scholarly journals APPLICATION OF DATA MINING FOR ANALYSIS OF CONSUMER PURCHASE DATA ON SALES TRANSACTION DATA AT HALAL MART HNI HPAI DHARMASRAYA

2020 ◽  
Vol 3 (1) ◽  
pp. 68-75
Author(s):  
Sri Kurnia Yuliarnis ◽  
Yeka Hendriyani ◽  
Denny Kurniadi ◽  
M. Giatman
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Online Media ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Sales Strategy ◽  
Using Data ◽  
Purchase Patterns ◽  
Product Layout ◽  
Archive Data

The sales strategy determines the continuity of the business being run. The problems that occur are the sales archive data has not been analyzed in-depth, the information system has not been integrated with applications for sales data analysis, online media promotion has not been maximized, inadequate stock of goods, the layout of goods is not optimal, and the combination of the number of products is not optimal. This study aims to extract hidden information in the sales database using Data Mining. From the information generated, sales strategy recommendations are developed relating to promotions, inventory, catalogue design, item layout, and the combination of product quantities. The method used is the association rule with Apriori algorithm to find consumer purchase patterns through the resulting association. The importance of association can be identified by two benchmarks, namely support and confidence. The sales strategy analyzed includes product promotion, catalogue design, product layout, stock predictions, and product combinations for sale. Based on the research produced 7 strong rules which are the highest association rules which are then developed into a sales strategy recommendation.

scholarly journals Analisis Pola Pembelian Obat di Apotek Sekar Adi Menggunakan Metode Algoritma Apriori Depok

2020 ◽  
Vol 4 (1) ◽  
pp. 112
Author(s):  
Siti Awaliyah Rachmah Sutomo ◽  
Frisma Handayanna
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Association Rules ◽  
High Frequency ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Mining Methods ◽  
Using Data ◽  
Purchase Patterns

By using data mining methods can be processed to obtain information and assist in decision making, the amount of data on sales transactions in each drug purchase can cause a data accumulation and various problems, such as drug stock inventory, and sales transaction data, with Data mining techniques, the behavior of consumers in making transactions of drug purchase patterns can be analyzed, It can be known what drugs are commonly purchased by mostly people, the application of Apriori Algorithm is expected to help in forming a combination of itemset. The process of determining drug purchase patterns can be carried out by applying the Appriori algorithm method, determination of drug purchase patterns can be done by looking at the results of the consumer's tendency to buy drugs based on a combination of 3 itemset. By calculating the Analysis of High Frequency Patterns and the Formation of Association Rules, with a minimum of 30% support, there is a combination of 3 itemsset namely MOLAGIT PER TAB (M1), VIT C TABLET (V2), and PARACETAMOL 500 MG TABLET (P2) with 33.33 % support results obtained, and with minimum confidence of 65% there are 6 final association rules.

scholarly journals Combination, Pola, Apriori ANALISIS POLA BELANJA KONSUMEN MENGGUNAKAN ALGORITMA APRIORI PADA RAFFA PHOTOCOPY

2019 ◽  
Vol 2 (2) ◽  
pp. 63-73
Author(s):  
Nurul Azwanti
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Minimum Support ◽  
Association Pattern ◽  
Using Data ◽  
Confidence Value

Raffa Photocopy is a shop that started its business in 2016. This business not only provides photocopy services, but also provides office stationery and school supplies. Every day there are sales transactions where the recording of goods sold has a relationship between one another, because in recording sometimes consumers do not just buy one item, but two items even more as when buying a book, it is likely that consumers also buy a pen. This recording is only stored as an archive by Raffa Photocopy, even though the number of sales transactions that occur every day can lead to a pile of data. One effort to increase sales at Raffa Photocopy can be done by processing transaction data that overlaps by using data mining association techniques. This association rule technique uses the Apriori algorithm which deals with the study of 'what is with what' or discovers the association pattern of items that are often bought. The results of this study in the form of rules include the first, if you buy an eraser, it is likely that consumers also buy notebooks simultaneously. Second, if you buy Tipex, then consumers also buy a double folio. The results of the Apriori algorithm process are based on a minimum support value of 35% and a minimum confidence value of 80%.

Decision Support System using Data Mining Method for a Cross Selling Strategy in Retail Stores

2014 ◽  
Vol 3 (3) ◽  
pp. 171
Author(s):  
Imam Tahyudin ◽  
Mohammad Imron ◽  
Siti Alvi Solikhatin
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Large Data ◽  
Retail Stores ◽  
Mining Method ◽  
Transaction Data ◽  
Sales Strategy ◽  
Using Data ◽  
Retail Company ◽  
Confidence Value

<p>A sales transaction dataof a retail company which is collect edevery day is enormous. Very large data will bemore meaning fultoin crease the company’s profitsif itcanbe extracted properly. Based on the research resultsof Andhika, et al[1], ZhangandRuan[6], Herera et al [7], Witten [11], explained that one of the methods that can gather information from the transaction data is the method of association. With this method it can be determined the patterns of transactions performed simultaneously and repeatedly. Thus, it can be obtained amodel that can be used as a reference for cross selling sales strategy. The purpose of this research is to apply data mining association methods of data mining by using <em>apriori </em>algorithm to create a new sales strategy for cross selling. Based on calculations, Association Rule is implemented by applying Confidence value=0.8while the value of Support=0.1 of the defined minimum value, the total result are 77 rules.</p><p>Keywords: Data Mining, Association, <em>Apriori</em> Algorithm, Cross Selling, Retail Stores</p>

scholarly journals Implementation of Apriori Algorithm for Determining Purchase Patterns in One Transaction

2018 ◽  
Vol 7 (4.33) ◽  
pp. 204
Author(s):  
Murnawan . ◽  
Ardiles Sinaga ◽  
Ucu Nughraha
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Apriori Algorithm ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Transaction Data ◽  
Future Developments ◽  
Operational Activities ◽  
Purchase Patterns

The organization data owned is one of the assets of the organization. With the daily operational activities, the longer the data will increase. By using techniques that can do data processing, these data can be obtained important information that can be used for future developments. Association rules are one of these techniques which aims to find patterns in the form of products that are often purchased together or tend to appear together in a transaction from transaction data which is generally very large by using the concept association rules themselves derived from Market Basket Analysis terminology, namely search for relationships from several products in a purchase transaction. In designing this application will build applications that classify the data items based on the tendency to appear together in a transaction using the Apriori Algorithm. The Apriori algorithm is the first algorithm and is often used to find association rules in data mining applications with association rule techniques. 

scholarly journals The Use of Apriori Algorithm in the Formation of Association Rule at Lotteria Cibubur

SinkrOn ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 76
Author(s):  
Ovi Liansyah ◽  
Henny Destiana
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Association Rules ◽  
Association Rule ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Data Mining Techniques ◽  
Sales Data ◽  
Sales Strategy ◽  
Data Mining Application

Lotteria as one of the franchises that produce sales data every day, has not been able to maximize the utilization of that data. The sale data storage is still not optimal. By utilizing sales transaction data that have been stored in the database, the management can find out the menus purchased simultaneously, using the association rule. Namely, data mining techniques to find the association rules of a combination of items. The process of searching for associations uses the help of apriori algorithms to produce patterns of the combination of items and rules as important knowledge and information from sales transaction data. By using the minimum support parameters, the minimum and the month period of the sales transaction to find the association rules, the data mining application generates association rules between items in April 2019, where consumers who buy hot / ice coffee will then buy float together with support of 16% and 100% confidence. Knowing which menu products or items are the most sold, thus lotteria Cibubur can develop a sales strategy to sell other types of menu products by examining the advantages of the most sold menu with other menus and can increase the stock of menu ingredients.

scholarly journals Sales Transaction Data Analysis using Apriori Algorithm to Determine the Layout of the Goods

2018 ◽  
Vol 1 (1) ◽  
pp. 16-23
Author(s):  
Taqwa Hariguna ◽  
Uswatun Hasanah ◽  
Nindi Nofi Susanti
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Association Rules ◽  
High Frequency ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Sales Strategy ◽  
Processing Data ◽  
Using Data

In a shop, usually apply a sales strategy in order. The sales strategy can be in the form of determining the layout of goods so that they are close to one another. Determining the layout of items can be based on items that are often purchased simultaneously. Searching for items that are often purchased together can be done using data mining techniques, which is processing data to become more useful information. Sales transaction data processing can be done using apriori algorithm. Apriori algorithm is the most famous algorithm for finding high-frequency patterns and generating association rules. From the results of the discussion and data analysis, there were 3 (three) association rules formed, namely "If you buy Milo Active 18 grm, then buy ABC Kopi Susu 31G" with support 0.36% and 75% confidence, "If you buy Dancow 1 + Honey 200 grm, then buy Ice Cream Corneto" wit H Support 0.36% and confidence 60%, "If you buy SIIP Roasted 6.5 grm, then buy Davos Strong 10 grm" with support 0.36% and 75% confidence. From the association's rules can be used as decision making to determine the layout of goods that are likely to be purchased simultaneously by the buyer

scholarly journals PENERAPAN ASSOCIATION RULE DATA MINING UNTUK REKOMENDASI PRODUK KOSMETIK PADA PT. FABIANDO SEJAHTERA MENGGUNAKAN ALGORITMA APRIORI

ALGOR ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Febri Antho ◽  
Dram Renaldi ◽  
Edy ◽  
Yakub
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Association Rule ◽  
Policy Decision ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Cosmetic Product ◽  
Product Recommendations ◽  
Using Data ◽  
Product Sales

In some companies that have sales transaction data and this data will increase from day to day so that it will accumulate and become garbage if it is not managed and utilized properly. Sales transaction data is one thing that can be used to increase product sales. Not only to increase product sales but also to provide product recommendations for each sale. As in the product stock setting section, it can provide recommendations for the number of products so that problems such as over stock will not occur which will cause the amount in a product to expire. In this study, an association rule data mining will be implemented for cosmetic product recommendations using the Apriori algorithm. Testing the results of using data mining and the Apriori algorithm is carried out to find out that the results of the study can find association rules from existing datasets to recommend cosmetic products. The association rule method is used in the search for product attachment patterns for sales strategies in policy decision making. So that it can be seen that the cosmetics that are often purchased by consumers, based on the rules generated from the data contained in the database. Tests were carried out using the Rapidminer 9.5 application. The results obtained from this test are that there are 16 rules (rules) that will be used for decision making in cosmetic product recommendations.

scholarly journals ANALISIS POLA PENJUALAN DENGAN MENGGUNAKAN ALGORITMA APRIORI PADA KOPERASI KARYAWAN YAYASAN ANAKKU

2019 ◽  
Vol 2 (1) ◽  
pp. 31-36
Author(s):  
Arfianto Darmawan ◽  
Titin Kristiana
Keyword(s):  
Data Mining ◽  
Strong Association ◽  
Apriori Algorithm ◽  
Business Unit ◽  
Savings And Loans ◽  
Transaction Data ◽  
Existing Problems ◽  
A Value ◽  
Transaction Database ◽  
Using Data

The Anakku Foundation Cooperative is a multi-business cooperative consisting of shop businesses, savings and loans, and student shuttle services. Every sale of stuff services will be inputted data directly to each business unit. The Anakku Foundation Cooperative still has problems, including store transactions that cannot yet answer what items are often sold, when stock items are still difficult to determine the items that are still available or almost running out. Data mining techniques have been mostly used to overcome existing problems, one of which is the application of the Apriori algorithm to obtain information about the associations between products from a transaction database. Transaction data on school equipment sales at Cooperative Employees of Anakku Foundation can be reprocessed using Data mining applications so as to produce strong association rules between itemset sales of school supplies so that they can provide recommendations for item alignment and simplify the arrangement or strong item placement related to interdependence. The results are found that the highest value of support and confidence is if buying MUSLIM L1.5P1, so it would buy AL-IZHAR II LOGO with a value of 14.5% support and 79.5% confidence

scholarly journals Prediksi Persediaan Perlengkapan Hewan Peliharaan Pada Toko Poopy Cat Store Menggunakan Algoritma Apriori

2021 ◽  
Vol 5 (3) ◽  
pp. 824
Author(s):  
Muliati Badaruddin ◽  
Santoso Santoso
Keyword(s):  
Data Mining ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Mining Methods ◽  
Using Data ◽  
Extract Information

Pets such as tame animals of various types such as cats, dogs, rabbits and others are one of the pleasures for animal lovers in having a desire to meet the needs and protect the animal from everything, difficulty in predicting the tendency of the breed. the goods to be purchased by consumers make shop owners often run out of items that are needed by consumers, this is because buyers do not make transactions and can reduce profit income to the store so it is necessary to extract information on data on buying and selling data or transaction data, in the application of extracting information using data mining methods with the APRIORI algorithm approach which is able to assist in finding out items of pet equipment from the number of sales, the results obtained from using this algorithm show the combination of the most frequent purchases carried out simultaneously on the supply of pet equipment so that it shows items that need to be stocked up more, the results obtained meet the previously set support and confidence values of 25% and 50%, the results obtained by 3 items Bolt 10 gr, Cage, Bowl get the highest value of 65%

scholarly journals Implementation of K-Medoids and FP-Growth Algorithms for Grouping and Product Offering Recommendations

2019 ◽  
Vol 2 (2) ◽  
pp. 107
Author(s):  
Imaduddin Syukra ◽  
Assad Hidayat ◽  
Muhammad Zakiy Fauzi
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Market Basket ◽  
Transaction Data ◽  
A Value ◽  
Using Data ◽  
The Right ◽  
A Company ◽  
Technical Recommendations

212 Mart Rambutan Street on Pekanbaru City is a company engaged in retail. Meeting the needs of consumers and making the right decision in determining the sales strategy is a must. One way to find out market conditions is to observe sales transaction data using data mining. The data mining method commonly used to analyze market basket (Market Basket Analysis) is the Association Rule. The Association Rule can provide product recommendations and promotions, so that the marketing strategy is more targeted and the items promoted are the customer's needs. At 212 Mart, the determination of product promotion is obtained from the analysis of sales transaction data reports, which are based on the most sold products and the expiration date. Often the product being promoted does not fit the customer's needs. The purpose of this study is to apply the K-Medoids algorithm for clustering on FP-Growth in producing product recommendation rules on a large number of datasets so that they can provide technical recommendations / new ways to the 212 Mart in determining product promotions. The results obtained are from the experiments the number of clusters 3 to 9 obtained optimal clusters of 3 clusters based on the validity test of the Davies Bouldin Index with a value of 0.678. With a minimum support value of 5% - 9% and a minimum value of 50% confidence, the result is that the Association Rule is found only in cluster 3 with 5 rules.

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