Market Basket Analysis with Apriori Algorithm and Frequent Pattern Growth (Fp-Growth) on Outdoor Product Sales Data

Wiwit Pura Nurmayanti; Hanipar Mahyulis  Sastriana; Abdul  Rahim; Muhammad  Gazali; Ristu Haiban Hirzi; Zuhut Ramdani; Muhammad  Malthuf

doi:10.51601/ijersc.v2i1.45

Authors

Wiwit Pura Nurmayanti universitas Hamzanwadi
Hanipar Mahyulis Sastriana Department of statistics, Faculty of Mathematics and Natural Sciences Universitas Hamzanwadi, Lommbok Timur, Nusa Tenggara Barat 83611, Indonesia.
Abdul Rahim Department of Pharmacy, Faculty of Health Universitas Hamzanwadi, Lommbok Timur, Nusa
Muhammad Gazali Department of statistics, Faculty of Mathematics and Natural Sciences Universitas Hamzanwadi, Lommbok Timur, Nusa Tenggara Barat 83611, Indonesia.
Ristu Haiban Hirzi Department of statistics, Faculty of Mathematics and Natural Sciences Universitas Hamzanwadi, Lommbok Timur, Nusa Tenggara Barat 83611, Indonesia.
Zuhut Ramdani Department of statistics, Faculty of Mathematics and Natural Sciences Universitas Hamzanwadi, Lommbok Timur, Nusa Tenggara Barat 83611, Indonesia.
Muhammad Malthuf Social Studies Education, Faculty of Teacher Training and Education Universitas Islam Negeri Mataram, Mataram, Nusa Tenggara Barat 83125, Indonesia.

DOI:

https://doi.org/10.51601/ijersc.v2i1.45

Keywords:

Keywords: Outdoor goods, MBA (Market Basket Analysis), Association Rule, Apriori Algorithm, FP-Growth Algorithm.

Abstract

Indonesia is an equatorial country that has abundant natural wealth from the seabed to the top of the mountains, the beauty of the country of Indonesia also lies in the mountains that it has in various provinces, for example in the province of West Nusa Tenggara known for its beautiful mountain, namely Rinjani. The increase in outdoor activities has attracted many people to open outdoor shops in the West Nusa Tenggara region. Sales transaction data in outdoor stores can be processed into information that can be profitable for the store itself. Using a market basket analysis method to see the association (rules) between a number of sales attributes. The purpose of this study is to determine the pattern of relationships in the transactions that occur. The data used is the transaction data of outdoor goods. The analysis used is the Association Rules with the Apriori algorithm and the frequent pattern growth (FP-growth) algorithm. The results of this study are formed 10 rules in the Apriori algorithm and 4 rules in the FP-Growth algorithm. The relationship pattern or association rule that is formed is in the item "if a consumer buys a portable stove, it is possible that portable gas will also be purchased" at the strength level of the rules with a minimum support of 0.296 and confidence 0.774 at Apriori and 0.296 and 0.750 at FP-Growth.

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