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All Journal EDUMATIC: Jurnal Pendidikan Informatika Jurnal Pengabdian Masyarakat Nusantara Journal of Multiscale Materials Informatics
Novianto Nur Hidayat
Universitas Dian Nuswantoro
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education 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.
Investigation of Corrosion Inhibition Efficiency of Pyridine-Quinoline Compounds through Machine Learning Wise Herowati; Muhamad Akrom; Novianto Nur Hidayat; Totok Sutojo
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.v1i1.10448

Abstract

Corrosion in materials is a significant concern for the industrial and academic fields because corrosion causes enormous losses in various fields such as the economy, environment, society, industry, security, safety, and others. Currently, material damage control using organic compounds has become a popular field of study. Pyridine and quinoline stand out as corrosion inhibitors among a myriad of organic compounds because they are non-toxic, inexpensive, and effective in a variety of corrosive environments. Experimental investigations in developing various candidate potential inhibitor compounds are time and resource-intensive. In this work, we use a quantitative structure-property relationship (QSPR)-based machine learning (ML) approach to investigate support vector machine (SVR), random forest (RF), and k-nearest neighbors (KNN) algorithms as predictive models of inhibition performance. (Inhibition efficiency) corrosion of pyridine-quinoline derivative compounds as corrosion inhibitors on iron. We found that the RF model showed the best predictive ability based on the coefficient of determination (R2) and root mean squared error (RMSE) metrics. Overall, our study provides new insights regarding the ML model in predicting corrosion inhibition on iron surfaces.
Tree Tensor Network Quantum-Classical Hybrid Neural Architecture for Efficient Data Classification Novianto Nur Hidayat; 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.v2i1.12949

Abstract

We introduce the Tree Tensor Network-enhanced Quantum-Classical Neural Network (TTN-QNet), a hybrid architecture that leverages the hierarchical structure of Tree Tensor Networks for efficient parameter representation and Variational Quantum Circuits (VQC) for expressive modeling. Unlike Tensor Ring Networks, TTNs reduce parameter redundancy through a tree-based topology, enabling scalable and interpretable computation. The proposed TTN-QNet is evaluated on the Iris, MNIST, and CIFAR-10 datasets, achieving classification accuracies of 93.2%, 85.24%, and 81.67%, respectively, on binary classification tasks. TTN-QNet demonstrates rapid convergence and robustness against barren plateaus, offering a promising direction for deep quantum learning.
Quantum Convolutional Neural Networks: Architectures, Applications, and Future Directions: A Review Gustina Alfa Trisnapradika; Aprilyani Nur Safitri; Novianto Nur Hidayat; Muhamad Akrom
Journal of Multiscale Materials Informatics Vol. 2 No. 2 (2025): Oktober
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v2i2.15154

Abstract

Quantum Convolutional Neural Networks (QCNNs) have emerged as one of the most promising architectures in Quantum Machine Learning (QML), enabling hierarchical quantum feature extraction and offering potential advantages over classical CNNs in expressivity and scalability. This study presents a Systematic Literature Review (SLR) on QCNN development from 2019 to 2025, covering theoretical foundations, model architectures, noise resilience, benchmark performance, and applications in materials informatics, chemistry, image recognition, quantum phase classification, and cybersecurity. The SLR followed PRISMA guidelines, screening 214 publications and selecting 47 primary studies. The review finds that QCNNs consistently outperform classical baselines in small-data and high-dimensional regimes due to quantum feature maps and entanglement-driven locality. Significant limitations include noise sensitivity, limited qubit availability, and a lack of standardized datasets for benchmarking. The novelty of this work lies in providing the first comprehensive synthesis of QCNN research across theory, simulations, and real-hardware deployment, offering a roadmap for research gaps and future directions. The findings confirm that QCNNs are strong candidates for NISQ-era applications, especially in physics-informed learning.
Optimizing XGBoost Performance through Recursive Feature Elimination for Methanol Conversion Prediction Ibnu Richo Kurniawan; Muhamad Febrian Akrom; Novianto Nur Hidayat; Muhammad Naufal
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33509

Abstract

The strong nonlinear interaction between catalytic properties and operating conditions complicates accurate space time yield modeling in thermocatalytic carbon dioxide hydrogenation, especially when redundant descriptors are included. Although XGBoost is widely used for predictive tasks, the influence of feature redundancy on generalization and interpretability in carbon dioxide to methanol systems remains insufficiently examined. This study investigates the integration of Recursive Feature Elimination with XGBoost using 639 experimental observations derived from copper based catalysts. Reducing the feature set from fifteen to eight variables improves generalization performance, as indicated by lower prediction error and higher explained variance. The retained variables correspond to key catalytic and operational parameters, including reaction temperature, pressure, and copper content, aligning with established kinetic and mechanistic principles. These results show that eliminating redundant descriptors stabilizes cross validated performance and reduces training complexity without sacrificing predictive accuracy. The reduced model concentrates predictive weight on kinetically relevant variables, providing a clearer quantitative representation of the parameters that govern space time yield in carbon dioxide hydrogenation.
Pelatihan Bijak Gunakan AI dalam Praktik Penulisan Prompt untuk Software Development Siswa Aprilyani Nur Safitri; Novianto Nur Hidayat; Muhamad Akrom; Didik Hermanto
Jurnal Pengabdian Masyarakat Nusantara Vol 5 No 2 (2026): Juni 2026
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/dimastara.v5i2.28674

Abstract

Perkembangan teknologi Artificial Intelligence (AI) yang semakin pesat memberikan dampak signifikan dalam berbagai bidang, termasuk dalam dunia pendidikan dan pengembangan perangkat lunak (software development). Namun, pemanfaatan AI secara bijak dan efektif masih menjadi tantangan bagi siswa, khususnya dalam memahami cara berinteraksi dengan sistem AI melalui penulisan prompt yang tepat. Oleh karena itu, kegiatan Pengabdian Kepada Masyarakat (PKM) ini dilaksanakan dengan sasaran siswa SMK Negeri 9 Semarang melalui program “Pelatihan Bijak Gunakan AI: Meningkatkan Pemahaman dan Praktik Penulisan Prompt dalam Software Development”. Kegiatan ini bertujuan untuk meningkatkan literasi AI serta kemampuan siswa dalam menyusun prompt yang efektif untuk mendukung proses pemrograman dan pengembangan perangkat lunak. Metode pelaksanaan kegiatan meliputi beberapa tahapan, yaitu penyampaian materi mengenai konsep dasar AI dan etika penggunaannya, pengenalan teknik penulisan prompt yang baik, diskusi interaktif, praktik langsung penggunaan AI dalam membantu proses software development, serta sesi pendampingan bagi peserta. Melalui kegiatan pelatihan ini diharapkan siswa dapat memahami prinsip penggunaan AI secara bijak, mampu menyusun prompt yang lebih terstruktur dan efektif, serta memanfaatkan AI sebagai alat bantu pembelajaran dan pengembangan keterampilan pemrograman. Hasil kegiatan menunjukkan bahwa pelatihan ini dapat meningkatkan pemahaman siswa terhadap penggunaan AI secara produktif dan bertanggung jawab dalam mendukung proses belajar di bidang teknologi informasi.
Co-Authors Aprilyani Nur Safitri Didik Hermanto Gustina Alfa Trisnapradika Ibnu Richo Kurniawan Muhamad Akrom Muhammad Naufal Reza Pamungkas Putra Sukanli Setyo Budi Totok Sutojo Wahyu Aji Eko Prabowo Wise Herowati