Utilization of Sales Data Analysis for Product Recommendation Systems in E-Commerce Using the Apriori Algorithm
DOI:
https://doi.org/10.64803/jocsaic.v1i2.17Kata Kunci:
Data Mining, Apriori Algorithm, E-Commerce, Recommendation System, Sales Data AnalysisAbstrak
The rapid development of e-commerce has significantly increased the volume of sales transactions and customer interaction data. This presents an opportunity for businesses to leverage data mining techniques to extract valuable insights that support decision-making processes. One such application is the development of product recommendation systems, which play a crucial role in enhancing customer satisfaction and driving sales. This research focuses on utilizing sales transaction data to build a product recommendation system using the Apriori algorithm, a well-known method for association rule mining. The study begins with the collection and preprocessing of transaction data from an e-commerce platform. Through the application of the Apriori algorithm, frequent itemsets are identified, and association rules are generated based on specified support and confidence thresholds. These rules reveal purchasing patterns and relationships between products that are frequently bought together. The system then uses these patterns to recommend relevant products to users, aiming to improve cross-selling opportunities and personalize the shopping experience. The results demonstrate that the Apriori-based recommendation model is effective in identifying meaningful product combinations and can be implemented as a lightweight, interpretable alternative to more complex machine learning methods. Furthermore, the system helps e-commerce businesses optimize inventory management and marketing strategies by understanding customer buying behavior. This research concludes that the integration of the Apriori algorithm into recommendation systems provides tangible benefits for e-commerce platforms seeking data-driven personalization solutions.
Referensi
[1] A. A. Nabi, F. H. Tunio, M. Azhar, M. S. Syed, and Z. Ullah, “Impact of information and communication technology, financial development, and trade on economic growth: Empirical analysis on N11 countries,” J. Knowl. Econ., vol. 14, no. 3, pp. 3203–3220, 2023.
[2] A. Sharma, S. K. Mishra, and V. K. Srivastav, “The Evolution And Impact Of E-Commerce.,” J. Namibian Stud., vol. 33, 2023.
[3] E. E. Pramiarsih, “Consumer behavior in the digital era,” Int. J. Financ. Econ., vol. 1, no. 3, pp. 662–674, 2024.
[4] Z. Kedah, “Use of e-commerce in the world of business,” Startupreneur Bus. Digit. (SABDA Journal), vol. 2, no. 1, pp. 51–60, 2023.
[5] O. Ogunwole, E. C. Onukwulu, N. J. Sam-Bulya, M. O. Joel, and G. O. Achumie, “Optimizing automated pipelines for realtime data processing in digital media and e-commerce,” Int. J. Multidiscip. Res. Growth Eval., vol. 3, no. 1, pp. 112–120, 2022.
[6] U. Javed, K. Shaukat, I. A. Hameed, F. Iqbal, T. M. Alam, and S. Luo, “A review of content-based and context-based recommendation systems,” Int. J. Emerg. Technol. Learn., vol. 16, no. 3, pp. 274–306, 2021.
[7] A. A. Agha, A. Rashid, R. Rasheed, S. Khan, and U. Khan, “Antecedents of customer loyalty at telecomm sector.,” Turkish Online J. Qual. Inq., vol. 12, no. 9, 2021.
[8] N. Johnson, M. S. Purwanegara, and N. B. Mulyono, “Enhancing E-commerce with big data: from browsing to buying through recommendation systems,” Int. J. Entrep. Bus. Creat. Econ., vol. 4, no. 1, p. 130, 2024.
[9] S. Khodijah, C. A. Rizki, and M. Hasanuddin, “Journal of Computer Science Artificial Intelligence,” vol. 1, no. 1, pp. 1–6, 2024.
[10] M. Hasanuddin, “Journal of Computer Science Artificial Intelligence Optimization of Computer Network Performance Using Heuristic Algorithms,” vol. 1, no. 1, pp. 12–17, 2024.
[11] L. Valtonen, S. J. Mäkinen, and J. Kirjavainen, “Advancing reproducibility and accountability of unsupervised machine learning in text mining: Importance of transparency in reporting preprocessing and algorithm selection,” Organ. Res. Methods, vol. 27, no. 1, pp. 88–113, 2024.
[12] A. Kurniawan and N. Suwaryo, “Analysis of the Apriori Algorithm for Enhancing Retail Product Staple Sales Recommendations,” Int. J. Softw. Eng. Comput. Sci., vol. 3, no. 3, pp. 449–456, 2023.
[13] M. Hasanuddin, B. E. Susanto, S. Ginting, and F. Rizaldi, “Analisis Minat Siswa Kelas 1 SMK Pada Ekstrakulikuler Sepak Bola Dengan Metode Technology Acceptance Model,” vol. 4, no. 1, pp. 52–58, 2025.
[14] F. T. A. Hussien, A. M. S. Rahma, and H. B. A. Wahab, “Recommendation systems for e-commerce systems an overview,” in Journal of Physics: Conference Series, IOP Publishing, 2021, p. 12024.
[15] A. T. Reni, “The Influence Of Personalization, User Experience, And Digital Marketing Strategy On Customer Loyalty With Churn Rate As A Mediating Variable In The E-Commerce Industry,” J. Ilmu Sos. Mamangan, vol. 12, no. 3, pp. 1522–1530, 2024.
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Hak Cipta (c) 2024 Muhammad Noor Hasan Siregar, Furqan Khalidy, Rismayanti, Khairunnisa (Author)
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