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Comparison of Linear and Non-Linear Machine Learning Algortima for Predicting the Effectiveness of Plant Extracts as Corrosion Inhibitors Mulyana, Yudha; Akrom, Muhamad; Trisnapradika, Gustina Alfa
IJNMT (International Journal of New Media Technology) Vol 11 No 1 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i1.3572

This research aims to develop a Machine Learning (ML) model that can predict the corrosion inhibitor potential of plant extracts with high accuracy. Corrosion is a serious problem in industry because it can reduce the service life of materials and cause economic losses. This research focuses on the use of green inhibitors, especially plant extracts, which are considered environmentally friendly and have high anticorrosion efficiency. The dataset used includes molecular and physicochemical features of plant extracts. The ML model development process involves data normalization, selection of linear and non-linear ML algorithms, model training with k-fold crossvalidation, and model performance evaluation using regression metrics such as MSE, RMSE, MAE, and R2. Experiments compare various ML algorithms and show that the AdaBoost Regressor (ABR) model exhibits the best prediction performance with the highest R2 value of 0.993 and a low MSE of 0.002. These results provide new insights into the potential of ML models to predict effective corrosion inhibitors from plant extracts. The ABR model had a low prediction error, indicating high accuracy in predicting corrosion inhibition efficiency. In addition, the analysis of important features shows that two features, Conc and LUMO, have a significant influence on the ABR model. This research makes an important contribution to the development of effective prediction methods in the corrosion control industry. The ABR model can serve as a basis for designing more effective and environmentally friendly corrosion inhibitor materials, as well as a reference for other researchers in developing ML models that accurately predict the corrosion inhibition efficiency of plant extracts.

Comparison of Multilinear Regression and AdaBoost Regression Algorithms in Predicting Corrosion Inhibition Efficiency Using Pyridazine Compounds Mulyana, Yudha; Akrom, Muhamad; Trisnapradika, Gustina Alfa; Setiawan, Nabila Putri
Ultimatics : Jurnal Teknik Informatika Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3809

Abstract-Corrosion is a serious problem in various industries that leads to increased production costs, maintenance, and decreased equipment efficiency. The use of organic compounds as corrosion inhibitors has become an increasingly desirable solution due to their effectiveness and environmental friendliness. This study compares the performance of two machine learning algorithms, Multilinear Regression (MLR) and AdaBoost Regression (ABR), in predicting the corrosion inhibition efficiency (CIE) of pyridazine-derived compounds. The dataset used consists of molecular properties as independent variables and CIE values as targets. To measure the performance of the model, a k-fold cross-validation process was used, where the dataset was divided into equal subsets. Each iteration uses one subset as validation data, while the other subset as training data. Results show that the AdaBoost Regression model achieves higher accuracy (99%) than Multilinear Regression (98%) in predicting CIE. Important feature analysis showed that Total Energy (TE) and Dipole Moment (µ) were the most influential variables in the ABR model, highlighting their important role in inhibitor effectiveness. Model evaluation was performed with R2 and RMSE metrics, where nonlinear models such as ABR were shown to be superior in predicting corrosion inhibition efficiency. These findings support the use of nonlinear methods to improve the effectiveness of protecting industrial equipment from corrosion.

Comparison of Linear and Non-Linear Machine Learning Algortima for Predicting the Effectiveness of Plant Extracts as Corrosion Inhibitors Mulyana, Yudha; Akrom, Muhamad; Trisnapradika, Gustina Alfa
IJNMT (International Journal of New Media Technology) Vol 11 No 1 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i1.3572

This research aims to develop a Machine Learning (ML) model that can predict the corrosion inhibitor potential of plant extracts with high accuracy. Corrosion is a serious problem in industry because it can reduce the service life of materials and cause economic losses. This research focuses on the use of green inhibitors, especially plant extracts, which are considered environmentally friendly and have high anticorrosion efficiency. The dataset used includes molecular and physicochemical features of plant extracts. The ML model development process involves data normalization, selection of linear and non-linear ML algorithms, model training with k-fold crossvalidation, and model performance evaluation using regression metrics such as MSE, RMSE, MAE, and R2. Experiments compare various ML algorithms and show that the AdaBoost Regressor (ABR) model exhibits the best prediction performance with the highest R2 value of 0.993 and a low MSE of 0.002. These results provide new insights into the potential of ML models to predict effective corrosion inhibitors from plant extracts. The ABR model had a low prediction error, indicating high accuracy in predicting corrosion inhibition efficiency. In addition, the analysis of important features shows that two features, Conc and LUMO, have a significant influence on the ABR model. This research makes an important contribution to the development of effective prediction methods in the corrosion control industry. The ABR model can serve as a basis for designing more effective and environmentally friendly corrosion inhibitor materials, as well as a reference for other researchers in developing ML models that accurately predict the corrosion inhibition efficiency of plant extracts.

Comparison of Multilinear Regression and AdaBoost Regression Algorithms in Predicting Corrosion Inhibition Efficiency Using Pyridazine Compounds Mulyana, Yudha; Akrom, Muhamad; Trisnapradika, Gustina Alfa; Setiawan, Nabila Putri
ULTIMATICS Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3809

Abstract-Corrosion is a serious problem in various industries that leads to increased production costs, maintenance, and decreased equipment efficiency. The use of organic compounds as corrosion inhibitors has become an increasingly desirable solution due to their effectiveness and environmental friendliness. This study compares the performance of two machine learning algorithms, Multilinear Regression (MLR) and AdaBoost Regression (ABR), in predicting the corrosion inhibition efficiency (CIE) of pyridazine-derived compounds. The dataset used consists of molecular properties as independent variables and CIE values as targets. To measure the performance of the model, a k-fold cross-validation process was used, where the dataset was divided into equal subsets. Each iteration uses one subset as validation data, while the other subset as training data. Results show that the AdaBoost Regression model achieves higher accuracy (99%) than Multilinear Regression (98%) in predicting CIE. Important feature analysis showed that Total Energy (TE) and Dipole Moment (µ) were the most influential variables in the ABR model, highlighting their important role in inhibitor effectiveness. Model evaluation was performed with R2 and RMSE metrics, where nonlinear models such as ABR were shown to be superior in predicting corrosion inhibition efficiency. These findings support the use of nonlinear methods to improve the effectiveness of protecting industrial equipment from corrosion.

Performance Comparison of IoT Classification Models using Ensemble Stacking and Feature Importance setiawan, nabila putri; Nugraha, Adhitya; Luthfiarta, Ardytha; Mulyana, Yudha
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4673

Internet of Things (IoT) security is becoming a top priority as the number of connected devices increases online. This research utilizes the CIC IoT ATTACK 2023 dataset from the University of Brunswick, which consists of 46 million data on various types of attacks on IoT devices, such as DDoS, DoS, Brute Force, Spoofing, and Mirai attacks. To address the imbalance in the dataset, a random undersampling technique is applied to ensure the machine learning model is not biased towards the majority class. The ensemble learning approach was chosen due to its ability to combine the strengths of multiple algorithms, thus improving accuracy and stability in detecting complex IoT attacks. The algorithms used include gradient boosting, bagging, voting, and stacking. In particular, the stacking model, which combines the bagging classifier and gradient boosting, achieved the highest accuracy of 93%. Although the accuracy of the stacking model decreased to 92.4% after feature selection, the precision, recall, and F1-score remained high at 92.0. In addition, the computation time was also reduced from 2111.69 seconds to 1208.27 seconds. These findings indicate that ensemble learning approaches and feature selection techniques have great potential in improving IoT security, providing more reliable and efficient threat detection solutions.

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