Market Basket Analisys  Data Mining Terhadap Data Penjualan Menggunakan Algoritma Frequent Pattern Growth (Fp Growth)

Musyaffa, Numan; Prasetyo, Arfhan; Sastra, Ricki

doi:10.31294/jki.v9i2.11133

Cited by 3 publications

(3 citation statements)

References 0 publications

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“…FP-Growth singkatan dari Frequent Pattern Growth, adalah salah satu opsi algoritma yang bisa dipakai untuk mendeteksi itemset atau sekumpulan data yang sering muncul [8]. Sebagai evolusi dari algoritma Apriori, FP-Growth menjadi alternatif dalam pengenalan rekaman yang sering muncul dalam suatu dataset [10].…”

Section: Fp-growthunclassified

Penerapan Fp-Growth Dalam Menganalisis Data Penjualan Di Toko X

Anggita Pratiwi,

Irawan,

Bahtiar

et al. 2024

jati

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Kemajuan pesat dalam bidang informatika telah mengubah berbagai aspek kehidupan, termasuk bisnis dan manajemen stok. Memahami pola pembelian pelanggan menjadi krusial dalam merancang strategi penjualan dan pengelolaan stok yang efektif bagi bisnis ritel. Penelitian ini bertujuan untuk menganalisis pola pembelian di Toko X menggunakan Algoritma FP-Growth. Penelitian ini menggunakan dataset data transaksi penjualan Toko X selama 1 bulan yaitu bulan Agustus 2023. Metode yang digunakan dalam penelitian ini adalah Knowledge Discovery in Database (KDD). Diterapkan dengan algoritma FP-Growth, melibatkan tahapan Data Selection, Data prepocessing, Data Transformation, Data mining dan Evaluation. Untuk menguji nilai minimum support, minimum confidence dan lift itu untuk mengevaluasi hasil analisis untuk mengidentifikasi pola pembelian. Dalam penelitian ini, support ditentukan dengan 0.6%, dan confidence 70% yang berhasil megidentifikasi 10 aturan asosiasi dengan lift ratio tertinggi. Misalnya, jika ada yang membeli Richesse Wafer kemungkinan besar pelanggan akan membeli Nabati Waffer dengan Lift 138.167, Nilai support 0.6% dan nilai confidence 83%. Implikasi praktis dari temuan pola pembelian ini mencakup penerapan strategi penjualan seperti Penempatan strategis produk, Penawaran bundel dan promosi dan Manajemen stok yang efisien. Selain itu, dengan menanfaatkan pola pembelian yang teridentifikasi, optimalisasi analisis stok dan produksi dapat dilakukan untuk meningkatkan efisiensi operasional secara menyeluruh.

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Section: Fp-growthunclassified

Penerapan Fp-Growth Dalam Menganalisis Data Penjualan Di Toko X

Anggita Pratiwi,

Irawan,

Bahtiar

et al. 2024

jati

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“…Data mining aims to obtain patterns or relationships that give a useful indication of one classification mode, namely, FP-Growth [12]. Data mining can be used for mathematical, statistical calculations, artificial intelligence, and machine learning in identifying and extracting useful information related to large databases [13]. The data mining function [5] is as follows :…”

Section: Data Miningmentioning

confidence: 99%

Association Analysis in Java Ateka for Stationery Sales Promotion Using the FP-Growth Algorithm

Wardani

Lestari

Daffa

et al. 2023

IOTA

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Every company or organization must have the right strategy to continue business or organizational activities. If not used, product sales data at the company will only become a pile of data; of course, it is regrettable if it is not used properly. The company can use the most product sales data to determine the next marketing strategy. To find out this data (the most sales), Association Rules Analysis is needed on the FP-Growth Algorithm method. This research aims to determine the association rule of Java ATK sales using the FP-Growth algorithm. This algorithm can be used for extensive data sets and to process Big Data. The results of this study are the FP Growth algorithm using association rules, which can be implemented in bookstore sales data with support count and minimum confidence parameters. A decimal value of 0.8 can form a product purchase correlation to increase stationery sales in Java Ateka. The rule obtained from the results of the FP-Growth calculation is that there are three transactions where if you buy item A, you will buy item B.

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“…Based on the description of the background above, an analysis of consumer purchasing patterns will be carried out in assisting the provision of products. In the case of analysis for scientific data mining, the rule association for the algorithm applied is the FP Growth algorithm (Musyaffa et al, 2021). The FP-Growth method is one of the methods of the Association Rule technique.…”

Section: Introductionmentioning

confidence: 99%

Fp Growth Algorithm Modeling for Product Inventory Analysis

Hartanti,

Atina

2023

icohetech

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Pusaka Tani is a shop that provides agricultural needs such as fertilizers, rice seeds and plant medicines. Sales transactions at the Farmer's Library still use a manual system, namely by using notes as proof of sales transactions. The amount of data that accumulates results in less useful data. So that Pusaka Tani cannot know the products that are often purchased by consumers simultaneously which results in many products being sold out without the knowledge of Pusaka Tani which results when consumers are not going to buy these products and products that are rarely purchased by consumers become unsold. The purpose of this study is to model FP Growt in the analysis of product stock supply. In the research conducted, the support value and confidence value used is a minimum support value of 30% and a minimum confidence value of 70%. The results obtained from the research conducted are for the highest lift ratio value of 1.67 and the lowest is 1.09

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Market Basket Analisys Data Mining Terhadap Data Penjualan Menggunakan Algoritma Frequent Pattern Growth (Fp Growth)

Cited by 3 publications

References 0 publications

Penerapan Fp-Growth Dalam Menganalisis Data Penjualan Di Toko X

Penerapan Fp-Growth Dalam Menganalisis Data Penjualan Di Toko X

Association Analysis in Java Ateka for Stationery Sales Promotion Using the FP-Growth Algorithm

Fp Growth Algorithm Modeling for Product Inventory Analysis

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