scholarly journals Penerapan Data Mining Korelasi Penjualan Spare Part Mobil Menggunakan Metode Algoritma Apriori (Studi Kasus: CV. Citra Kencana Mobil)

2021 ◽  
Vol 1 (2) ◽  
pp. 83-90
Author(s):  
Amenta Ovilianda Br Ginting
Keyword(s):  
Data Mining ◽  
Spare Part ◽  
Spare Parts ◽  
Apriori Algorithm ◽  
Process Information ◽  
Customer Data ◽  
Transaction Data ◽  
Minimum Support ◽  
Confidence Value ◽  
Data Mining Application

By utilizing customer data that has been stored in the database, the management can find out how the current sales system is less efficient, therefore a system is needed to process information data more quickly and accurately in increasing sales of car spare parts using the Data Mining application. The Apriori Algorithm method that works by searching for and finding associated patterns among the products being marketed, so that later it can help companies improve the associated items. And with the sales transaction data, the company can know better how they should increase the spare part stock in the company. From the results of testing the sale of car spare parts with 589 data, it was found that 81 rules were formed and the highest Best Rule was obtained and a minimum support value of 1% and a confidence value of 11% If the type of car is Avanza / Xenia and the brand is Toyota, the spare parts used are filters. Air. With supporting spare parts in the database of 1% and certainty of spare parts of 11.

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%.

scholarly journals PENENTUAN POLA SEKUENSIAL DATA TRANSAKSI PENJUALAN MENGGUNAKAN ALGORITMA SEQUENTIAL PATTREN DISCOVERY USING EQUIVALENT CLASSES (SPADE)

2019 ◽  
Vol 16 (2) ◽  
pp. 271-282
Author(s):  
Diki Andika Saputra ◽  
Eneng Tita Tosida ◽  
Fajar Delli W
Keyword(s):  
Decision Making ◽  
Consumption Patterns ◽  
Sequential Patterns ◽  
Apriori Algorithm ◽  
Customer Data ◽  
Transaction Data ◽  
Minimum Support ◽  
Maximum Value ◽  
Confidence Value ◽  
Maximum Minimum

Transaction data is customer or customer data at a commercial or non-commercial institution that contains the consumer id, transaction time, and transaction items. From transaction data such as supermarket transactions, sequential patterns can be found to determine the interrelationship between items or items. Data if further processed or analyzed will produce information or knowledge that is important and valuable as a support in decision making. This study aims to determine the consumption patterns owned by customers and provide information that can be used in determining the layout of new store shelves. The SPADE algorithm is an algorithm for finding sequential patterns to break down the main problem into sub-problems that can be solved separately. Based on the result obtained it can be concluded that the application of the SPADE algorithm has the highest minimum support value that can still form maximal frequent sequences is 29%. The highest minimum support value of the SPADE algorithm is 0.2% with a maximum minimum confidence value of 81% and the number of rules formed is 1,118 Rule, but confidence is taken 60% up so that there are only 15 Rule. Whereas the Apriori algorithm has the highest minimum support value that can still form the maximum frequent sequences is 25%. The highest minimum support value of the apriori algorithm which can still form a rule is 0.3% with a maximum value of 88% minimum confidence and the number of rules formed as many as 494 Rule, but confidence is taken 60% up so that there are only 29 Rule.

scholarly journals IMPLEMENTASI DATA MINING MENGGUNAKAN METODE ALGORITMA APRIORI PADA TOKO DHURROH ELEKTRONIK

2021 ◽  
Vol 13 (2) ◽  
pp. 67
Author(s):  
Syafrianto Syafrianto ◽  
Durotun Ayniyah
Keyword(s):  
Data Mining ◽  
Apriori Algorithm ◽  
Business World ◽  
Transaction Data ◽  
Sales Data ◽  
Group Data ◽  
Confidence Value

In the business world, every store must of course be able to compete and think about how the store can continue to grow and be able to expand its business scale. In order to increase sales of products sold, business actors must have various strategies. One way is by utilizing all sales transaction data that has occurred in the store itself. Dhurroh Elektronik store is a store that sells various kinds of goods such as cellphone accessories. Management of sales data in this store is still done manually, namely by recording sales data in the sales book or sometimes when serving purchases just remembering it. The obstacle faced is that it is difficult to find out where the goods are not in accordance with the behavior of consumers' habits in buying goods at the same time. Based on the above problems, it is necessary to have a calculation to group data items based on their tendencies that appear together in a transaction with Data Mining calculations using the Apriori Algorithm method. The results of the calculation of the items that are most in-demand are if you buy a headset, you will buy a lamp with a 100% confidence value and 19% support, if you buy a radio you will buy a lamp with a 71% confidence value and 16% support, if you buy a data cable, you will buy a flashlight. with a 71% Confidence value and 16% Support, If you buy a battery, you will buy a Flashlight with a 71% Confidence value and 16% Support. Keywords: Data Mining, Apriori Algorithm..

Rancang Bangun Inventarisasi Barang menggunakan Algoritma Apriori Sebagai Data Mining

2021 ◽  
Vol 5 (4) ◽  
pp. 354
Author(s):  
Aditya Prasetya ◽  
Septi Andriana ◽  
Ratih Titi Komalasari
Keyword(s):  
Data Mining ◽  
Programming Language ◽  
A Priori ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Important Thing ◽  
Minimum Support ◽  
Manual Method ◽  
The Times ◽  
Modern Era

Inventory activities become an important thing for business progress, along with the times, inventory activities become easier due to the large number of facilities and infrastructure to support activities, including the Ap Jaya Store which also competes in the modern era, but currently, inventory activities in stores Ap Jaya still uses the manual method, namely by recording inventory activities using a book then recapitulating one by one so that it takes a lot of time, along with these problems an inventory application is needed that can be used to support these activities, this inventory application is made using the a priori algorithm method as data mining and using the programming language PHP and MySQL as a database besides that the a priori algorithm can also be used for item recommendation systems, on testing with 20 transaction data with a minimum support value = 20% and a minimum confidence = 70% also from the results of the transaction. Tests carried out using the apriori algorithm and using applications that are made get the same results according to the requirements for support and confidence values.Keywords:Inventory, Data Mining, Apriori Algorithm

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

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.  

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 Analisa Data Mining Terhadap Penjualan Food Dengan Metode Apriori Pada Kopsyahira

2020 ◽  
Vol 8 (1) ◽  
pp. 44-48
Author(s):  
Agung Riyanto ◽  
Melan Susanti
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Limiting Factor ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Minimum Support ◽  
To Come ◽  
Food Sales

Every company engaged in trade must have a strategy to improve service. Some of them regulate the arrangement of goods (display) or make the appearance of a store look attractive and make shopping easier so that consumers are willing to come back to shop. Many transactions every day but are still done manually. So there might be a lot of errors and inaccurate reports. The number of transactions is also only used as a document. It is not possible for many transaction data to be lost or tucked away. Collection of transaction data if left alone for months, then the data will only be meaningless data and will be a limiting factor in improving services. Purchases are often done simultaneously at one time, so there is a queue in the store. In Kopsyahira there were also several obstacles in terms of sales, especially food sales. In this study, researchers will use the Apriori algorithm, the author uses Tanagra's data mining software. The results of this study produce 2 final association rules if using a minimum support of 30% and Confidence of 66%.

scholarly journals Penerapan Data Mining Untuk Analisis Daftar Pembelian Konsumen Dengan Menggunakan Algoritma Apriori Pada Transaksi Penjualan Toko Bangunan MDN

2021 ◽  
Vol 2 (2) ◽  
pp. 89-101
Author(s):  
Edo Tachi Naldy ◽  
Andri Andri
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Building Material ◽  
Apriori Algorithm ◽  
Processing Methods ◽  
Data Reporting ◽  
Transaction Data ◽  
Minimum Support ◽  
Minimum Value

Everyday the MDN Building Shop has sales transactions but these transactions are only used as data reporting, MDN Building Stores do not manage sales transaction data and analyze a relationship between building material products purchased by consumers in the future. The purpose of this study is to process sales transaction data from consumer purchases by utilizing the Apriori Algorithm, one of the data mining processing methods. From the Apriori algorithm that will be used, it will find an association rule by finding the minimum value of support and confidence. The final result is that if the minimum support value is 50% and the minimum trust is 90%, then 10 patterns of consumer purchase transactions are obtained with 100% confidence. From the association rules, it was found that the transactions that occurred were the purchase of Knie In Grest, Tee in grest, gelam 10 x 12, thinner bottles, knie grest 3 in, waving aw pipes, speck gloves, 3 mm polywood, and 1 nail in a keris.

Penerapan Metode Klasifikasi Decision Tree dan Algoritma C4.5 dalam Memprediksi Kriteria Nasabah Kredit Mega Auto Finance

2020 ◽  
Vol 7 (2) ◽  
pp. 200
Author(s):  
Puji Santoso ◽  
Rudy Setiawan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Microsoft Excel ◽  
Customer Data ◽  
Data Mining Techniques ◽  
C4.5 Algorithm ◽  
Marketing Costs ◽  
Excel Format ◽  
Data Mining Application

One of the tasks in the field of marketing finance is to analyze customer data to find out which customers have the potential to do credit again. The method used to analyze customer data is by classifying all customers who have completed their credit installments into marketing targets, so this method causes high operational marketing costs. Therefore this research was conducted to help solve the above problems by designing a data mining application that serves to predict the criteria of credit customers with the potential to lend (credit) to Mega Auto Finance. The Mega Auto finance Fund Section located in Kotim Regency is a place chosen by researchers as a case study, assuming the Mega Auto finance Fund Section has experienced the same problems as described above. Data mining techniques that are applied to the application built is a classification while the classification method used is the Decision Tree (decision tree). While the algorithm used as a decision tree forming algorithm is the C4.5 Algorithm. The data processed in this study is the installment data of Mega Auto finance loan customers in July 2018 in Microsoft Excel format. The results of this study are an application that can facilitate the Mega Auto finance Funds Section in obtaining credit marketing targets in the future

scholarly journals Implementasi Data Mining Untuk Menentukan Pola Penyebab Kecelakaan Lalu Lintas Di Wilayah Kota Palembang Menggunakan Algoritma FP-Growth

2020 ◽  
Vol 1 (2) ◽  
pp. 97-109
Author(s):  
Fathan Pangestu ◽  
Andri Andri
Keyword(s):  
Data Mining ◽  
Traffic Accidents ◽  
Weather Conditions ◽  
Minimum Support ◽  
City Area ◽  
Road Geometry ◽  
Sunny Weather ◽  
Motorized Vehicles ◽  
The City ◽  
Confidence Value

Palembang City is one of the big cities in Indonesia. Along with the increasing population and the increasing number of motorized vehicles, it will certainly have an impact on the increasing number of traffic accidents in the city of Palembang. In this study, the writer will determine the pattern of traffic accidents by using the fp-growth algorithm and using various variables. The variables that will be used consist of weather, time of incident, road geometry, profession, level of injury. This research is expected to be a reference for the police to be able to take anticipatory measures in order to reduce the number of traffic accidents in the Palembang City area. The fp-growth algorithm can be applied properly to determine the pattern of the causes of traffic accidents in the city of Palembang by using 2 minimum support of 40% and 50% and 2 minimum confidence of 70% and 90%. Based on the resulting rules, there are rules with the highest confidence value of 98% with these rules: When an accident occurs with a Side-Side accident type, the accident occurs in sunny weather conditions

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