Quantum Perceptron in Predicting the Number of Visitors to E-Commerce Websites in Indonesian

Solikhun Solikhun; Dinda Carissa Arishandy; Ela Roza Batubara; Poningsih

doi:10.32736/sisfokom.v14i2.2334

Authors

Solikhun Solikhun Department of Informatics Engineering, STIKOM Tunas Bangsa
Dinda Carissa Arishandy Department of Informatics Engineering, STIKOM Tunas Bangsa
Ela Roza Batubara Department of Information Systems, STIKOM Tunas Bangsa
Poningsih Departemnt of Master Informatics, STIKOM Tunas Bangsa

DOI:

https://doi.org/10.32736/sisfokom.v14i2.2334

Keywords:

Quantum Perceptron, E-Commerce, Website Visitor Prediction, Quantum Computing, Marketing Strategies

Abstract

In the current digital era, e-commerce has become the backbone of Indonesia's digital economy, which is experiencing rapid growth. However, competition in this industry is becoming increasingly fierce, indicating the importance of predicting the number of website visitors for an effective marketing strategy. Quantum Perceptron, the latest quantum computing innovation, promises a more accurate and efficient approach compared to conventional methods such as classical Perceptron. This research proposes the use of Quantum Perceptron to predict the number of visitors on large e-commerce platforms in Indonesia. The data used in the research is data on the number of e-commerce visitors obtained from the katadata.com website. Data from Shopee, Tokopedia, Lazada, Blibli, and Bukalapak were used to analyze and compare predictions with classical perceptron methods, showing the significant potential of Quantum Perceptron in supporting the development of more efficient business strategies. The research results show that the Quantum Perceptron algorithm can make predictions very well compared to the classical perceptron, proven by the Quantum Perceptron having a perfect accuracy of 100% with a total of 2 epochs while the classical perceptron has 100% accuracy with a total of 10 epochs. Quantum perceptron has better performance and shorter time, this can be seen from the smaller number of epochs.

References

A. Perdomo-Ortiz, et al., "A quantum approach to training neural networks," Quantum Science and Technology, vol. 5, no. 2, pp. 145-156, 2021.

Y. Cao, J. Romero, and A. Aspuru-Guzik, "Quantum machine learning and quantum algorithms," Nature Reviews Physics, vol. 3, pp. 12-25, 2020.

H. A. Kadhim and A. Mohammed, "Quantum perceptron for pattern recognition in big data," IEEE Access, vol. 8, pp. 87242-87256, 2020.

J. Liu, et al., "Quantum neural networks for high-dimensional datasets," IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 3, pp. 1-12, 2021.

R. Subramanian and A. John, "Big data analysis in e-commerce: challenges and opportunities," Journal of Electronic Commerce Research, vol. 22, no. 1, pp. 65-82, 2021.

K. Sundar and A. Ren, "Challenges in scalable e-commerce systems," Journal of Computing and Information Technology, vol. 29, pp. 113-123, 2021.

M. Benedetti, et al., "A review of quantum perceptron models," IEEE Transactions on Artificial Intelligence, vol. 12, pp. 102-112, 2022.

S. K. Goyal and A. Pathak, "Quantum machine learning algorithms for e-commerce data analysis," Applied Quantum Information, vol. 6, pp. 122-136, 2023.

P. Rebentrost, et al., "Quantum support vector machine for big data analysis," Journal of Quantum Computing, vol. 8, pp. 220-234, 2020.

H. Neven, et al., "Quantum optimization and machine learning," IEEE Spectrum, vol. 59, pp. 28-35, 2022.

S. Lloyd and P. Kok, "Machine learning in quantum computing," Physics Today, vol. 75, no. 6, pp. 32-39, 2022.

J. Preskill, "Quantum computing in the NISQ era and beyond," Quantum Information, vol. 3, no. 8, pp. 13-25, 2021.

M. Schuld and F. Petruccione, "Quantum machine learning: Recent developments and future directions," Frontiers in Physics, vol. 9, pp. 220-232, 2023.

S. K. Sharma and K. A. Kim, "Leveraging quantum computing for e-commerce prediction models," Journal of Electronic Commerce Research, vol. 20, pp. 332-344, 2023.

T. L. Mailloux, "Predicting web traffic using quantum algorithms," Journal of Big Data and Quantum Computing, vol. 4, pp. 45-58, 2021.

B. X. Chen and J. G. Mauk, "Quantum-enhanced predictive models for e-commerce," Journal of Business Analytics, vol. 17, pp. 102-120, 2022.

P. Alvarez, et al., "Evaluating quantum machine learning models for e-commerce growth," Journal of Quantum Information Systems, vol. 9, no. 2, pp. 56-73, 2023.

F. H. Smith and N. K. Verma, "Implementing quantum predictive algorithms for e-commerce," IEEE Access, vol. 12, pp. 15926-15935, 2024.

C. F. Fox, "Quantum computing applications in business and e-commerce," Journal of Quantum Business Models, vol. 22, pp. 74-86, 2024.

D. L. Stover, "A study on the impact of quantum computing on business processes," Quantum Information Systems, vol. 13, pp. 120-134, 2022.

H. W. Leung and G. Novak, "Quantum data science in the future of business analytics," Journal of Quantum Research, vol. 6, pp. 233-248, 2022.

B. Chen and S. Boyd, "Quantum optimization techniques in large-scale e-commerce systems," Journal of Computational Business Analytics, vol. 18, no. 2, pp. 41-56, 2023.

A. Patel and M. Raghavan, "Quantum perceptron-based models for data prediction," IEEE Transactions on Neural Networks, vol. 33, no. 5, pp. 202-214, 2023.

P. Wong and M. Yung, "Application of quantum computing in customer data prediction models," Journal of Business Intelligence, vol. 11, pp. 92-107, 2023.

S. Aggarwal and V. Bhatia, "Quantum computing for large-scale data analysis in e-commerce," Quantum Computing and Big Data, vol. 7, pp. 34-46, 2024.