Analysis of Consumer Purchase Patterns on Handphone Accessories Sales Using FP-Growth Algorithm

Fitrianah, Devi; Zain, Shifa Yasinta

doi:10.2991/assehr.k.210421.022

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…FP-Growth adalah metode yang bisa mengolah data transaksi dengan lebih cepat dan akurat [15]. Metode ini juga dapat digunakan untuk menganalisis data penjualan dengan menentukan jenis produk dan transaksi, merancang sistem pengelompokan data penjualan [16]. Cara ini dapat digunakan untuk menata tampilan produk agar dapat menarik pelanggan dan meningkatkan penjualan [17].…”

Section: Pendahuluanunclassified

Penerapan Metode FP-Growth Dalam Optimalisasi Bisnis Retail

Rhayatun Aviqah,

Muhammad,

Mandala

2024

CoSciTech

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Data transaksi penjualan merupakan sebuah hal yang dapat dimanfaatkan kembali untuk pengambilan keputusan bisnis. Namun pada kasus ini data transaksi tersebut tidak dimanfaatkan kembali untuk keperluan bisnis, dan hanya dijadikan sebagai arsip laporan penjualan. Algoritma FP-Growth yakni tingkatan dari algoritma asosiasi apriori yang menggunakan alternatif frekuensi itemset berdasarkan pada angka yang paling sering muncul pada tiap transaksi(frequent itemset) dalam sebuah kelompok data. Karakteristik dari algoritma FP-Growth yaitu struktur pada data yang dipakai berupa tree dengan nama FP-Tree. Dengan penggunaan FP-Tree, algoritma FP-Growth dapat mengekstrak frequent Itemset dari FP-Tree. Metode FP-Growth terbagi menjadi 3 tahapan-tahapan utama yaitu tahap pembangkitan conditional pattern base, tahap pembangkitan conditional FP-Tree, dan tahap pencarian frequent itemset. Dengan penerapan metode FP-Growth pada penelitian ini bisa digunakan untuk melihat pola penjualan produk. Hasil yang didapatkan berupa 5 interesting rules dengan memasukkan nilai min support 10% dan min confidance 50 % yaitu jika membeli popok maka membeli baju, jika membeli dot baby maka membeli baju, jika membeli topi maka membeli baju, jika membeli celana akan membeli baju, dan jika membeli singlet akan membeli baju. Diharapkan penelitian ini dapat membantu pemilik Retail Aura Moms Baby & Kids dalam memanfaatkan hasil transaksi penjualan sehingga hasil dapat dimanfaatkan dengan tepat. Hal ini dibuktikan dengan dibuatnya sistem E-Business yang dapat mengelola transaksi penjualan untuk menentukan pola pembelian produk pada Retail Aura Moms Baby & Kids.

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Section: Pendahuluanunclassified

Penerapan Metode FP-Growth Dalam Optimalisasi Bisnis Retail

Rhayatun Aviqah,

Muhammad,

Mandala

2024

CoSciTech

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“…Association rule is a method of analysis of the behavior of customers, specifically a particular group (Fitrianah & Zain, 2021). The Association Rule Mining method is a pattern of data linkages in the database and in the retail business field known as Market Basket Analysis (Bunda, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Product Sales Promotion Recommendation Strategy with Purchase Pattern Analysis FP-Growth Algorithm

Ismarmiaty¹,

Rismayati²

2023

SinkrOn

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The development of retail business technology is related to the need for management to meet customer demands by using technology. To help make effective sales strategic decisions, it is necessary to optimize the use of information technology on existing sales transaction data. The transaction database that has been stored as a company archive asset can be used for processing information that is useful in increasing product sales and promotions. This study aims to provide an analysis related to the product sales pattern of PT. X in Sumbawa Besar city. PT. X is a retail company that sells distributes daily consumer goods. The algorithm used is Frequent Pattern – Growth which is one of the algorithms in data mining used to find relationships in large data based on the number of occurrences of these data relationships. The Association Rule Mining method can be used in the retail business field, known as Market Basket Analysis. The application used for testing is Rapidminer 9.10. The research stages include: data collection, data preparation, FP-Growth algorithm implementation, result analysis and conclusions. The results of the tests carried out resulted in 819 rules with a total of 85 rules. The results of grouping strong rules based on the combination and number of products that produce information that is expected to be used as recommendations to promote products with discount, cross-selling, up-selling, product bundling and other types of promotions to increase product sales.

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“…Therefore, this study applies the FP-Growth method to analyze consumer purchasing patterns using supermarket sales data, aiming to effortlessly identify relationships between items frequently purchased together by customers. FP-Growth is a data processing technique that enables the identification of frequently occurring patterns in a set of transactional data [20]. The primary objective of this research is to analyze customer purchasing patterns in supermarkets using the FP-Growth method to comprehend customer buying behavior and identify relevant patterns from transactional data.…”

Section: Introductionmentioning

confidence: 99%

Exploring Customer Purchasing Patterns: A Study Utilizing FP-Growth Algorithm on Supermarket Transaction Data

Hairani,

Ximenes Guterres

2024

IJECSA

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The need to analyze consumer purchasing patterns using association techniques also lies in the increasingly fierce competition in the retail market. Supermarkets face the challenge of understanding their customers' buying patterns. By utilizing association techniques, supermarkets can identify customer buying trends and quickly and appropriately adjust their strategies. Thus, analyzing consumer purchasing patterns using association techniques is no longer an option but an urgent need for supermarkets that want to survive and thrive in a changing market. Therefore, this study aimed to analyze purchasing patterns in supermarkets using the FP-Growth method to understand purchasing behavior and identify relevant patterns from transaction data. The method used in this research was the FP-Growth association method to create association rules from customer transaction data. The findings of this research were the use of the FP-Growth method in analyzing supermarket customer purchasing patterns, which obtained 10 association rules for 2 itemsets and 11 association rules for 3 itemsets based on a minimum Support value of 30% and a minimum Confidence of 70%. The association rules generated by the FP-Growth method on 2 itemsets and 3 itemsets simultaneously bring up items often purchased by customers with the same pattern, namely Cooking Oil, Eggs, Flour, and Candy. This research concludes that the association rules formed can be used as a benchmark by supermarkets in preparing stock items and making strategies to increase sales for more profit.

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Analysis of Consumer Purchase Patterns on Handphone Accessories Sales Using FP-Growth Algorithm

Cited by 3 publications

References 9 publications

Penerapan Metode FP-Growth Dalam Optimalisasi Bisnis Retail

Penerapan Metode FP-Growth Dalam Optimalisasi Bisnis Retail

Product Sales Promotion Recommendation Strategy with Purchase Pattern Analysis FP-Growth Algorithm

Exploring Customer Purchasing Patterns: A Study Utilizing FP-Growth Algorithm on Supermarket Transaction Data

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