This study analyzes the comparative performance of Quantum Machine Learning in classifying factual and hoax political news using three approaches, namely Quantum Neural Network (QNN), Quantum Support Vector Classifier (QSVC), and Hybrid Quantum Kernel with Classical SVM. News data is represented using TF-IDF and its dimensionality is reduced using Principal Component Analysis, then balanced using SMOTE. Feature transformation is carried out to the quantum domain through angle encoding, then applied to the QML model. Performance evaluation is carried out using accuracy, precision, recall, and F1-Score. The experimental results show that QSVC has the best performance with an accuracy of 0.629 and an F1-Score of 0.735, followed by QNN and Hybrid Quantum Kernel Classical SVM. This study proves that the quantum kernel-based approach is effective in classifying medium-dimensional text, while also demonstrating the potential of Quantum Machine Learning as an alternative method for classifying factual and hoax political news.Keywords: Quantum Machine Learning; Quantum Neural Network; Quantum Support Vector Classifier; Hybrid Quantum Kernel; News Classification AbstrakPenelitian ini menganalisis perbandingan kinerja Quantum Machine Learning dalam klasifikasi berita politik fakta dan hoaks dengan menggunakan tiga pendekatan, yaitu Quantum Neural Network (QNN), Quantum Support Vector Classifier (QSVC), dan Hybrid Quantum Kernel dengan Classical SVM. Data berita direpresentasikan menggunakan TF-IDF dan direduksi dimensinya dengan Principal Component Analysis, kemudian diseimbangkan menggunakan SMOTE. Transformasi fitur dilakukan ke domain kuantum melalui angle encoding, kemudian diterapkan pada model QML. Evaluasi kinerja dilakukan menggunakan accuracy, precision, recall, dan F1-Score. Hasil eksperimen menunjukkan QSVC memiliki performa terbaik dengan accuracy 0,629 dan F1-Score 0,735, diikuti QNN dan Hybrid Quantum Kernel Classical SVM. Penelitian ini membuktikan bahwa pendekatan berbasis quantum kernel efektif dalam klasifikasi teks berdimensi sedang, sekaligus menunjukkan potensi Quantum Machine Learning sebagai alternatif metode klasifikasi berita politik fakta dan hoaks.
This study analyzes the comparative performance of Quantum Machine Learning in classifying factual and hoax political news using three approaches, namely Quantum Neural Network (QNN), Quantum Support Vector Classifier (QSVC), and Hybrid Quantum Kernel with Classical SVM. News data is represented using TF-IDF and its dimensionality is reduced using Principal Component Analysis, then balanced using SMOTE. Feature transformation is carried out to the quantum domain through angle encoding, then applied to the QML model. Performance evaluation is carried out using accuracy, precision, recall, and F1-Score. The experimental results show that QSVC has the best performance with an accuracy of 0.629 and an F1-Score of 0.735, followed by QNN and Hybrid Quantum Kernel Classical SVM. This study proves that the quantum kernel-based approach is effective in classifying medium-dimensional text, while also demonstrating the potential of Quantum Machine Learning as an alternative method for classifying factual and hoax political news.Keywords: Quantum Machine Learning; Quantum Neural Network; Quantum Support Vector Classifier; Hybrid Quantum Kernel; News Classification AbstrakPenelitian ini menganalisis perbandingan kinerja Quantum Machine Learning dalam klasifikasi berita politik fakta dan hoaks dengan menggunakan tiga pendekatan, yaitu Quantum Neural Network (QNN), Quantum Support Vector Classifier (QSVC), dan Hybrid Quantum Kernel dengan Classical SVM. Data berita direpresentasikan menggunakan TF-IDF dan direduksi dimensinya dengan Principal Component Analysis, kemudian diseimbangkan menggunakan SMOTE. Transformasi fitur dilakukan ke domain kuantum melalui angle encoding, kemudian diterapkan pada model QML. Evaluasi kinerja dilakukan menggunakan accuracy, precision, recall, dan F1-Score. Hasil eksperimen menunjukkan QSVC memiliki performa terbaik dengan accuracy 0,629 dan F1-Score 0,735, diikuti QNN dan Hybrid Quantum Kernel Classical SVM. Penelitian ini membuktikan bahwa pendekatan berbasis quantum kernel efektif dalam klasifikasi teks berdimensi sedang, sekaligus menunjukkan potensi Quantum Machine Learning sebagai alternatif metode klasifikasi berita politik fakta dan hoaks.
Classifying product reviews on e-commerce platforms, both real and fake, requires a model that can effectively represent text data patterns. This study aims to compare the performance of several Quantum Machine Learning methods, namely QNN, QSVC, and Hybrid Quantum kernel and Classical SVM, in classifying Amazon product reviews. The study uses a quantitative approach with a computational experimental design. Review data is represented using TF-IDF, standardized, and reduced in dimension with Principal Component Analysis (PCA) before being transformed into quantum feature space. Performance evaluation is carried out using accuracy, precision, recall, F1-Score, and MCC metrics. The experimental results show that QNN achieved the best performance with an accuracy value of 85,63%, an F1-Score of 0.9130, and an MCC of 0.5608, while QSVC and the hybrid approach achieved an accuracy of 83.23% with an MCC of 0.4331. These results indicate that QNN has more balanced classification performance.Keywords: Quantum Neural Network; Fake Review Detection; Amazon Reviews; Natural Language Processing; Quantum Machine Learning. AbstrakKlasifikasi ulasan produk pada platform e-commerce, baik ulasan asli maupun palsu, memerlukan model yang mampu merepresentasikan pola data teks secara efektif. Penelitian ini bertujuan untuk membandingkan kinerja beberapa metode Quantum Machine Learning (QML), yaitu QNN, Quantum Support Vector (QSVC), dan Hybrid Quantum kernel and Classical SVM, dalam mengklasifikasikan ulasan produk Amazon. Penelitian menggunakan pendekatan kuantitatif dengan desain eksperimen komputasional. Data ulasan direpresentasikan menggunakan TF-IDF, distandardisasi, dan direduksi dimensinya dengan Principal Component Analysis (PCA) sebelum ditransformasikan ke ruang fitur kuantum. Evaluasi kinerja dilakukan menggunakan metrik accuracy, precision, recall, F1-Score, dan MCC. Hasil eksperimen menunjukkan bahwa QNN memperoleh kinerja terbaik dengan nilai accuracy sebesar 85,63%, F1-Score 0.9130, dan MCC 0.5043, sedangkan QSVC dan pendekatan hybrid mencapai accuracy 83,23% dengan MCC 0,4331. Hasil ini menunjukkan bahwa QNN memiliki performa klasifikasi yang lebih seimbang.
The rapid advancement of machine learning has driven the exploration of various computational models to address classification problems across datasets with diverse characteristics. This study aimed to compare the performance of Artificial Neural Network (ANN) and Hybrid Quantum Neural Network (Hybrid QNN) under controlled experimental conditions. Three benchmark datasets, namely Red Wine Quality, Banknote Authentication, and Leukemia Gene Expression, were used with a training and test data split of 80:20. The evaluation was conducted using accuracy, precision, recall, F1-score, and computational time. The results showed that on the Gene Expression Leukemia dataset, Hybrid QNN achieved an accuracy of 0.9333, significantly outperforming ANN, which obtained 0.6111. Conversely, ANN demonstrated competitive performance and higher computational efficiency on low- and medium-dimensional datasets. These findings indicate that the advantages of Hybrid QNN are contextual and strongly dependent on data characteristics.Keywords: Artificial Neural Network; Hybrid Quantum Neural Network; classification; machine learningAbstrakPerkembangan pesat pembelajaran mesin telah mendorong eksplorasi berbagai model komputasi untuk menyelesaikan permasalahan klasifikasi pada data dengan karakteristik yang beragam. Penelitian ini bertujuan untuk membandingkan kinerja Artificial Neural Network (ANN) dan Hybrid Quantum Neural Network (Hybrid QNN) dalam kondisi eksperimen yang terkontrol. Tiga dataset acuan digunakan, yaitu Red Wine Quality, Banknote Authentication, dan Gene Expression Leukemia, dengan pembagian data latih dan uji sebesar 80:20. Evaluasi dilakukan menggunakan metrik akurasi, precision, recall, F1-score, serta waktu komputasi. Hasil eksperimen menunjukkan bahwa pada dataset Gene Expression Leukemia, Hybrid QNN mencapai akurasi 0,9333, jauh lebih tinggi dibandingkan ANN sebesar 0,6111. Sebaliknya, ANN menunjukkan performa yang kompetitif dan lebih efisien pada dataset berdimensi rendah hingga menengah. Temuan ini menunjukkan bahwa keunggulan Hybrid QNN bersifat kontekstual dan bergantung pada karakteristik data.
Quantum Machine Learning (QML) is increasingly attracting attention as a potential solution to improve computational performance, especially in handling complex and big data-driven classification tasks. In this study, the Quantum Support Vector Machine (QSVM) algorithm is applied to prostate cancer classification, with the results compared to the classical Support Vector Machine (SVM) model. QSVM shows superiority in accuracy, reaching 0.93, compared to the classical SVM which has an accuracy of 0.91. In addition, QSVM produces precision, recall, and F1-score values of 0.83, 0.95, and 0.88, respectively, higher than the precision of 0.82, recall of 0.93, and F1-score of 0.87 of the classical SVM. These findings indicate that QSVM is more effective in handling high-dimensional data and complex classification, thus demonstrating the great potential of QML in medical applications, especially in cancer classification and biomarker discovery.Keywords: Quantum Machine Learning, Quantum Support Vector Machine, Klasifikasi, Kanker Prostat
Cyberbullying on social media has become a serious issue that affects users' mental well-being, safety, and online engagement. Detecting cyberbullying automatically is challenging due to informal writing styles, short messages, and the presence of subtle abusive patterns that often overlap with normal communication. This paper presents a comparative evaluation of quantum-based learning models for cyberbullying detection using a unified experimental pipeline. The proposed system applies text preprocessing followed by TF-IDF feature extraction and PCA-based dimensionality reduction, and then performs classification using Quantum Support Vector Machine (QSVM), Quantum Neural Network (QNN), Quantum Natural Language Processing (QNLP), and Quantum Singular Value Decomposition (QSVD). In addition, a hybrid QNN+QSVM ensemble is evaluated to examine whether combining complementary decision mechanisms improves prediction consistency. Experimental results show that QSVM achieved the best overall accuracy of 88.15%, with 92.52% precision, 92.23% recall, and an F1-score of 92.38%. QSVD delivered comparable performance with 88.08% accuracy, 93.53% precision, 90.98% recall, and an F1-score of 92.24%, indicating strong class separation with reduced feature complexity. QNN and QNLP produced competitive results, achieving 84.12% and 83.85% accuracy, respectively, with F1-scores close to 90%, demonstrating stable detection capability under quantum embedding. The hybrid QNN+QSVM model achieved 87.37% accuracy and an F1-score of 91.87%, showing balanced precision (92.06%) and recall (91.68%) and confirming that ensemble integration can improve reliability over individual quantum neural approaches. These findings suggest that kernel-driven quantum classifiers and decomposition-based quantum representations can provide strong performance for cyberbullying detection while maintaining consistent predictive behavior across evaluation measures.
Binary classification is one of the important tasks in machine learning, with wide applications in various fields, including agriculture and food processing. This study compares the performance of the classical Support Vector Machine (SVM) and Quantum Support Vector Machine (QSVM) in wheat grain classification, focusing on accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). The wheat grain dataset consists of physical features relevant to distinguish between two types of grains. The analysis results show that QSVM significantly outperforms classical SVM in all measured metrics, with higher accuracy and a better balance between precision and recall. The superiority of QSVM can be attributed to its ability to handle complex feature interactions and accelerate the training process through quantum algorithms. These findings demonstrate the potential of QSVM as a more effective model for binary classification applications. However, factors such as implementation complexity and availability of quantum computing resources need to be considered. This study provides valuable insights for the development of more efficient classification methods in the context of agriculture and other related fields.Keywords: Quantum Machine Learning, Quantum Support Vector Machine, Classification, Seeds
Breast cancer is rated as one of the top leading diagnosed and deadliest cancers affecting women worldwide and causing high mortality rate among cancers. With the data-driven applications, hybrid quantum-classical machine learning models have evolved as significant alternatives for handling complex feature spaces with the growing interest in quantum computing. This paper presents a systematic comparative study of classical and quantum machine learning approaches for breast cancer classification using the Wisconsin Breast Cancer Dataset. In this architecture we have preprocessed classical feature vectors using standard normalized techniques and parallelly quantum states are mapped through data embedding techniques which relies on parameterized rotational gates. In this work we have implemented all quantum models using the Pennylane library, resulting in hybrid quantum–classical optimization on near-term quantum simulators. The comparison results in multiple classical and quantum models. In classical we have Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Convolutional Neural Networks (CNN), which are compared with the quantum-based models such as the Variational Quantum Classifier (VQC), Quantum Support Vector Machine (QSVM) and Quantum Convolutional Neural Network (QCNN). In QSVM, we have performed dimensionality reduction using Principal Component Analysis (PCA) before to quantum embedding, and quantum-generated features are used to train a classical linear SVM. The evaluation metrics results in accuracy, precision, recall, F1-score, ROC curves and confusion matrices. The classical models achieves the accuracy in the range of 94%-96%, whereas competitive performance of quantum models demonstrate depending on the embedding strategy and circuit configuration. In the implemented quantum models, the variational quantum classifier achieved the highest classification accuracy of 96.06%, surpassing other evaluated models. These results demonstrate quantum embedding combined with variational quantum circuits can effectively capture discriminative patterns in medical datasets, indicating the implementation of quantum machine learning models for breast cancer diagnosis with the present quantum simulating platforms.