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Bella Audi Najib
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Penerapan Data Mining Terhadap Data Penjualan Lapis Bogor Sangkuriang Dengan Metode Algoritma Apriori Bella Audi Najib; Nining Suryani
JURNAL TEKNIK KOMPUTER Vol 6, No 1 (2020): JTK-Periode Januari 2020
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (102.213 KB) | DOI: 10.31294/jtk.v6i1.6765

Abstract

Determination of the pattern of purchasing goods and layout of goods based on the tendency of consumers to buy goods canbe one solution for the Bogor Sangkuriang Lapis Shop in developing marketing strategies so as to increase sales of Lapis Bogor Sangkuriang products. The algorithm that can be used to determine the pattern of purchasing goods and this layout is the Apriori Algorithm which is one of the data mining algorithms in the formation of rule mining associations. With frequent items in a priori algorithm by producing a small frequent, without doing candidate generation and minimizing the completion stages starting at k-1 items or the first stage in the a priori algorithm then used with the FP-Growth method where this method is very significant with the a priori algorithm , efficient in terms of time, the completion stage is faster, produces less frequent items and is more detailed in describing frequent item results because frequent results with a value <1 are still shown, not deleted. This research produced the most sold products for layer cakes are Original Cheese, Cheese Brownies, Full Talas. Based on the rules of the final association, it is known that if you buy the Bogor Sangkuriang Lapis Original Cheese cake layer, you will buy the Lapis Bogor Sangkuriang Brownies Cheese and Full Talas Cheese with a support value of 30% and a confidence value of 70%. Based on these results the company can make the decision to develop a strategy that is done next
Co-Authors Nining Suryani