Market Basket Analysis for Books Sales Promotion using FP Growth Algorithm, Case Study : Gramedia Matraman Jakarta

Firmansyah, Firmansyah; Agus, Yulianto

doi:10.31289/jite.v4i2.4539

Cited by 11 publications

(13 citation statements)

References 10 publications

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“…The FP-Growth algorithm is an alternative used in determining the data set that often appears in the data set [14]. FP-Growth is also used as a system to recommend a product [15].…”

Section: E Fp-growthmentioning

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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“…The FP-Growth algorithm is an alternative used in determining the data set that often appears in the data set [14]. FP-Growth is also used as a system to recommend a product [15].…”

Section: E Fp-growthmentioning

confidence: 99%

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

Wardani

Lestari

Daffa

et al. 2023

IOTA

View full text Add to dashboard Cite

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“…FP-Growth merupakan algoritma data mining yang memiliki performa yang lebih baik dan lebih efisien bila dibandingkan dengan algoritma analisis asosiasi (hubungan antar penjualan barang), seperti apriori. FP-Growth menggunakan struktur pohon 10.37600/tekinkom.v4i2.309 ISSN: 2621-1556 202 FP-Tree, sehingga dapat memproses data yang besar dengan lebih efisien dan cepat [13].…”

Section: Pendahuluanunclassified

“…Beberapa penelitian terdahulu yang terkait dengan analisis FP-Growth adalah Market Basket Analysis untuk promosi penjualan buku menggunakan algoritma FP-Growth [13], analisa data minng menggunakan Frequent Pattern Growth pada transaksi penjualan untuk rekomendasi strategi pemasaran produk internet [14], implementasi algoritma K-Medoids dan FP-Growth untuk grouping dan penawaran rekomendasi produk [15], implementasi algoritma Frequent Pattern Growth untuk menentukan asosiasi antar produk pada Nadiamart [16] dan penerapan data mining menggunakan algoritma FP-Tree dan FP-Growth pada data transaksi penjualan obat [17]. Hasil dari semua penelitian terdahulu adalah algoritma FP-Growth dapat diterapkan untuk menemukan kombinasi itemset yang sering terjual, sehingga dapat digunakan untuk membuat promosi paket penjualan yang tepat.…”

Section: Pendahuluanunclassified

“…Algoritma FP-Growth merupakan algoritma pencarian frequent itemset (kombinasi barang) yang efisien karena menggunakan struktur data tree (FP -Tree). Algoritma FP-Growth menentukan frequent itemset yang berakhiran suffix tertentu dengan menggunakan metode divide dan conquer untuk memecah problem menjadi subproblem yang lebih kecil [13]. Metode FP Growth dapat dibagi menjadi 3 tahapan utama yaitu sebagai [14]: 1.…”

Section: Algoritma Fp-growthunclassified

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Penerapan Data Mining Untuk Pembuatan Paket Promosi Penjualan Menggunakan Kombinasi Fp-Tree Dan Tid-List

et al. 2021

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The manual preparation strategy of a promosi package can encounter difficulties in determining the right product to be promosited caused of no sales data analysis and the large size of sales database.The solution to this problem is to apply data mining science to find buying patterns that consumers often make in a collection of sales transactions, so that company can create the right promosi packages to encourage increased sales turnover. The study scanned the sales database obtained from the UCI maching learning repository and intelligent system dataset with 12,224 sales transaction records and 126,898 record of goods sold. Furthermore, the data mining process carries out the process of constructing an FP-Tree tree structure, constructing a conditional FP-Tree and TID List, then extracting a combination of items and calculating support (S), confidence (C) and sorting association rules based on the value of S x C descending, from the highest to the lowest value. The combination of FP-Tree and TID-List algorithms can be used to help formulate promosi package strategies, by analyzing the sales database and finding the most frequently sold combinations of items.

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“…The stages in KDD (Suad A. & Wesam S., 2017) begin with Data Selection from raw data where selecting and dividing data to be used and not used (Chen et al, 2015); Preprocessing data with data cleaning techniques and data integration (L. W. Santoso & Yulia, 2019); Data Transformation, where raw data is converted into datasets that can be analyzed, such as smoothing (clustering, regression and binning), aggregation (by calculating the summarize value), generalization and normalization (Firmansyah & Yulianto, 2021); Data Mining, where the transformed data is analyzed and extracted using analytical techniques contained in data mining, so as to produce new information/knowledge; and Interpretation/ Evaluation. The KDD process can be seen in Figure 1 The use of data mining in the analysis of data generated from business processes has a significant impact in determining the direction of company policies (Plotnikova et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Sales Strategy by Determining Tenant Position at Sriwijaya State Polytechnic Through Association Rules Analysis

Isa

2022

ijebd

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Purpose: The purpose of this research is to optimize the usefulness of sales transaction reports in Sriwijaya State Polytechnic (Polsri) foodcourt and extract sales transaction report data to become the knowledge that can be used in sales strategies that is recommendations for tenant positions in accordance with customer behavior by using association rules analysis. Design/methodology/approach: The approach using Knowledge Discovery in Database through stages (1) Data Selection; (2) Data Classification; (3) Raw Itemset Table; (4) Data Mining Process using Association Rules Analysis; (5) Analysis Result Interpretation; (6) Marketing Strategy Recommendation. Findings: The results of the study are recommendations for tenant positions in accordance with customer behavior tendencies Research limitations/implications: Polsri foodcourt sales transaction report used in the period 01 May 2019 – 31 July 2019, with a total of 153,401 data records, data analysis using Association Rules Analysis with A Priori Algorithm, tools used are Microsoft Excel and WeKa 3.6.10 Practical implications: The recommended tenant position can be used as a sales strategy that makes it easier for customers to choose the product to be purchased and increase sales rate Originality/value: The paper is original Paper type: Research paper

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Market Basket Analysis for Books Sales Promotion using FP Growth Algorithm, Case Study : Gramedia Matraman Jakarta

Cited by 11 publications

References 10 publications

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

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

Penerapan Data Mining Untuk Pembuatan Paket Promosi Penjualan Menggunakan Kombinasi Fp-Tree Dan Tid-List

Sales Strategy by Determining Tenant Position at Sriwijaya State Polytechnic Through Association Rules Analysis

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