menonjol: Regresi Linear, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest, dan XGBoost. Model-model ini dilatih dan dievaluasi menggunakan dataset yang terdiri dari 14 fitur molekuler dengan efisiensi inhibisi korosi (IE%) sebagai variabel target. Hasil pelatihan model awal mengidentifikasi Random Forest dan XGBoost sebagai yang berkinerja terbaik berdasarkan metrik seperti Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), dan R-squared (R²). Penyetelan hiperparameter lebih lanjut menggunakan GridSearchCV menunjukkan bahwa XGBoost, setelah penyetelan, secara signifikan mengungguli model lainnya, mencapai kesalahan terendah dan nilai R² tertinggi, menunjukkan akurasi prediktif yang superior untuk aplikasi ini. Temuan ini menegaskan potensi ML, khususnya XGBoost, dalam meningkatkan pemodelan prediktif inhibitor korosi, sehingga memberikan wawasan berharga bagi bidang ilmu korosi.
Variational Quantum Circuit-Based Quantum Machine Learning Approach for Predicting Corrosion Inhibition Efficiency of Expired Pharmaceuticals Akrom, Muhamad; Rosyid, Muhammad Reesa; Mawaddah, Lubna; Santosa, Akbar Priyo
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1333

Abstract

This study examines the potential of quantum machine learning (QML) to predict the corrosion inhibition capacity of expired pharmaceutical compounds. The investigation employs a QSPR model, using features generated from density functional theory (DFT) calculations as input. At the same time, corrosion inhibition efficiency (CIE) values obtained from experimental data serve as the target output. The VQC model demonstrates varied performance across evaluation metrics, especially with encoding and ansatz design. The model achieves fine scores in evaluation metrics, with root mean square error (RMSE) of 6.15, mean absolute error (MAE) of 5.63, and mean absolute deviation (MAD) of 5.50. The research underscores the significance of larger datasets for enhancing predictive accuracy and points to QML's potential in exploring anti-corrosion materials. Although there are some limitations, this study provides a foundational framework for using QML to predict anti-corrosive properties.
Klasifikasi Otomatis Korosi Menggunakan Convolutional Neural Network dan Transfer Learning dengan Model MobileNetV2 Rizky Pratama, Muhammad Hafiz; Akrom, Muhamad; Santosa, Akbar Priyo; Rosyid, Muhammad Reesa; Mawaddah, Lubna
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2182

Abstract

Corrosion is a major problem that causes significant economic losses in various industries, including transportation, energy, and manufacturing. Early detection of corrosion is essential to reduce its negative impact. This research aims to develop an automatic corrosion classification system based on Convolutional Neural Networks (CNN) with a transfer learning approach. Two models were evaluated, namely a simple CNN architecture and the pre-trained MobileNetV2. The dataset consists of corrosion and non-corrosion images divided into training, validation, and testing data. Data augmentation techniques are applied to increase the variety and number of samples in the training process. The experimental results show that MobileNetV2 achieves a testing accuracy of 95%, which is higher than that of a simple CNN that only reaches 82%. In addition, MobileNetV2 showed better performance in identifying both classes (corrosion and non-corrosion). Despite indications of overfitting due to dataset limitations, the transfer learning approach significantly improved the classification performance. This system has the potential to be applied in real-time industrial applications to reduce economic losses due to corrosion. Further research is recommended to improve the generalization of the model by using a larger dataset and applying more robust regularization techniques.
Co-Authors Akrom, Muhamad Rizky Pratama, Muhammad Hafiz Rosyid, Muhammad Reesa Santosa, Akbar Priyo