Article Per Year (5 Year)
p-Index From 2021 - 2026
7.06
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi dan Manajemen Informatika Jurnal Algoritma Eksergi: Chemical Engineering Journal JAIS (Journal of Applied Intelligent System) JOIN (Jurnal Online Informatika) JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of New Media Technology Sebatik Jurnal ULTIMATICS KOMPUTIKA - Jurnal Sistem Komputer METIK JURNAL Building of Informatics, Technology and Science Jurnal Pengabdian Masyarakat Sains Indonesia (Indonesian Journal Of Science Community Services) International Journal of Advances in Data and Information Systems Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pendidikan, Sains, Geologi, dan Geofisika (GeoScienceEd Journal) JoMMiT : Jurnal Multi Media dan IT Jurnal Algoritma Journal of Computing Theories and Applications NERO (Networking Engineering Research Operation) Journal of Multiscale Materials Informatics Jurnal Inovasi Pendidikan dan Pembelajaran
Akrom, Muhamad
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Physics Public Health Social Sciences

Published : 59 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Utilization of Machine Learning for Predicting Corrosion Inhibition by Quinoxaline Compounds Fadil, Muhamad; Akrom, Muhamad; Herowati, Wise
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8894

Abstract

Corrosion is a significant issue in both industrial and academic sectors, with widespread negative impacts on various aspects, including economics and safety. To address this problem, the use of corrosion inhibitors has proven effective. This study explores the application of Machine Learning (ML) methods based on Quantitative Structure-Properties Relationship (QSPR) to develop a predictive model for the efficiency of quinoxaline compounds as corrosion inhibitors. By conducting a comparative analysis among three algorithms: AdaBoost Regressor (ADB), Gradient Boosting Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR), and optimizing parameters through hyperparameter tuning using Grid Search and Random Search, this research demonstrates that the XGBR model yields the most superior prediction results. The XGBR optimized with hyperparameter tuning using Grid Search achieved the highest R² value of 0.970 and showed the lowest RMSE, MSE, MAD, and MAPE values of 0.368, 0.135, 0.119, and 0.273, respectively, indicating high predictive accuracy. These results are expected to contribute to the development of more effective methods for identifying corrosion inhibitor candidates.
Prediction of Corrosion Inhibitor Efficiency Based on Quinoxaline Compounds Using Polynomial Regression Rana, Bastion Jader; Setiyanto, Noor Ageng; Akrom, Muhamad
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9031

Abstract

Corrosion is a natural process that leads to material degradation due to environmental factors. It significantly impacts financial and safety aspects, including structural weakening and economic losses in various industries such as oil, gas, and nuclear. Corrosion inhibitors, especially organic compounds like quinoxaline, are widely used to reduce corrosion by forming protective layers on metal surfaces. Quinoxaline compounds, characterized by their heterocyclic structure with nitrogen atoms, demonstrate promising inhibition efficiency in corrosive environments. In this study, machine learning (ML) approaches are utilized to predict the corrosion inhibition efficiency of quinoxaline compounds. Algorithms such as Gradient Boosting Regressor (GBR), Extreme Gradient Boosting Regressor (XGBR), and Automatic Relevance Determination (ARD) regression are compared. The implementation of polynomial functions significantly improves the prediction accuracy of these models. Among them, GBR achieved the best value with MSE, RMSE, MAE, MAPE, and R2 values of 0.0000001, 0.0003229, 0.0000029, 0.0002294, and 0.999999998, respectively. These findings highlight the potential of polynomial-enhanced ML models in accurately predicting corrosion inhibition efficiency. Moreover, the study demonstrates the viability of GBR as a reliable tool for analyzing and optimizing corrosion inhibitors for industrial applications.
Effect of Virtual Sample Generation in Predicting Corrosion Inhibition Efficiency on Pyridazine Aldiansah, Ilham Pratama; Akrom, Muhamad
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9131

Abstract

The purpose of this research is to study how the application of virtual sample generation using the linear interpolation and gaussian noise augmentation method impacts the improvement of prediction model performance in the case of corrosion inhibition efficiency using pyridazine. Random Forest Regressor, Gradient Boosting Regressor, and Bagging Regressor are the models used. The coefficient of determination (R2) values for each model are -0.06, 0.05, and 0.12 on the initial data; the RMSE values are 34.80, 32.90, and 31.65, respectively. After the use of virtual sample development, the R2 values significantly increased to 0.99, 0.96, and 0.99, while the RMSE values significantly decreased to 1.59, 2.88, and 1.25. The research results show that the linear interpolation method can enrich the dataset without altering the data distribution pattern, this method significantly improves the model's accuracy. This performance improvement demonstrates the ability of virtual sample generation to overcome the limitations of the original data; ultimately, this results in a more accurate and reliable predictive model. In the field of material efficiency prediction especially for material technology applications and corrosion control this research helps develop data augmentation methods for similar cases.
Gaussian Mixture-Based Data Augmentation Improves QSAR Prediction of Corrosion Inhibition Efficiency Ignasius, Darnell; Akrom, Muhamad; Budi, Setyo
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10895

Abstract

Predicting corrosion inhibition efficiency IE (%) is often hindered by small, heterogeneous datasets. This study proposes a Gaussian mixture–based data augmentation pipeline to strengthen QSAR generalization under data scarcity. A curated set of 70 drug-like compounds with 14 physicochemical and quantum descriptors was cleaned, split 90/10 (train/test), and transformed using a Quantile Transformer followed by a Robust Scaler. A Gaussian Mixture model (GMM) with 2–5 components selected by the variational lower bound was fitted to the transformed training features and used to generate up to 2,500 synthetic samples. Eight regressors (Gaussian Process, Decision Tree, Random Forest, Bagging, Gradient Boosting, Extra Trees, SVR, and KNN) were evaluated on the held-out test set using R2 and RMSE. Augmentation improved performance across several families: for example, Gaussian Process R2 improved from −1.54 to 0.54 (RMSE 11.71 to 5.01) and Decision Tree R2 from −0.33 to 0.63 (RMSE 8.48 to 4.44), Bagging and Random Forest showed R2 increases of 0.67 and 0.40, respectively. The optimal synthetic size varied by model.
Co-Authors Achmad Wahid Kurniawan Adiprasetya, Vicenzo Frendyatha Aldiansah, Ilham Pratama Alfa Trisnapradika, Gustina Ardyansyah, Nibras Bahy Ariyanto, Noval Ayu Pertiwi Budi, Setyo De Rosal Ignatius Moses Setiadi Desrina Desrina Diah Rahayu Ningtias, Diah Rahayu Dian Arif Rachman Ditha Sabrani, Amalia Gumelar, Rizky Syah habiburrahman, Lalu Hariati, Endang Harun Al Azies Herawati, Wise Herowati, Wise Hidayat, Ari Hidayat, Novianto Hidayat, Novianto Nur Hilmy, Nur Amalina Rahmaputri HOTIMAH, NURUL Huryati, Huryati Husnawati Amini, Titik Ignasius, Darnell Imam Muslim, Rachmat Islahudin Jalaluddin Jalaluddin Jannah, Rauhun Kurniawan, Azril Lingga Dewi, Adhe Mawaddah, Lubna Miyati, Hijiul Moch Anjas Aprihartha Muhamad Fadil Muhammad Naufal, Muhammad Muhammad Sanusi Mulyana, Yudha Musafir, Musafir Noor Ageng Setiyanto, Noor Ageng Nur Safitri, Aprilyani Prabowo, Wahyu Aji Eko Putra, Dzaki Asari Surya Putranto, Nicholaus Verdhy Rahman, Irfan Fauzia Rana, Bastion Jader Rizky Pratama, Muhammad Hafiz Rosyid, Muhammad Reesa Safitri, Aprilyani Nur Sahrul Kadri, Agus Santosa, Akbar Priyo setiawan, nabila putri Sudibyo, Usman Sumarjono, Cornellius Adryan Putra Supriadi Rustad T. Sutojo Trinapradika, Gustina Alfa Trisnapradika, Gustina Alfa Umam, Saripul Wahyu Astuti, Ristina Widia Wati, Via Yani, Rindi Zihab, Zihab Zulfiani, Nila