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All Journal Jurnal Algoritma JOIN (Jurnal Online Informatika) Building of Informatics, Technology and Science Jurnal Algoritma Journal of Multiscale Materials Informatics
Rosyid, Muhammad Reesa
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Journal : Jurnal Algoritma

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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.
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 Mawaddah, Lubna Rizky Pratama, Muhammad Hafiz Santosa, Akbar Priyo