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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
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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

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Implementasi Fungsi Polinomial pada Algoritma Gradient Boosting Regressor: Studi Regresi pada Dataset Obat-Obatan Kadaluarsa Sebagai Material Antikorosi Putranto, Nicholaus Verdhy; Akrom, Muhamad; Trinapradika, Gustina Alfa
Jurnal Teknologi dan Manajemen Informatika Vol. 9 No. 2 (2023): Desember 2023
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v9i2.11192

Abstract

Corrosion is an electrochemical process between the metal surface and a corrosive environment that can lead to significant losses in various industries, especially in the oil and gas sector. Experimental studies are conducted to evaluate the performance of corrosion inhibitors and available resources. In this research, a machine learning (ML) approach is employed to assess the effectiveness of expired drug compounds as corrosion inhibitors. The primary challenge in machine learning is obtaining a highly accurate model to ensure that predictions are relevant to the properties of the tested materials. Therefore, the polynomial function is tested in the gradient-boosting regressor (GBR) algorithm to enhance the accuracy of the developed ML model. The results indicate that the implementation of the polynomial function in the GBR algorithm can improve the accuracy of the prediction model based on R2 and RMSE metrics.
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.
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.
Classical-Quantum CNN Hybrid for Image Classification Akrom, Muhamad; Prabowo, Wahyu Aji Eko
Techno.Com Vol. 18 No. 4 (2019): November 2019
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v18i4.12488

Abstract

This study explores Quantum Convolutional Neural Network (QCNN) starting from foundational quantum operations, such as the Rx gate for encoding MNIST image data into quantum states. We implemented quantum convolutional and pooling layers using one_unitary and two_unitary circuits, enabling effective feature extraction and dimensionality reduction while preserving critical information. Expressibility analysis revealed varying capabilities across different one_unitary circuits, with Rx, Ry, and Rz combinations demonstrating promising results akin to Haar random states. The proposed QCNN model exhibited robust performance metrics (accuracy: 95.98%, precision: 94.44%, recall: 96.59%, F1-score: 0.9551, AUC: 0.9604) in classification tasks, supported by efficient convergence during optimization. Future directions include expanding QCNN applications to handle more complex datasets and optimizing architectures to enhance quantum machine learning capabilities, particularly in image processing. This study underscores the potential of QCNNs in advancing quantum computing applications in neural network architectures. Keywords: MNIST, classification, CNN, expressibility
Machine Learning and Density Functional Theory Investigation of Corrosion Inhibition Capability of Ionic Liquid Safitri, Aprilyani Nur; Akrom, Muhamad; Al Azies, Harun; Pertiwi, Ayu; Kurniawan, Achmad Wahid; Herowati, Wise; Rustad, Supriadi
International Journal of Advances in Data and Information Systems Vol. 6 No. 1 (2025): April 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i1.1372

Abstract

This study investigated the corrosion inhibition potential of ionic liquid compounds using a QSPR-based machine learning predictive model combined with DFT calculations. The Gradient Boosting (GB) model was identified as the most effective predictor, demonstrating excellent accuracy with a high R² value of 0.98. Additionally, the model exhibited low RMSE (0.95), MAE (0.84), and MAD (0.94) values. The predicted corrosion inhibition efficiencies (CIE) for three new ionic liquid compounds (IL1, IL2, and IL3) were 88.95, 90.82, and 93.16, respectively, which aligned well with experimental data. By integrating DFT simulations into the data updating process, facilitated by machine learning, the approach proved invaluable for identifying new corrosion inhibitors. This work highlighted the continuous refinement of data related to the corrosion inhibition effects of ionic liquid compounds.
Comparative Study of Classical, Quantum, and Hybrid Stacking Models for Predicting Corrosion Inhibition Efficiency Using QSAR Descriptors Herowati, Wise; Akrom, Muhamad
Journal of Multiscale Materials Informatics Vol. 2 No. 1 (2025): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v2i1.12217

Abstract

This study investigates the performance of classical, quantum, and hybrid classical-quantum stacking models in predicting Corrosion Inhibition Efficiency (IE%) using 14 QSAR descriptors. The hybrid model combines a Gradient Boosting Regressor (GBR) and a Quantum Support Vector Regressor (QSVR) through a meta-learner (Ridge Regression). Results show a significant improvement over traditional models. The hybrid stacking model achieved an R² of 0.834, an MSE of 8.123, an MAE of 2.371, and an RMSE of 2.850, outperforming both individual classical and quantum models. These results confirm the strength of hybrid models in capturing both complex nonlinear and quantum-interaction patterns in QSAR-based molecular prediction.
Towards intelligent post-quantum security: a machine learning approach to FrodoKEM, Falcon, and SIKE Akrom, Muhamad; Setiadi, De Rosal Ignatius Moses
Journal of Multiscale Materials Informatics Vol. 2 No. 1 (2025): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v2i1.12865

Abstract

The rapid advancement of quantum computing poses a substantial threat to classical cryptographic systems, accelerating the global shift toward post-quantum cryptography (PQC). Despite their theoretical robustness, practical deployment of PQC algorithms remains hindered by challenges such as computational overhead, side-channel vulnerabilities, and poor adaptability to dynamic environments. This study integrates machine learning (ML) techniques to enhance three representative PQC algorithms: FrodoKEM, Falcon, and Supersingular Isogeny Key Encapsulation (SIKE). ML is employed for four key purposes: performance optimization through Bayesian and evolutionary parameter tuning; real-time side-channel leakage detection using deep learning models; dynamic algorithm switching based on runtime conditions using reinforcement learning; and cryptographic forensics through anomaly detection on vulnerable implementations. Experimental results demonstrate up to 23.6% reduction in key generation time, over 96% accuracy in side-channel detection, and significant gains in adaptability and leakage resilience. ML models also identified predictive patterns of cryptographic fragility in the now-broken SIKE protocol. These findings confirm that machine learning augments performance and security and enables intelligent and adaptive cryptographic infrastructures for the post-quantum era.
Synergizing Quantum Computing and Machine Learning: A Pathway Toward Quantum-Enhanced Intelligence Trisnapradika, Gustina Alfa; Akrom, Muhamad
Journal of Multiscale Materials Informatics Vol. 2 No. 1 (2025): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v2i1.12947

Abstract

The convergence of quantum computing and artificial intelligence has introduced a new paradigm in computational science known as Quantum Artificial Intelligence (QAI). By leveraging quantum mechanical principles such as superposition, entanglement, and quantum parallelism, QAI aims to overcome the limitations of classical machine learning, particularly in handling high-dimensional data, complex optimization, and scalability issues. This paper presents a comprehensive review of foundational concepts in both classical machine learning and quantum computing, followed by an in-depth discussion of emerging quantum algorithms tailored for AI applications, such as quantum neural networks, quantum support vector machines, and variational quantum classifiers. We explore the practical implications of these approaches across key sectors, including healthcare, finance, cybersecurity, and logistics. Furthermore, we identify critical challenges related to hardware limitations, algorithmic stability, data encoding, and ethical considerations. Finally, we outline research directions necessary to advance the field, highlighting the transformative potential of QAI in shaping the next generation of intelligent technologies
Layerwise Quantum Training: A Progressive Strategy for Mitigating Barren Plateaus in Quantum Neural Networks Al Azies, Harun; Akrom, Muhamad
Journal of Multiscale Materials Informatics Vol. 2 No. 1 (2025): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v2i1.12948

Abstract

Barren plateaus (BP) remain a core challenge in training quantum neural networks (QNN), where gradient vanishing hinders convergence. This paper proposes a layerwise quantum training (LQT) strategy, which trains parameterized quantum circuits (PQC) incrementally by optimizing each layer separately. Our approach avoids deep circuit initialization by gradually constructing the QNN. Experimental results demonstrate that LQT mitigates the onset of barren plateaus and enhances convergence rates compared to conventional and residual-based QNN, rendering it a scalable alternative for Noisy Intermediate-Scale Quantum (NISQ)-era quantum devices.
Co-Authors Achmad Wahid Kurniawan Adiprasetya, Vicenzo Frendyatha Aldiansah, Ilham Pratama Alfa Trisnapradika, Gustina Amri, Sahrul 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 Ikhsanudin, Nur 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 Sanusi Mulyana, Yudha Musafir, Musafir Noor Ageng Setiyanto, Noor Ageng Nur Safitri, Aprilyani Prabowo, Wahyu Aji Eko Pratama, Fikri Luqman 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 Sheilla Rully Anggita 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