PENERAPAN ALGORITMA K-MEANS TERHADAP EVALUASI WEBSITE E-COMMERCE

Febriyanto A.; Dzulqornain Sabri S. Anggie; Ida Mulyadi

doi:10.59003/nhj.v3i12.1124

Authors

Febriyanto A. STMIK Profesional Makassar
Dzulqornain Sabri S. Anggie STMIK Profesional Makassar
Ida Mulyadi STMIK Profesional Makassar

DOI:

https://doi.org/10.59003/nhj.v3i12.1124

Keywords:

K-Means, E-Commerce, Clustering

Abstract

Facing large amounts of high-dimensional transaction data, clustering approaches often face challenges that include elasticity, weak high-dimensional data processing capabilities, sensitivity to data order over time, independence from parameters, and the ability to manage noise. These problems can limit a method from producing accurate predictions. Experiments conducted with data samples collected from 50 different mobile phones purchased on Lazada yielded the following results: K-means outperforms Single-pass in evaluating e-commerce transactions because it has higher intra-class dissimilarity and inter-class similarity. K-means clustering is an approach to the effective and flexible organization of large datasets. The results of a clustering algorithm are sensitive not only to the total number of clusters but also to how they were originally arranged. Therefore, it is easy to show that the clustering results are locally optimized. Further research conducted into the elements that influence the number of clusters produced by this method as well as the initial location of clustering centers is a very important endeavor.

Downloads

Download data is not yet available.

References

W. Wang, Application of E-Commerce Recommendation Algorithm in Consumer Preference Prediction, J. Cases Inf. Technol. JCIT, vol. 24, no. 5, pp. 1–28, Feb. 2021.

K. U. Sarker, M. Saqib, R. Hasan, S. Mahmood, S. Hussain, A. Abbas, et al., A Ranking Learning Model by K-Means Clustering Technique for Web Scraped Movie Data, Computers, vol. 11, no. 11, p. 158, Nov. 2022.

X. Xiahou and Y. Harada, B2C E-Commerce Customer Churn Prediction Based on K-Means and SVM, J. Theor. Appl. Electron. Commer. Res., vol. 17, no. 2, pp. 458–475, Jun. 2022.

H. Singh and P. Kaur, An Effective Clustering-Based Web Page Recommendation Framework for E-Commerce Websites, SN Comput. Sci., vol. 2, no. 4, p. 339, Jun. 2021.

P. Valdiviezo-Diaz, Partitional clustering based on PCA method for segmentation of products, in 2021 16th Iberian Conference on Information Systems and Technologies (CISTI), 2021, pp. 1–4.

M. S. Kumar and J. Prabhu, A hybrid model collaborative movie recommendation system using K-means clustering with ant colony optimisation, Int. J. Internet Technol. Secur. Trans., vol. 10, no. 3, pp. 337–354, Jan. 2020.

T. Hariguna, W. M. Baihaqi, and A. Nurwanti, Sentiment Analysis of Product Reviews as A Customer Recommendation Using the Naive Bayes Classifier Algorithm, Int. J. Inform. Inf. Syst., vol. 2, no. 2, pp. 48–55, Sep. 2019.

D. R. K. Raja, G. H. Kumar, S. M. Basha, and S. T. Ahmed, Recommendations based on Integrated Matrix Time Decomposition and Clustering Optimization, Int. J. Perform. Eng., vol. 18, no. 4, pp. 298, Apr. 2022.

V. Arul, A. Kumar, and A. Agarwal, Segmenting Mall Customers Data to Improve Business into Higher Target using K-Means Clustering, in 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), 2021, pp. 1602–1604.

H. Pan and X. Yang, Fast clustering algorithm of commodity association big data sparse network, Int. J. Syst. Assur. Eng. Manag., vol. 12, no. 4, pp. 667–674, Aug. 2021.

B. Mulyawan, M. V. Christanti, and R. Wenas, Recommendation Product Based on Customer Categorization with K-Means Clustering Method, IOP Conf. Ser. Mater. Sci. Eng., vol. 508, no. 1, 012123, Apr. 2019.

L. Rajput and S. N. Singh, Customer Segmentation of E-commerce data using K-means Clustering Algorithm, in 2023 13th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2023, pp. 658–664.

S. Kumaresh, R. Haran, and M. M. Jarret, Analytics of e-Commerce Platforms Based on User-Experience (UX), in S. L. Peng, S. Y. Hsieh, S. Gopalakrishnan, and B. Duraisamy, Eds., Intelligent Computing and Innovation on Data Science, Springer Nature, 2021, pp. 309–318. (Lecture Notes in Networks and Systems)