MARKET BASE ANALYSIS IN DROPSHIP BUSINESS WITH APRIORI ALGORITHM IN DETERMINING R-BASED PRODUCT BUNDLING

Authors

  • Anni Karimatul Fauziyyah FKOM Universitas Alma Ata

DOI:

https://doi.org/10.21927/ijubi.v2i1.967

Keywords:

Association algorithm, product bundling, dropshipper, minimum support, confidence

Abstract

The association method will associate data using a priori (experience) rules that meet the minimum support requirements, namely the combination of each item in the database and the minimum confidence requirement, namely the strength of the relationship between items in the association rules. In this study, the sales transaction data for three months will be analyzed from the dropshipper in the field of sales of beauty and fashion products using the association method with a priori algorithm to look for product bundling data patterns. The minimum value of support is 10% and 20% confidence, and with % ain function where Ordinary Shampoo items become the main products in product bundling, obtained 15 packages of the strongest products with lift values more than 1.1.

References

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M. S. Yadav and K. B. Monroe. “How buyers perceive savings in a bundle price: An examination of a bundle's transaction value,†Journal of Marketing Research, 1993, pp. 350-358.

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Yanto, R., & Di Kesuma, H. (2017). Pemanfaatan Data Mining Untuk Penempatan Buku Di Perpustakaan Menggunakan Metode Association Rule. JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI, 4(1), 1-10.

Bhandari, Akshita, Ashutosh Gupta, Debasis Das, 2015. Improvised Apriori Algorithm Using Frequent Pattern Tree For Real Time Applications In Data Mining, in: Procedia Computer Science 46 (2015): 644-651.

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Published

2019-07-09

Issue

Vol. 2 No. 1 (2019): Indonesian Journal of Business Intelligence (IJUBI)

Section

Articles

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