Penerapan Metode Data Mining Terhadap Data Transaksi Penjualan Menggunakan Algoritma Apriori

Erfina, Adhitia; Melawati,; Arianti, Nunik Destria

doi:10.52005/jursistekni.v2i3.62

Cited by 3 publications

(5 citation statements)

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“…In this research, after the data was obtained, the data was processed using the a priori method in data mining. The a priori method is a method with an association model that can be used to determine a pattern of opportunities to buy or take an object or item (Erfina, Melawati, & Destria Arianti, 2020) (Agustiani, Suhendro, Saputra, & Tunas Bangsa Pematangsiantar, 2020) . As in research conducted by (Agustiani et al, 2020) the a priori method can be used to make sales associations for goods and has good accuracy (Atadjawa, Haryanti, & Kurniawati, 2021) (Erfina et al, 2020) (Adha & Utami, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The a priori method is a method with an association model that can be used to determine a pattern of opportunities to buy or take an object or item (Erfina, Melawati, & Destria Arianti, 2020) (Agustiani, Suhendro, Saputra, & Tunas Bangsa Pematangsiantar, 2020) . As in research conducted by (Agustiani et al, 2020) the a priori method can be used to make sales associations for goods and has good accuracy (Atadjawa, Haryanti, & Kurniawati, 2021) (Erfina et al, 2020) (Adha & Utami, 2022). Not only that, the a priori method can also determine the sales pattern of an item and this method can also be used to determine consumer purchasing patterns (Syahputri, 2020) .…”

Section: Literature Reviewmentioning

confidence: 99%

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Implementation of Data Mining to Determine Sales Patterns Using the Apriori Method

Ritonga,

Juledi,

Mutia

2024

SinkrOn

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Research on the Implementation of Data Mining to Determine Sales Patterns Using the Apriori Method is an effort to understand and utilize sales data in making more informed and strategic business decisions. The main goal of this research is to extract hidden patterns from large sales data sets, which cannot be discovered by manual analysis alone. This research process is divided into several key stages, namely Data Selection, Preprocessing, Transformation, and Data Mining. The research results show that the Apriori method is effective in finding purchasing patterns. In terms of the frequency of 2 itemsets, the highest support value was found to be 1, which indicates that the combination of the two products is always purchased together in all transactions. For 3 itemsets and 4 itemsets, the high support value of 0.9 also indicates the existence of product combinations that are often purchased together. In terms of confidence, 2 itemsets show the highest value of 1.25, indicating that purchasing one product has a high tendency to be followed by purchasing other products. For 3 itemsets and 4 itemsets, the confidence values show a slightly lower trend but are still significant. Furthermore, lift analysis provides additional insight into the strength of association between itemsets, with 4 itemsets showing the highest lift value of 1.30, indicating the product combination has a very strong association compared to random expectations. This research confirms the potential of the Apriori method in finding valuable sales patterns, which can help companies make strategic decisions for increasing sales and customer satisfaction.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Implementation of Data Mining to Determine Sales Patterns Using the Apriori Method

Ritonga,

Juledi,

Mutia

2024

SinkrOn

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“…tidak terkecuali dalam proses ini harga, pengiriman dan dalam bisnis yang menggunakan teknologi untuk mempermudah proses penjualan atau menjangkau pelanggan. Penjualan adalah kegiatan bisnis yang membawa pendapatan utama bagi perusahaan besar dan kecil [1].…”

Section: Pendahuluanunclassified

“…E-commerce adalah suatu wadah atau tempat dimana pembeli dan penjual saling melakukan transaksi berbagai jenis barang dengan menggunakan teknologi, dan dengan bantuan komputer, laptop atau handphone, tanpa mempertemukan pembeli dan penjual [1][2] [3].…”

Section: Pendahuluanunclassified

Penerapan Algoritma Apriori Pada E-commerce Elektronik

Iza,

Nazir,

Iskandar

et al. 2023

JoSYC

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Because there are so many advantages to using e-commerce, it is now expanding quickly. E-commerce, particularly for electronic items, makes it simpler for customers to execute transactions without traveling. Because businesses (business actors) do not yet have a pattern and strategy for the products they sell, the use of e-commerce has not yet reached its full potential. As a result, sales occasionally suffer because the supply of products does not meet consumer needs, forcing consumers to leave without purchasing these products, which has an impact on transactions. sales firm. Businesses (businesspeople) must use data mining to implement data processing. For this reason, researchers use an application strategy that is appropriate in this situation: the a priori algorithm. Finding frequent itemsets that frequently show up in the data set with the strongest pattern is frequently done using the a priori algorithm. This algorithm's output can be used to assist management in making decisions. According to the study's findings, the rule "if you buy AA Batteries (4-pack), you will buy AAA Batteries (4-pack), "if you buy AA Batteries (4-pack), you will buy a USB-C Charging Cable," and "if you buy AA Batteries (4-pack) and AAA Batteries (4-pack), you will buy a USB-C Charging Cable" all have a support and confidence value of 100%.

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“…Sales transaction data has a large amount of data but does not have a significant increase in information. Several studies have been conducted in terms of data mining in sales data, such as data mining in grocery stores using the Apriori algorithm [1], data mining on sales transaction data at the Fasentro Fancy store [2], and the application of Data Mining on Goods Sales Transactions at BE-MART Store [3]. These studies used the Apriori algorithm, which used association rules that were very suitable for mining transaction data.…”

Section: Introductionmentioning

confidence: 99%

Associative Classification with Classification Based Association (CBA) Algorithm on Transaction Data with Rshiny

Frederika,

Bayupati,

Buana

2023

LKJITI

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Data mining can be used for businesses with large amounts of data. One of the data mining techniques is Associative Classification. It is a new strategy in data processing that combines association and classification techniques to build a classification model. This research used an associative classification technique on sales transaction data of Frozen Food Stores, which had sales transaction data on their business activities. It would be used in sales strategies to find items often purchased by class customers, namely, members and general. This research aimed to classify based on association rules using the CBA (Classification based Association) algorithm on sales transaction data. The application used the R programming language that business owners could use. The results of the rules obtained from the trial had the value of support, confidence, coverage, and lift ratio, which were the best value levels of a rule. The results of the rules that had the highest lift ratio value from all the data that have been inputted can be used as a reference to be implemented in sales strategies in knowing consumer needs.

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Penerapan Metode Data Mining Terhadap Data Transaksi Penjualan Menggunakan Algoritma Apriori

Cited by 3 publications

References 0 publications

Implementation of Data Mining to Determine Sales Patterns Using the Apriori Method

Implementation of Data Mining to Determine Sales Patterns Using the Apriori Method

Penerapan Algoritma Apriori Pada E-commerce Elektronik

Associative Classification with Classification Based Association (CBA) Algorithm on Transaction Data with Rshiny

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