Analisis Pola Pembelian Makanan dan Minuman di Kedai Distrik Menggunakan Algoritma Fp-Growth

Tiana Dewi; Rini Astuti; Yudhistira Arie Wijaya

doi:10.36499/jinrpl.v6i1.10303

Analisis Pola Pembelian Makanan dan Minuman di Kedai Distrik Menggunakan Algoritma Fp-Growth

Tiana Dewi, Rini Astuti, Yudhistira Arie Wijaya

Abstract

District Kedai is one of the many shops in Cirebon district, located on Jl. Raya Kalikoa, District. Kedawung, Cirebon Regency, West Java which operates in the food and beverage sector. Every day, sales transactions occur at the District Store. Sometimes consumers don't just buy one food or drink, but two or more foods or drinks in one transaction. Transaction recording is still limited to archives and has not been utilized, only left to accumulate by the District Store so that it does not provide information for the Store. Transaction data is also related to shopping patterns which can be used to determine the results of sales of goods in order to maximize sales to meet buyers' needs, therefore it is very important to know the purchasing patterns frequently made by Kedai District customers in order to develop strategies and increase sales. The aim of this research is to find out what the support and confidence values are to get an association to occur using the Association Rules method and the FP-Growth Algorithm. To find out consumer shopping patterns and find out how often item combinations appear in the sales data, the FP-Growth algorithm is an alternative algorithm that can be used to determine the data set that appears most often (frequent item set) in a data set. Therefore, this research will involve the process of collecting and processing data on food and drink purchase transactions that occurred at Kedai District during the period 1-31 October totaling 2,360 transactions. Next, the FP-Growth algorithm is used to identify purchasing patterns that have significant value and tend to repeat themselves. By conducting frequent itemsets using association rule techniques, and determining the support value and confident value to find out how often relationships appear between itemsets. Therefore, based on the final results obtained from this research, the relationship pattern generated from District Store transaction data with a minimum (support 0.7825) and (confidence 0.8) produces 1 rule. If a customer buys Grilled Sausage, they will buy Sate Suki with a support value of 0.161% and a confidence of 0.806%.

Keywords

Data mining, Association, FP-Growth Algorithm, Kedai

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DOI: http://dx.doi.org/10.36499/jinrpl.v6i1.10303