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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Adimas : Jurnal Pengabdian Kepada Masyarakat International Journal of Advances in Data and Information Systems Abdimasku : Jurnal Pengabdian Masyarakat Journal of Computing Theories and Applications Journal of Multiscale Materials Informatics Journal of Future Artificial Intelligence and Technologies
Herowati, Wise
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

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Journal : Journal of Computing Theories and Applications

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A Quantum Circuit Learning-based Investigation: A Case Study in Iris Benchmark Dataset Binary Classification Akrom, Muhamad; Herowati, Wise; Setiadi, De Rosal Ignatius Moses
Journal of Computing Theories and Applications Vol. 2 No. 3 (2025): JCTA 2(3) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11779

Abstract

This study presents a Quantum Machine Learning (QML) architecture for perfectly classifying the Iris flower dataset. The research addresses improving classification accuracy using quantum models in machine-learning tasks. The objective is to demonstrate the effectiveness of QML approaches, specifically the Variational Quantum Circuit (VQC), Quantum Neural Network (QNN), and Quantum Support Vector Machine (QSVM), in achieving high performance on the Iris dataset. The proposed methods result in perfect classification, with all models attaining accuracy, precision, recall, and an F1-score of 1.00. The main finding is that the QML architecture successfully achieves flawless classification, contributing significantly to the field. These results underscore the potential of QML in solving complex classification problems and highlight its promise for future applications across various domains. The study concludes that QML techniques can offer transformative solutions in machine learning tasks, particularly those leveraging VQC, QNN, and QSVM.
Integrating Quantum, Deep, and Classic Features with Attention-Guided AdaBoost for Medical Risk Prediction Kusuma, Muh Galuh Surya Putra; Setiadi, De Rosal Ignatius Moses; Herowati, Wise; Sutojo, T.; Adi, Prajanto Wahyu; Dutta, Pushan Kumar; Nguyen, Minh T.
Journal of Computing Theories and Applications Vol. 3 No. 2 (2025): in progress
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.14873

Abstract

Chronic diseases such as chronic kidney disease (CKD), diabetes, and heart disease remain major causes of mortality worldwide, highlighting the need for accurate and interpretable diagnostic models. However, conventional machine learning methods often face challenges of limited generalization, feature redundancy, and class imbalance in medical datasets. This study proposes an integrated classification framework that unifies three complementary feature paradigms: classical tabular attributes, deep latent features extracted through an unsupervised Long Short-Term Memory (LSTM) encoder, and quantum-inspired features derived from a five-qubit circuit implemented in PennyLane. These heterogeneous features are fused using a feature-wise attention mechanism combined with an AdaBoost classifier to dynamically weight feature contributions and enhance decision boundaries. Experiments were conducted on three benchmark medical datasets—CKD, early-stage diabetes, and heart disease—under both balanced and imbalanced configurations using stratified five-fold cross-validation. All preprocessing and feature extraction steps were carefully isolated within each fold to ensure fair evaluation. The proposed hybrid model consistently outperformed conventional and ensemble baselines, achieving peak accuracies of 99.75% (CKD), 96.73% (diabetes), and 91.40% (heart disease) with corresponding ROC AUCs up to 1.00. Ablation analyses confirmed that attention-based fusion substantially improved both accuracy and recall, particularly under imbalanced conditions, while SMOTE contributed minimally once feature-level optimization was applied. Overall, the attention-guided AdaBoost framework provides a robust and interpretable approach for clinical risk prediction, demonstrating that integrating diverse quantum, deep, and classical representations can significantly enhance feature discriminability and model reliability in structured medical data.
Co-Authors Achmad Wahid Kurniawan Akrom, Muhamad Ayu Pertiwi Budi, Setyo De Rosal Ignatius Moses Setiadi Dutta, Pushan Kumar Hanny Haryanto Harisa, Ardiawan Bagus Harun Al Azies Hidayat, Novianto Nur Ignatius Moses Setiadi, De Rosal Indra Gamayanto Islam, Hussain Md Mehedul Kusuma, Muh Galuh Surya Putra Minh T. Nguyen, Minh T. Moch Anjas Aprihartha Muhamad Fadil Muljono Muljono Prajanto Wahyu Adi, Prajanto Wahyu Safitri, Aprilyani Nur Sasono Wibowo Sendi Novianto Supriadi Rustad T. Sutojo Trisnapradika, Gustina Alfa Zami, Farrikh Al