Penerapan Market Basket Analysis dengan Menggunakan Metode Association Rule Untuk Pengenalan  Pola Perilaku Konsumen

Raudhah, Raudhah; Muhammad, Iman; Ramadhany, Sri

doi:10.30865/jurikom.v9i6.5221

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Penerapan algoritma Apriori [2] didorong oleh kebutuhan akan pendekatan yang lebih sistematis dan akurat dalam menganalisis data transaksi dan mengidentifikasi pola [3][4][5][6] preferensi pelanggan. Dengan memanfaatkan kekuatan data mining [7][8], penelitian ini berusaha untuk meningkatkan proses pengambilan keputusan bagi toko suku cadang [2], memungkinkan mereka untuk proaktif memenuhi kebutuhan pelanggan dan mengoptimalkan strategi manajemen inventaris mereka.…”

Section: Pendahuluanunclassified

Implementation of Apriori Algorithm for Determining Spare Parts Product Recommendation Packages

Alhillah,

Priatna,

Fitriyani

2023

JAIC

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The aim of this research is to determine recommended product packages for spare parts from an automotive parts supplier. Shop owners have faced challenges in meeting customer demands over the past few months, experiencing frequent stockouts of spare parts due to a manual transaction recording system and a manual checking system for spare parts storage. This inefficiency and lack of accuracy in managing in-demand spare parts prompted the application of the apriori algorithm, a data mining method. Data was collected from the total sales over the past three months, subsequently cleaned and transformed for manual and Python-based apriori calculations. The results, obtained through both manual and Python implementations of apriori, indicate that the two frequently occurring item sets are oil filters with a confidence value of 68% and air filters with a confidence value of 63%. Based on these findings, the study recommends spare parts stores to maintain higher stock levels of oil filters and air filters compared to other spare parts.

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Section: Pendahuluanunclassified

Implementation of Apriori Algorithm for Determining Spare Parts Product Recommendation Packages

Alhillah,

Priatna,

Fitriyani

2023

JAIC

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“…b. Pembentukan Aturan Asosiatif Setelah pola max frequent didapati, tahap berikutnya adalah menelusuri asosiasi rule yang mencukupi syarat minimal untuk tingkat kepercayaan (confidence) yang ditetapkan. Dengan menjumlahkan bobot confidence dari aturan asosiatif A∪B, yang didapat dari rumus (Raudhah et al, 2022):…”

Section: Metode Market Basket Analysisunclassified

Analisis Data Penjualan Pada Supermarket Xyz Menggunakan Metode Market Basket

Akbar,

Izzulhaq,

Nursabila

et al. 2023

j.sist.inf.inform.

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The economic growth in Indonesia has increased significantly, positioning Indonesia as the 4th largest economy after China, India, and Malaysia. This is also accompanied by national retail sales reaching a total of Rp6,044 trillion. One example of a retail business in Indonesia is Supermarket XYZ, which operates in Indonesia and sells essential items such as sugar, eggs, butter, and others. This research discusses an issue that exists in Indonesia, for example, Supermarket XYZ. The supermarket faces a problem with its employees struggling to determine the appropriate product layout based on consumer purchasing behavior. The market basket analysis method is used in this research to assist Supermarket XYZ's employees in determining the product layout. Transaction data is collected through internet searches, specifically on the Kaggle website. The data is then processed using the market basket analysis method, both manually and with the aid of the RapidMiner tool, to ensure the validity of the calculations. Once obtained, the results are implemented into a program to provide predictions for product placement layout. The research findings show that out of the 10 most purchased products by consumers, the top four products are chocolate, apples, bread, and ice cream. Furthermore, the suitable product combinations for layout placement yield 3 combinations: chocolate placed near apples with a support rate of 23%, chocolate placed near bread with a support rate of 22%, and chocolate placed near ice cream with a support rate of 21%.

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Penerapan Market Basket Analysis dengan Menggunakan Metode Association Rule Untuk Pengenalan Pola Perilaku Konsumen

Cited by 2 publications

References 12 publications

Implementation of Apriori Algorithm for Determining Spare Parts Product Recommendation Packages

Implementation of Apriori Algorithm for Determining Spare Parts Product Recommendation Packages

Analisis Data Penjualan Pada Supermarket Xyz Menggunakan Metode Market Basket

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