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Jurnal Sisfokom (Sistem Informasi dan Komputer)

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

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

Published May 23, 2025• 0 citations
Publisher

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.

DOI
10.32736/sisfokom.v14i2.2334

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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.
Rice imports are crucial to ensure a country's food availability, especially when domestic production is insufficient. Because rice is the staple food of Indonesians, a spike in rice prices could cause social unrest. Rice imports have a strategic role in maintaining food stability and reducing the risk of price instability. This research aims to utilize the Quantum Perceptron algorithm to predict rice imports more effectively. Quantum Perceptron is a new approach that combines the principles of quantum mechanics with artificial intelligence to improve prediction performance. Researchers used data on the number of rice imports from the leading countries of origin obtained from the Central Statistics Agency using 7 variables x1 to x7. The research results show that the quantum perceptron algorithm can make predictions very well, proven by a perfect accuracy of 100% with a total of 20 epochs. This result is still better than the classical perceptron, which has 100% accuracy but with a larger number of epochs, namely 50. Quantum perceptron has better performance and shorter time, which can be seen from the smaller number of epochs compared to the classical perceptron.
The wholesale rice trade in Indonesia encounters various challenges in forecasting prices. These challenges are influenced by factors such as weather, government policies, global market conditions, and other economic variables. Accurate price predictions are crucial for informing government policy in a timely manner. This research introduces a new approach that utilizes the Quantum Perceptron algorithm to forecast rice prices. The algorithm, an innovative method in quantum computing, is expected to enhance the efficiency and effectiveness of price predictions. Although the research is still in the analytical stage, the use of Quantum Perceptron shows promise in dynamically addressing the complexity of market data and the variability of factors affecting rice prices. The method focuses on developing models that can leverage quantum computing to process information more effectively than classical methods. By harnessing the unique properties of quantum mechanics, such as superposition and entanglement, Quantum Perceptron can identify complex patterns and optimize predictions of future rice prices. The research describes the implementation of quantum algorithms in the context of the Indonesian rice wholesale market, including the technical challenges encountered and future development prospects. The research utilizes quantum computing along with the perceptron algorithm. The researchers focused on analyzing the quantum perceptron algorithm because of the limited availability of quantum computing devices. The findings of this research are confined to analysis. In order to advance this research, the author recommends that future studies employ quantum devices to achieve more accurate predictions
The application of Quantum Computing to improve the perceptron algorithm in unemployment prediction is a new aspect of this research. This study focuses on unemployment, which is a big challenge for the young generation in Indonesia, especially in the North Sumatra region. This research applies the quantum perceptron method to provide an alternative solution in predicting the unemployment rate. The data used in this analysis comes from the North Sumatra Central Statistics Agency and includes published unemployment rates (TPT) for individuals aged 15 years and over from 2017 to 2023. This research uses seven variables ranging from x1 to x7 to produce accurate data. Quantum perceptron methods offer specific advantages over traditional methods, including higher computing speeds and the ability to handle greater data complexity. This analysis aims to identify unemployment patterns and trends in North Sumatra and provide more accurate predictions by applying the quantum perceptron method. Although the results of this research are still limited to analysis, this research shows promising results and opens up opportunities for further, more in-depth research. This research is limited to predicting unemployment rates in North Sumatra. The use of quantum computing using the quantum perceptron method shows great potential for application to various other socio-economic problems in the future. This research contributes by introducing a new approach that utilizes quantum technology to improve prediction accuracy in economic analysis.
Quantum computing is a technology that takes advantage of quantum effects, which operates using quantum computing, resulting in increased algorithm speed compared to the speed of algorithms that use classical computing. This study aims to classify bank marketing data in determining the eligibility of customers who are entitled to become depositors with the quantum perceptron algorithm using calculations with 5 variables, namely employment, status, education, home credit, and bank loans. The result of this research is the analysis of bank marketing classification data to determine the eligibility of customers who are entitled to become depositors using the quantum perceptron algorithm with quantum computation.
This study explores the application of quantum computing and quantum artificial intelligence methodologies in analyzing and optimizing e-business performance metrics. A comprehensive dataset spanning 30 days was generated to simulate key metrics, including daily sales, website visits, customer satisfaction scores, marketing campaign effectiveness, and customer retention rates. The dataset reflects realistic fluctuations and trends encountered by an e-business, providing numerical insights into operational dynamics and customer interactions. Data analysis reveals that daily sales range from $100 to $1000, which is dependent on market demand and promotional activities. Daily website visits are between 1000 and 5000, showing the pattern of engagement and digital marketing effectiveness. Customer satisfaction scores range between 3.0 and 5.0, indicating mixed experiences for customers in the period under study. This is because marketing effectiveness ranges from 0 to 1, based on fluctuations in traffic and sales conversions. Customer retention increases by 60%–90%, showing that the e-business has devised a mechanism for gaining customer loyalty. These findings help emphasize the importance of quantum computing and quantum artificial intelligence in improving decision-making processes, better resource allocation, and enhancement of customer engagement strategies in digital business contexts.
The remarkable expansion of e-commerce, together with its competitive market, has created difficulties for traditional machine learning models to forecast customer actions when dealing with intricate datasets. The encountered problems with conventional machine learning models in predictive analytics for customer behaviour in e-commerce have led researchers to explore quantum computing algorithms as a potential solution. The main goal of this research work is to analyse how quantum computing technology and quantum algorithms can address the problems of inaccuracy and high dimensionality and speed and unbalanced datasets. The system implementation integrates quantum infrastructure through quantum processing units that utilize parallel processing to handle large datasets efficiently. The proposed algorithms for customer behaviour prediction models include Quantum Support Vector Machines, Quantum K-means Clustering, Quantum Boltzmann Machines, Quantum Principal Component Analysis, Quantum Neural Networks, and Quantum Ensemble Learning. These algorithms use quantum parallelism to achieve better accuracy while speeding up computations and handling unbalanced datasets. The AI framework combines traditional machine learning pipeline with quantum computing enhancements to implement quantum algorithms. The framework consists of data pre-processing followed by Quantum Machine Learning (QML) model training and Quantum-Powered AI to address imbalanced data while reducing prediction biases. Real-time insights and visualization and analytics from the advance predictive analytics dashboard enable stakeholders to make data-driven decisions. The system implements security and privacy measures through quantum cryptography which uses quantum cryptographic algorithms to boost customer data security. The research results present a potential solution to predictive analytics challenges which would transform the E-commerce industry.