Pencarian Pola Akses Pengunjung Toko Online Menggunakan Weighted Graph Web Usage Mining

Helmy Helmy

Abstract


Abstract— The growth of online stores is proportional to the
increase in web usage data that is generated. Web Usage Mining can generate useful information based on web usage data. This information is required by the owner of the online shop to find information on frequently accessed pages and demanded items by visitors. This study is using Weighted Graph Web Usage Mining method for generating online shoppers access patterns. This methode include collecting web usage data when client using AJAX interface in real time, ¬ pre-processing to generate the database traversal and discovering pattern with Weighted Frequent Patterns Mining methods. The results show that Weighted Graph Web Usage Mining can generate informations about frequently accessed pages and demanded items by visitors in a given period based on visitors access pattern.

Keywords— web usage mining, weighted graph web usage
mining, online shop

Intisari— Pertumbuhan toko online yang semakin meningkat berbanding lurus dengan peningkatan data penggunaan web yang dihasilkan. Web Usage Mining dapat menghasilkan informasi yang berguna berdasarkan data penggunaan web. Informasi ini diperlukan oleh pemilik toko online untuk mendapatkan informasi mengenai halaman yang sering diakses dan item-item yang diminati oleh pengunjung. Pada penelitian ini menggunakan metode Weighted Graph Web Usage Mining untuk menghasilkan pola akses pengunjung toko online. Metode ini meliputi pengumpulan data penggunaan web pada level klien menggunakan antar muka AJAX secara real time, ¬preprocessing untuk menghasilkan basis data traversal dan penemuan pola menggunakan metode Weighted Frequent Patterns Mining. Hasil penelitian menunjukkan Weighted Graph Web Usage Mining dapat menghasilkaninformasi mengenai
halaman yang sering diakses dan item-item yang diminati oleh
pengunjung dalam periode tertentu berdasarkan pola akses
pengunjung.

Kata Kunciweb usage mining, weighted graph web usage
mining, toko online


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References


B. Liu, Web Data Mining, Second. Berlin: Springer Verlag New York, Inc., 2011, p. 605.

Q. Song and M. Shepperd, “Mining web browsing patterns for Ecommerce,” Computers in Industry, vol. 57, no. 7, pp. 622–630, Sep. 2006.

G. Velayathan, “Behavior-Based Web Page Evaluation,” in

IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2006, pp. 6–9.

M. Heydari, R. A. Helal, and K. I. Ghauth, “A Graph-Based Web Usage Mining Method Considering Client Side Data,” in 2009 International Conference on Electrical Engineering and Informatics, 2009, no. August, pp. 147–153.

M. Heydari, R. Alsaqour, K. Imran, and K. Vaziry, “A Weighted Graph Web Usage Mining Method to Evaluate Usage of Websites Department of Computer Science , Faculty of Information Science and Technology , University,” Australian Journal of Basic and Applied Sciences, vol. 5, no. 9, pp. 1606–1616, 2011.

U. Fayyad, G. Piatetsky-shapiro, and P. Smyth, “From Data Mining to Knowledge Discovery in,” American Association for Artificial Intelligence, vol. 17, no. 3, pp. 37–54, Jul-1996.

N. K. Tyagi, A. K. Solanki, and M. Wadhwa, “Analysis of Server Log by Web Usage Mining for Website Improvement,” IJCSI International Journal of Computer Science, vol. 7, no. 4, pp. 17–21, 2010.

S. S. Skiena, The Algorithm Design Manual, Second. London: Springer-Verlag London, 2008.

S. D. Lee and H. C. Park, “Mining Weighted Frequent Patterns from Path Traversals on Weighted Graph,” IJCSNS International Journal of Computer Science and Network Security, vol. 7, no. 4, pp. 140–148, 2007.

K. Mihara, Koichiro; Terabe, Masahiro; Hashimoto, “A Graph-Based Web Usage Mining Considering Page Browsing Time,” in KICSS 2007 : The Second International Conference on Knowledge, Information and Creativity Support Systems, 2007.

G. Li, T. Beijing, I. Engineering, B. Yang, and J. Guo, “Weighted Frequent Patterns Mining Over Data Streams,” in 2nd International Conference on Industrial and Information Systems Weighted Frequent Patterns Mining Over Data Stream, 2010, no. c, pp. 1–4.

H. Kim and P. K. Chan, “Implicit indicators for interesting web pages,” in International Conference on Web Information Systems, 2005.

M. Claypool, D. Brown, P. Le, and M. Waseda, “Inferring User Interest,” IEEE Internet Computing, vol. 5, no. 6, pp. 32–39, 2001.




DOI: http://dx.doi.org/10.22146/jnteti.v3i1.37

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