Looking for Transaction Data Pattern Using Apriori Algorithm with Association Rule Method

The sustainability of a company will not be separated from the role of consumers in conducting transactions. In fact, a consumer has different behaviour and character, therefore as a company owner must be able to analyze the patterns or habits of consumers in making transactions. This also happens in the retail center X, which has problems in the sales process, such as products running out of stock and unsold products and the most popular products and products that are not in demand by consumers. Therefore we need an analysis of consumer habits in conducting transactions. The method of association rule with Apriori algorithm is able to be applied well in the analysis of the habits of consumer transactions in the central retail X. The results of the calculation obtained an average percentage of the value of support 33%-40% and the value of confidence 43%-80%. The results of applying the association rule method with Apriori algorithm can help recommend central retail X owners in structuring product and determine strategic steps in increasing sales, such as providing discounts or promos for certain products.

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Implementasi Algoritma Apriori Pada Penyusunan Menu Makanan Rumah Makan Prasmanan

Jurnal Teknik Informatika dan Sistem Informasi ◽

10.28932/jutisi.v6i2.2742 ◽

2020 ◽

Vol 6 (2) ◽

Author(s):

Asep Budiman Kusdinar ◽

Daris Riyadi ◽

Asriyanik Asriyanik

Keyword(s):

Data Mining ◽

Association Rules ◽

Association Rule ◽

High Frequency ◽

A Priori ◽

Apriori Algorithm ◽

Frequency Pattern ◽

Data Mining Techniques ◽

Rule Method

A buffet restaurant is a restaurant that provides buffet food that is served directly at the dining table so that customers can order more food according to their needs. This study uses the association rule method which is one of the methods of data mining and a priori algorithms. Data mining is the process of discovering patterns or rules in data, in which the process must be automatic or semi-automatic. Association rules are one of the techniques of data mining that is used to look for relationships between items in a dataset. While the apriori algorithm is a very well-known algorithm for finding high-frequency patterns, this a priori algorithm is a type of association rule in data mining. High- frequency patterns are patterns of items in the database that have frequencies or support. This high-frequency pattern is used to develop rules and also some other data mining techniques. The composition of the food menu in the Asgar restaurant is now arranged randomly without being prepared on the food menu between one another. The result of this research is to support the composition of the food menu at the Asgar restaurant so that it is easier to take food menu with one another.

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

JURNAL PENDIDIKAN TEKNOLOGI KEJURUAN ◽

10.24036/jptk.v3i1.6923 ◽

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.

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Penggunaan Association Rule Data Mining Untuk Menentukan Pola Lama Studi Mahasiswa F-MIPA UNSRAT

d'CARTESIAN ◽

10.35799/dc.3.1.2014.3777 ◽

2014 ◽

Vol 3 (1) ◽

pp. 1

Author(s):

M. Zainal Mahmudin ◽

Altien Rindengan ◽

Winsy Weku

Keyword(s):

Data Mining ◽

Association Rule ◽

Large Data ◽

Apriori Algorithm ◽

Data Mining Algorithms ◽

Adequate Information ◽

Processing Data ◽

Rule Method ◽

Mining Algorithms ◽

Confidence Value

Abstract The requirement of highest information sometimes is not balance with the provision of adequate information, so that the information must be re-excavated in large data. By using the technique of association rule we can obtain information from large data such as the college data. The purposes of this research is to determine the patterns of study from student in F-MIPA UNSRAT by using association rule method of data mining algorithms and to compare in the apriori method and a hash-based algorithms. The major’s student data of F-MIPA UNSRAT as a data were processed by association rule method of data mining with the apriori algorithm and a hash-based algorithm by using support and confidance at least 1 %. The results of processing data with apriori algorithms was same with the processing results of hash-based algorithms is as much as 49 combinations of 2-itemset. The pattern that formed between 7,5% of graduates from mathematics major that studied for more 5 years with confidence value is 38,5%. Keywords: Apriori algorithm, hash-based algorithm, association rule, data mining. Abstrak Kebutuhan informasi yang sangat tinggi terkadang tidak diimbangi dengan pemberian informasi yang memadai, sehingga informasi tersebut harus kembali digali dalam data yang besar. Dengan menggunakan teknik association rule kita dapat memperoleh informasi dari data yang besar seperti data yang ada di perguruan tinggi. Tujuan penelitian ini adalah menentukan pola lama studi mahasiswa F-MIPA UNSRAT dengan menggunakan metode association rule data mining serta membandingkan algoritma apriori dan algoritma hash-based. Data yang digunakan adalah data induk mahasiswa F-MIPA UNSRAT yang diolah menggunakan teknik association rule data mining dengan algoritma apriori dan algoritma hash-based dengan minimum support 1% dan minimum confidance 1%. Hasil pengolahan data dengan algoritma apriori sama dengan hasil pengolahan data dengan algoritma hash-based yaitu sebanyak 49 kombinasi 2-itemset. Pola yang terbentuk antara lain 7,5% lulusan yang berasal dari jurusan matematika menempuh studi selama lebih dari 5 tahun dengan nilai confidence 38,5%. Kata kunci : Association rule data mining, algoritma apriori, algoritma hash-based

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Market Basket Analysis to Identify Customer Behaviours by Way of Transaction Data

Knowledge Engineering and Data Science ◽

10.17977/um018v1i12018p20-25 ◽

2017 ◽

Vol 1 (1) ◽

pp. 20 ◽

Cited By ~ 1

Author(s):

Fachrul Kurniawan ◽

Binti Umayah ◽

Jihad Hammad ◽

Supeno Mardi Susiki Nugroho ◽

Mochammad Hariadi

Keyword(s):

Association Rule ◽

Market Basket Analysis ◽

Data Mining Technique ◽

Market Basket ◽

Transaction Data ◽

New Information ◽

Analysis Application ◽

Rule Method ◽

Confidence Value

Transaction data is a set of recording data result in connections with sales-purchase activities at a particular company. In these recent years, transaction data have been prevalently used as research objects in means of discovering new information. One of the possible attempts is to design an application that can be used to analyze the existing transaction data. That application has the quality of market basket analysis. In addition, the application is designed to be desktop-based whose components are able to process as well as re-log the existing transaction data. The used method in designing this application is by way of following the existing steps on data mining technique. The trial result showed that the development and the implementation of market basket analysis application through association rule method using apriori algorithm could work well. With the means of confidence value of 46.69% and support value of 1.78%, and the amount of the generated rule was 30 rules.

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Market Basket Analysis with Apriori Algorithm and Frequent Pattern Growth (Fp-Growth) on Outdoor Product Sales Data

International Journal of Educational Research & Social Sciences ◽

10.51601/ijersc.v2i1.45 ◽

2021 ◽

Vol 2 (1) ◽

pp. 132-139

Author(s):

Wiwit Pura Nurmayanti ◽

Hanipar Mahyulis Sastriana ◽

Abdul Rahim ◽

Muhammad Gazali ◽

Ristu Haiban Hirzi ◽

...

Keyword(s):

Association Rules ◽

Association Rule ◽

Frequent Pattern ◽

Apriori Algorithm ◽

Market Basket Analysis ◽

Market Basket ◽

Transaction Data ◽

Pattern Growth ◽

Product Sales ◽

The Relationship

Indonesia is an equatorial country that has abundant natural wealth from the seabed to the top of the mountains, the beauty of the country of Indonesia also lies in the mountains that it has in various provinces, for example in the province of West Nusa Tenggara known for its beautiful mountain, namely Rinjani. The increase in outdoor activities has attracted many people to open outdoor shops in the West Nusa Tenggara region. Sales transaction data in outdoor stores can be processed into information that can be profitable for the store itself. Using a market basket analysis method to see the association (rules) between a number of sales attributes. The purpose of this study is to determine the pattern of relationships in the transactions that occur. The data used is the transaction data of outdoor goods. The analysis used is the Association Rules with the Apriori algorithm and the frequent pattern growth (FP-growth) algorithm. The results of this study are formed 10 rules in the Apriori algorithm and 4 rules in the FP-Growth algorithm. The relationship pattern or association rule that is formed is in the item "if a consumer buys a portable stove, it is possible that portable gas will also be purchased" at the strength level of the rules with a minimum support of 0.296 and confidence 0.774 at Apriori and 0.296 and 0.750 at FP-Growth.

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

JURNAL TEKNOLOGI DAN OPEN SOURCE ◽

10.36378/jtos.v2i2.348 ◽

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%.

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IMPLEMENTASI METODE MARKET BASKET ANALYSIS (MBA) MENGGUNAKAN ALGORITMA APRIORI DALAM TRANSAKSI PENJUALAN (STUDI KASUS: KAFE RUANG TEMU)

Jurnal SAINTEKOM ◽

10.33020/saintekom.v10i2.137 ◽

2020 ◽

Vol 10 (2) ◽

pp. 138

Author(s):

Muhammad SyahruRomadhon ◽

Achmad Kodar

Keyword(s):

Data Mining ◽

Association Rules ◽

Association Rule ◽

Apriori Algorithm ◽

Market Basket Analysis ◽

Tourist Attractions ◽

Market Basket ◽

Transaction Data ◽

Data Mining Techniques ◽

Minimum Support

Jakarta is one of the culinary attractions, many tourist attractions every year become creative in business. One of them is a cafe. Cafe Ruang Temu has sales transaction data but is not used to see associations between one product and another. In this case there needs to be a system for finding menu combinations by processing sales transactions. One of the data mining techniques is association rule or Market Basket Analysis (MBA) with apriori algorithm. Apriori algorithm aims to produce association rules to form menu combinations. The sales dataset for January 2019 to July 2019 is determined by the minimum support and minimum confidence values that have been set.

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Application of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom

Brilliance: Research of Artificial Intelligence ◽

10.47709/brilliance.v1i2.1228 ◽

2021 ◽

Vol 1 (2) ◽

pp. 54-66

Author(s):

M. Hamdani Santoso

Keyword(s):

Data Mining ◽

Association Rule ◽

A Priori ◽

Apriori Algorithm ◽

Rule Discovery ◽

Association Rule Discovery ◽

Rule Method ◽

Confidence Value ◽

Method Show

Data mining can generally be defined as a technique for finding patterns (extraction) or interesting information in large amounts of data that have meaning for decision support. One of the well-known and commonly used association rule discovery data mining methods is the Apriori algorithm. The Association Rule and the Apriori Algorithm are two very prominent algorithms for finding a number of frequently occurring sets of items from transaction data stored in databases. The calculation is done to determine the minimum value of support and minimum confidence that will produce the association rule. The association rule is used to produce the percentage of purchasing activity for an itemset within a certain period of time using the RapidMiner software. The results of the test using the priori algorithm method show that the association rule, that customers often buy toothpaste and detergents that have met the minimum confidence value. By searching for patterns using this a priori algorithm, it is hoped that the resulting information can improve further sales strategies.

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Implementation of Apriori Algorithm for Determining Purchase Patterns in One Transaction

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.33.23560 ◽

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.

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