ANALISIS POLA PERMINTAAN PUBLIKASI DATA BADAN PUSAT STATISTIK MENGGUNAKAN ASSOCIATION RULE APRIORI

Farid Ridho, Fachruddin Mansyur

Abstract


BPS is a data provider body in Indonesia. In publishing, BPS uses a variety of media, one of which is the BPS website. To get data through the BPS website, users can visit the website then download the data they need. The services obtained by data users on the BPS website depend on the quality of the website. The better the quality, the better the service experience gained by data users. The method that can be used to improve the quality of a website is the web usage mining method. Web usage mining is the application of data mining techniques on web repositories to study usage patterns. The purpose of this study is to determine the pattern of data publication requests on the BPS website which can later be used as a reference to improve the quality of BPS website services. Based on the results of the study, it was found that data users tend to access the same data with different years simultaneously. For results by grouping data by title without year, obtained quite diverse rules.

Keywords: web usage mining, association rule, apriori

BPS merupakan badan penyedia data di Indonesia. Dalam mempublikasikan datanya, BPS menggunakan berbagai media, salah satunya adalah website BPS. Untuk mendapatkan data melalui website BPS, pengguna dapat mengunjungi website kemudian mengunduh data yang mereka butuhkan. Layanan yang didapatkan oleh pengguna data pada website BPS tergantung dari kualitas website tersebut. Semakin baik kualitasnya, semakin baik pula pengalaman pelayanan yang didapatkan oleh pengguna data. Metode yang dapat digunakan untuk meningkatkan kualitas suatu website adalah metode web usage mining. Web usage mining merupakan penerapan tekhnik data mining pada web repositori untuk mempelajari pola penggunaan. Tujuan dari penelitian ini adalah untuk mengetahui pola permintaan publikasi data pada website BPS yang nantinya dapat digunakan sebagai acuan untuk meningkatkan kualitas layanan website BPS. Berdasarkan hasil penelitian, didapatkan bahwa pengguna data cenderung mengakses data yang sama dengan tahun yang berbeda secara bersamaan. Untuk hasil dengan mengelompokan data berdasarkan judul tanpa tahun, diperoleh rules yang cukup beragam.

Kata kunci: web usage mining, association rule, apriori


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References


Cooley, R., Mobasher, B., & Srivastava, J. (1997, November). Web Mining: Information and Pattern Discovery on the World Wide Web. In ictai (Vol. 97, pp. 558-567).

Cooley, R., Tan, P. N., & Srivastava, J. (1999, August). Discovery of interesting usage patterns from web data. In International Workshop on Web Usage Analysis and User Profiling (pp. 163-182). Springer, Berlin, Heidelberg.

Agrawal, R., Imieli?ski, T., & Swami, A. (1993, June). Mining association rules between sets of items in large databases. In Acm sigmod record (Vol. 22, No. 2, pp. 207-216). ACM.

Agrawal, R., & Srikant, R. (1994, September). Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB (Vol. 1215, pp. 487-499).

Srivastava, J., Cooley, R., Deshpande, M., & Tan, P. N. (2000). Web usage mining: Discovery and applications of usage patterns from web data. Acm Sigkdd Explorations Newsletter, 1(2), 12-23.

Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37-37.

Patel, K. B., Chauhan, J. A., & Patel, J. D. (2011). Web mining in e-commerce: Pattern discovery, issues and applications. International Journal of P2P Network Trends and Technology, 1(3), 40-45.

BPS. Tentang BPS. Diakses pada Mei 17, 2019, dari https://www.bps.go.id/menu/1/informasi-umum.

Hahsler, M., & Chelluboina, S. (2011). Visualizing association rules: Introduction to the R-extension package arulesViz. R project module, 223-238.

Hornik, K., Grün, B., & Hahsler, M. (2005). arules-A computational environment for mining association rules and frequent item sets. Journal of Statistical Software, 14(15), 1-25.

Kumbhare, T. A., & Chobe, S. V. (2014). An overview of association rule mining algorithms. International Journal of Computer Science and Information Technologies, 5(1), 927-930.




DOI: http://dx.doi.org/10.20527/klik.v7i2.322

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