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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING METIK JURNAL Journal of Multiscale Materials Informatics
Wahyu Aji Eko Prabowo
Universitas Dian Nuswantoro
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Quantum Machine Learning Models, Limitations, and Opportunities in the NISQ Era: A Review Muhamad Akrom; Aprilyani Nur Safitri; Novianto Nur Hidayat; Wahyu Aji Eko Prabowo; Setyo Budi; Reza Pamungkas Putra Sukanli
Journal of Multiscale Materials Informatics Vol. 3 No. 1 (2026): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v3i1.15955

Abstract

Quantum machine learning (QML) has emerged as a promising interdisciplinary field that integrates principles of quantum computing with machine learning techniques to address complex computational challenges. By leveraging quantum phenomena such as superposition and entanglement, QML aims to enhance learning efficiency, improve model performance, and enable the exploration of high-dimensional feature spaces that are intractable for classical methods. This paper presents a comprehensive review of recent developments in QML, covering fundamental concepts, algorithmic taxonomies, data encoding techniques, implementation challenges, and real-world applications. Key approaches, including quantum support vector machines (QSVM), variational quantum circuits (VQC), and quantum neural networks (QNN), are systematically analyzed. Furthermore, critical challenges, including noisy intermediate-scale quantum (NISQ) limitations, barren plateaus, data encoding bottlenecks, and the lack of demonstrated quantum advantage, are discussed in detail. The review also highlights emerging applications in material informatics, energy systems, healthcare, and optimization problems. Finally, future research directions are outlined, emphasizing the need for advancements in quantum hardware, scalable algorithms, hybrid frameworks, and standardized benchmarking. This work aims to provide a structured perspective on the current state of QML and to identify opportunities in deploy it effectively in solve real-world problems.
Prediksi Aksebilitas Molekul Tamu pada Metal-Organic Framework dengan SMOTE dan AdaBoost-Machine Learning Moch Anjas Aprihartha; Harun Al Azies; Wahyu Aji Eko Prabowo; Usman Sudibyo; Ika Puspitasari; Indah Putianik; Fatma Ahardika Nurfaizal
METIK Jurnal Vol. 10 No. 1 (2026): METIK Jurnal Issue Published
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/45crx119

Abstract

Metal-Organic Frameworks (MOFs) are a special class of organic-inorganic hybrid materials widely known for their regular and periodic crystal structures. MOFs are composed of metal ions or clusters connected by organic linkers that form a three-dimensional lattice-shaped series. The advantage of MOFs is their ability to capture guest molecules in their pores. Based on these capabilities, MOFs can be utilized in various applications such as gas absorption and separation processes, catalysts, and therapeutic compound delivery systems. Currently, in creating new materials, the MOFs synthesis process still applies a conventional trial-and-error approach that has the potential for high failure rates. The purpose of this study is to develop a machine learning model as an efficient tool design in creating new MOFs materials before the experimental process is carried out. This study implements the SMOTE and AdaBoost methods integrated with machine learning algorithms in classifying MOFs pores based on the pore limiting diameter (PLD) size. The results obtained from the CART-Gentle AdaBoost model provide the best performance with an accuracy of 72.82%; precision 71.32%; recall 73.53%; specificity 72.88%; and f1 score 72.39%. This model is quite suitable for use in identifying MOF structures that are accessible to guest molecules compared to other classification models.
Development of a Machine Learning Model to Predict the Corrosion Inhibition Ability of Benzimidazole Compounds Aprilyani Nur Safitri; Gustina Alfa Trisnapradika; Achmad Wahid Kurniawan; Wahyu AJi Eko Prabowo; Muhamad Akrom
Journal of Multiscale Materials Informatics Vol. 1 No. 1 (2024): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v1i1.10464

Abstract

The purpose of this study is to use quantitative structure-property relationship (QSPR)-based machine learning (ML) to examine the corrosion inhibition capabilities of benzimidazole compounds. The primary difficulty in ML development is creating a model with a high degree of precision so that the predictions are correct and pertinent to the material's actual attributes. We assess the comparison between the extra trees regressor (EXT) as an ensemble model and the decision tree regressor (DT) as a basic model. It was discovered that the EXT model had better predictive performance in predicting the corrosion inhibition performance of benzimidazole compounds based on the coefficient of determination (R2) and root mean square error (RMSE) metrics compared DT model. This method provides a fresh viewpoint on the capacity of ML models to forecast potent corrosion inhibitors.
Evaluating Gate-Based Quantum Machine Learning Models on Quantum Chemistry Datasets Wahyu Aji Eko Prabowo; Muhamad Akrom
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.v2i1.12950

Abstract

This study evaluates gate-based quantum machine learning (QML) models, including the Variational Quantum Classifier (VQC) and Quantum k-Nearest Neighbors (QkNN), on the QM9 quantum chemistry dataset for binary classification of molecular electronic properties. Using IBM Qiskit, both models were tested on simulators and real quantum hardware. Classical models (LightGBM, SVM, MLP) served as benchmarks. Results show classical models outperform quantum ones, with LightGBM achieving the highest AUC-ROC (0.901). However, VQC on simulators achieved a competitive AUC of 0.781, and real hardware still yielded performance above that of chance. Despite hardware constraints, quantum models demonstrated learning capability. The findings support hybrid quantum-classical systems as a promising near-term approach while quantum hardware continues to evolve
Evaluating LSB and MSB Steganography in Retinal Fundus Images Through Image Quality Assessment and VGG19-Based Classification Gilang Faturrahman; Muhammad Naufal; Wahyu Aji Eko Prabowo; Sindhu Rakasiwi
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13198

Abstract

The security of medical image data within electronic medical record systems has become a critical issue due to the increasing threat of health data breaches. Steganography is a promising technique for protecting patient information by concealing secret data within medical images without significantly altering their visual appearance. However, the application of steganography to retinal fundus images, which carry high diagnostic value, has never been comprehensively evaluated in terms of image quality or its impact on artificial intelligence-based diagnostic model performance. This study compares Least Significant Bit (LSB) and Most Significant Bit (MSB) steganography methods applied to 3,200 retinal fundus images from the Retinal Fundus Multi-disease Image Dataset (RFMiD) dataset across four payload levels (0.1-0.4 bpp), evaluated using PSNR, SNR, SSIM, and FSIM for image quality, and VGG19 classification accuracy and AUC for diagnostic impact. Results show LSB achieves substantially superior image quality (PSNR: 59.97-65.93 dB; SNR: 49.34-55.30 dB; SSIM: 0.9981-0.9997; FSIM: 0.9999-1.0000) compared to MSB (PSNR: 12.98-18.99 dB; SNR: 2.35-8.37 dB; SSIM: 0.5979-0.9003; FSIM: 0.5342-0.7500), while VGG19 classification accuracy remains stable for both methods (LSB: 0.8938-0.9000; MSB: 0.8953-0.9031) with a maximum difference of 0.62% from baseline. This study demonstrates that LSB is the more appropriate steganography method for retinal fundus images, delivering superior visual quality while preserving VGG19 diagnostic capability.
Co-Authors Achmad Wahid Kurniawan Aprilyani Nur Safitri Fatma Ahardika Nurfaizal Gilang Faturrahman Gustina Alfa Trisnapradika Harun Al Azies Ika Puspitasari Indah Putianik Moch Anjas Aprihartha Muhamad Akrom Muhammad Naufal Novianto Nur Hidayat Reza Pamungkas Putra Sukanli Setyo Budi Sindhu Rakasiwi Usman Sudibyo