Marketing Strategy for the Determination of Staple Consumer Products Using Fp-Growth and Apriori Algorithm

Ariestya, Winda Widya; Supriyatin, Wahyu; Astuti, Ida

doi:10.35760/eb.2019.v24i3.2229

Cited by 13 publications

(9 citation statements)

References 5 publications

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“…The experimental results show that this method is used in data processing. It has good application performance and effectively provides a supporting basis for the formulation of consumer marketing strategies [20].…”

Section: Related Workmentioning

confidence: 99%

Fault Diagnosis Technology of Railway Signal Equipment based on Improved FP-Growth Algorithm

Yang¹

2022

IJACSA

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The rapid development of computer information technology has made various fault diagnosis and detection technologies emerge in an endless stream. As one of the main transportation vehicles, the detection efficiency of fault diagnosis of railway signal equipment has important practical significance for maintaining the overall safe operation of railways. On the basis of the traditional FP-Growth algorithm, improve the TF-IDF algorithm to realize the weight discretization of text features, and realize the improvement of the FP-Growth algorithm by adjusting the adaptive confidence and support. The FP-Growth algorithm will be improved. FP-Growth algorithm is used for performance tests and applications. The results show that the minimum running time-saving of the proposed algorithm is 1500ms, and the average accuracy of P@N exceeds 85%, which is higher than that of the FP-Growth algorithm (81.4%) and VSM algorithm (82.1%). The PR curve of the improved algorithm is closer to the upper right, which effectively ensures the processing of correlated data, and the overall average precision performance under the influence of positive and negative signal-to-noise ratio values exceeds 95%. And the signal curve generated by the algorithm. The error range of the data under the four fault types of track circuit, turnout, signal, and connecting line floats between 1% and 5%. The improved FP-Growth algorithm can effectively analyze railway fault types and data. Perform analysis and data processing to minimize diagnostic errors.

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Section: Related Workmentioning

confidence: 99%

Fault Diagnosis Technology of Railway Signal Equipment based on Improved FP-Growth Algorithm

Yang¹

2022

IJACSA

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“…This method is often also called market basket analysis. The data processing method known as Association Rule or ARM (Association Rule Mining) is a single subprocess-based system or technique [12]. Association rule mining is a procedure for looking for relationships between items in a specified data set.…”

Section: B Association Rulementioning

confidence: 99%

Implementation of the FP-Growth Algorithm in Sales Transactions for Menu Package Recommendations at Warung Oemah Tani

Triana

Khoerida

Widiawati

et al. 2022

IOTA

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Along with the rapid development of the culinary industry, business competition is also getting tougher. Warung Oemah Tani serves a variety of menus and drinks, but to provide satisfying service to customers, business people must try to develop new products. Under these circumstances, the menu recommendations for Warung Oemah Tani need to be analyzed so that the recommendations made are right on target. This study aims to analyze the sales of Warung Oemah Tani using the FP Growth algorithm. This algorithm identifies the data set with the highest frequency of concurrent sales (frequent itemset). The results of the association rules show that the highest support value is 0.520 and the highest confidence value is 0.929, with a minimum support of 30% and a minimum confidence of 80%. Obtained 14 rule associations that meet the minimum support and minimum confidence.

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“…MBA sudah banyak diterapkan pada segmen retail (Kurniawan, Umayah, & Hammad, 2018), penerapan pada aplikasi e-commerce (Dai & Zeng, 2016), implementasi di toko retail perkakas (Sagin & Ayvaz, 2018), implementasi pada supermarket grosir (Musungwini, Zhou, Gumbo, & Mzikamwi, 2014), menggabungkan FP-Growth dengan K-Apriori pada penerapan di supermarket (Annie & Kumar, 2012), menganalisis trend dari pasar (M. Kaur & Kang, 2016) dan meningkatkan penjualan produk (Alfianzah, Handayani, & Murniyati, 2020). Untuk menganalisis pola pembelian pelanggan juga telah diimplementasikan menggunakan Apriori (Panjaitan et al, 2019) dan menggabungkan dengan FP-Growth (Ariestya, Supriyatin, & Astuti, 2019). FP-Growth juga digunakan sebagai sistem untuk rekomendasi seperti rekomendasi produk (Dharshinni et al, 2019) bahkan digabungkan ke dalam statistik (Szymkowiak, Klimanek, & Józefowski, 2018).…”

Section: A Penelitian Terkaitunclassified

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

Firmansyah¹,

Agus²

2021

JITE

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For retail companies such as Gramedia stores, promotion and strategies to sell books are important, so tools are needed to analyze past sales data. Gramedia does not yet have tools to analyze shopping cart patterns that aim to carry out product promotions appropriately. To promote what books should be promoted using the market basket analysis method or shopping basket analysis. The algorithm used in the data mining process is Frequent Pattern Growth (FP Growth) because it is faster in processing large data. The data analyzed is historical data on book sales from January to March 2020 which is taken randomly (random sampling). The framework used in the data mining process is the Cross Industry Standard Process for Data Mining (CRISP-DM) and the tool used is the Rapid Miner using a market basket analysis framework. With a minimum support of 0.003 and a minimum confidence 0.3 using the FP-Growth algorithm to produce an item set of 7 rules to recommend product promotions. The algorithm results are also in accordance with the business understanding phase of CRISP-DM.

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Marketing Strategy for the Determination of Staple Consumer Products Using Fp-Growth and Apriori Algorithm

Cited by 13 publications

References 5 publications

Fault Diagnosis Technology of Railway Signal Equipment based on Improved FP-Growth Algorithm

Fault Diagnosis Technology of Railway Signal Equipment based on Improved FP-Growth Algorithm

Implementation of the FP-Growth Algorithm in Sales Transactions for Menu Package Recommendations at Warung Oemah Tani

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

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