KOMPARASI SVM KLASIK DAN KUANTUM DALAM KLASIFIKASI BINER BIJI GANDUM (SEEDS)
Abstract
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, SeedsFull Text:
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J. Preskill, “Quantum computing in the NISQ era and beyond,” Quantum, vol. 2, Aug. 2018, doi: 10.22331/q-2018-08-06-79.
J. Bub, “QUANTUM INFORMATION AND COMPUTATION,” Philosophy of Physics, pp. 555–660, Jan. 2007, doi: 10.1016/B978-044451560-5/50009-9.
N. S, H. Singh, and A. U. N, “An extensive review on quantum computers,” Advances in Engineering Software, vol. 174, p. 103337, Dec. 2022, doi: 10.1016/J.ADVENGSOFT.2022.103337.
C. F. Tsai, Y. F. Hsu, C. Y. Lin, and W. Y. Lin, “Intrusion detection by machine learning: A review,” Expert Syst Appl, vol. 36, no. 10, pp. 11994–12000, Dec. 2009, doi: 10.1016/J.ESWA.2009.05.029.
M. Akrom, S. Rustad, H. K. Dipojono, and R. Maezono, “A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds,” Artificial Intelligence Chemistry, vol. 2, no. 2, p. 100073, Dec. 2024, doi: 10.1016/J.AICHEM.2024.100073.
A. P. Santosa and M. Akrom, “Quantum support vector regression for predicting corrosion inhibition of drugs,” Journal of Multiscale Materials Informatics, vol. 1, no. 2, pp. 30–34, Aug. 2024, doi: 10.62411/jimat.v1i2.11427.
Y. Bai, X. Han, T. Chen, and H. Yu, “Quadratic kernel-free least squares support vector machine for target diseases classification,” J Comb Optim, vol. 30, no. 4, pp. 850–870, Nov. 2015, doi: 10.1007/s10878-015-9848-z.
O. Farooq et al., “An enhanced approach for predicting air pollution using quantum support vector machine,” Sci Rep, vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-69663-2.
I. H. Sarker, “Machine Learning: Algorithms, Real-World Applications and Research Directions,” May 01, 2021, Springer. doi: 10.1007/s42979-021-00592-x.
T. Kumar, D. Kumar, and G. Singh, “Brain Tumour Classification Using Quantum Support Vector Machine Learning Algorithm,” IETE J Res, 2023, doi: 10.1080/03772063.2023.2245350.
D. Peral-García, J. Cruz-Benito, and F. J. García-Peñalvo, “Systematic literature review: Quantum machine learning and its applications,” Comput Sci Rev, vol. 51, p. 100619, Feb. 2024, doi: 10.1016/J.COSREV.2024.100619.
L. Wei et al., “Quantum machine learning in medical image analysis: A survey,” Neurocomputing, vol. 525, pp. 42–53, Mar. 2023, doi: 10.1016/J.NEUCOM.2023.01.049.
U. Ullah and B. Garcia-Zapirain, “Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000. Quantum Machine Learning Revolution in Healthcare: A Systematic Review of Emerging Perspectives and Applications”, doi: 10.1109/ACCESS.2017.DOI.
T. Suzuki and M. Katouda, “Predicting toxicity by quantum machine learning,” J Phys Commun, vol. 4, no. 12, 2020, doi: 10.1088/2399-6528/abd3d8.
R. Zhang, J. Wang, N. Jiang, and Z. Wang, “Quantum support vector machine without iteration,” Inf Sci (N Y), vol. 635, pp. 25–41, Jul. 2023, doi: 10.1016/J.INS.2023.03.106.
V. Havlíček et al., “Supervised learning with quantum-enhanced feature spaces,” Nature, vol. 567, no. 7747, pp. 209–212, Mar. 2019, doi: 10.1038/s41586-019-0980-2.
S. Park, D. K. Park, and J. K. K. Rhee, “Variational quantum approximate support vector machine with inference transfer,” Sci Rep, vol. 13, no. 1, Dec. 2023, doi: 10.1038/s41598-023-29495-y.
D. Willsch, M. Willsch, H. De Raedt, and K. Michielsen, “Support vector machines on the D-Wave quantum annealer,” Comput Phys Commun, vol. 248, p. 107006, Mar. 2020, doi: 10.1016/J.CPC.2019.107006.
X. Vasques, H. Paik, and L. Cif, “Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M-type classification,” Sci Rep, vol. 13, no. 1, Dec. 2023, doi: 10.1038/s41598-023-38558-z.
R. D. M. Simoes, P. Huber, N. Meier, N. Smailov, R. M. Fuchslin, and K. Stockinger, “Experimental Evaluation of Quantum Machine Learning Algorithms,” IEEE Access, vol. 11, pp. 6197–6208, 2023, doi: 10.1109/ACCESS.2023.3236409.
A. Rana, P. Vaidya, and G. Gupta, “A comparative study of quantum support vector machine algorithm for handwritten recognition with support vector machine algorithm,” Mater Today Proc, vol. 56, pp. 2025–2030, Jan. 2022, doi: 10.1016/J.MATPR.2021.11.350.
F. Novianti and N. Ulinnuha, “SELEKSI FITUR ALGORITMA GENETIKA DALAM KLASIFIKASI DATA REKAM MEDIS PCOS MENGGUNAKAN SVM FEATURE SELECTION USING GENETIC ALGORITHM IN PCOS MEDICAL RECORD DATA CLASSIFICATION USING SVM,” 2024.
K. A. Tychola, T. Kalampokas, and G. A. Papakostas, “Quantum Machine Learning—An Overview,” Jun. 01, 2023, MDPI. doi: 10.3390/electronics12112379.
M. Akrom, S. Rustad, and H. K. Dipojono, “Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds,” Materials Today Quantum, vol. 2, p. 100007, Jun. 2024, doi: 10.1016/j.mtquan.2024.100007.
J. Rothfuss et al., “Noise Regularization for Conditional Density Estimation,” Jul. 2019, [Online]. Available: http://arxiv.org/abs/1907.08982
Y. Xiang and Y.-C. Chen, “Statistical Inference Using Mean Shift Denoising,” Oct. 2016, [Online]. Available: http://arxiv.org/abs/1610.03927
N. Iam-On and T. Boongoen, “Diversity-driven generation of link-based cluster ensemble and application to data classification,” Expert Syst Appl, vol. 42, no. 21, pp. 8259–8273, Nov. 2015, doi: 10.1016/J.ESWA.2015.06.051.
M. Wei, T. W. S. Chow, and R. H. M. Chan, “Clustering heterogeneous data with k-means by mutual information-based unsupervised feature transformation,” Entropy, vol. 17, no. 3, pp. 1535–1548, 2015, doi: 10.3390/e17031535.
A. Sagingalieva, M. Kordzanganeh, N. Kenbayev, D. Kosichkina, T. Tomashuk, and A. Melnikov, “Hybrid Quantum Neural Network for Drug Response Prediction,” Cancers (Basel), vol. 15, no. 10, May 2023, doi: 10.3390/cancers15102705.
N. V Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” 2002.
M. Akrom, S. Rustad, and H. K. Dipojono, “Development of Quantum Machine Learning to Evaluate the Corrosion Inhibition Capability of Pyrimidine Compounds,” Mater Today Commun, p. 108758, Mar. 2024, doi: 10.1016/J.MTCOMM.2024.108758.
S. Budi, M. Akrom, G. A. Trisnapradika, T. Sutojo, W. Aji, and E. Prabowo, “Optimization of Polynomial Functions on the NuSVR Algorithm Based on Machine Learning: Case Studies on Regression Datasets,” Scientific Journal of Informatics, vol. 10, no. 2, 2023, doi: 10.15294/sji.v10i2.43929.
M. Akrom, “Investigation Of Natural Extracts As Green Corrosion Inhibitors In Steel Using Density Functional Theory,” 2022.
M. Akrom, “Quantum Support Vector Machine for Classification Task: A Review,” Journal of Multiscale Materials Informatics, vol. 1, no. 2, pp. 1–8, Jul. 2024, doi: 10.62411/jimat.v1i2.10965.
DOI: https://doi.org/10.21107/nero.v9i1.28082
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