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 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.  

Analisa Algoritma Apriori Untuk Peminjaman Buku Pada Perpustakaan SMA 1 Silima Pungga-Pungga Parongil

2019 ◽  
Vol 4 (1) ◽  
pp. 154-160
Author(s):  
Oktaviani Manurung ◽  
Penda Sudarto Hasugian
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
High Frequency ◽  
A Priori ◽  
Apriori Algorithm ◽  
Minimum Support ◽  
Minimum Requirements ◽  
To Come

ABSTRACT The library has the role of helping students to love reading books. The availability of books in various fields motivates students to come to visit the library, students can read or borrow library books. For this reason, library officers apply the rules for visiting the library. The Apriori algorithm is a part of data mining, namely the search for high frequency patterns such as activities that often appear simultaneously. The pattern that will be analyzed is the pattern of borrowing any books that are often borrowed so that librarians know the information of books that are often borrowed. With the application of a priori algorithms, book data is processed to produce a book borrowing pattern. After all the high frequency patterns were found, then association rules were found that met the minimum requirements for associative confidence A → B minimum confidence = 25%. Rules for sequential final association based on minimum support and minimum confidence, if borrowing an IPA, then borrowing MTK Support = 15%, Confidence = 42.8%. Keywords:Patterns of borrow of books, Library, Apriori Algorithms

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.

scholarly journals IMPLEMENTASI ALGORITMA APRIORI TERHADAP DATA PENJUALAN PADA PERUSAHAAN RETAIL

2019 ◽  
Vol 15 (1) ◽  
pp. 85-90 ◽  
Author(s):  
Jordy Lasmana Putra ◽  
Mugi Raharjo ◽  
Tommi Alfian Armawan Sandi ◽  
Ridwan Ridwan ◽  
Rizal Prasetyo
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
A Priori ◽  
Apriori Algorithm ◽  
Business World ◽  
Transaction Data ◽  
Retail Company ◽  
Special Strategy

The development of the business world is increasingly rapid, so it needs a special strategy to increase the turnover of the company, in this case the retail company. In increasing the company's turnover can be done using the Data Mining process, one of which is using apriori algorithm. With a priori algorithm can be found association rules which can later be used as patterns of purchasing goods by consumers, this study uses a repository of 209 records consisting of 23 transactions and 164 attributes. From the results of this study, the goods with the name CREAM CUPID HEART COAT HANGER are the products most often purchased by consumers. By knowing the pattern of purchasing goods by consumers, the company management can increase the company's turnover by referring to the results of processing sales transaction data using a priori algorithm

Research of Association Rules Algorithm Based on Matrix under Cloud Computing

2014 ◽  
Vol 568-570 ◽  
pp. 798-801
Author(s):  
Ye Qing Xiong ◽  
Shu Dong Zhang
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Association Rules ◽  
Apriori Algorithm ◽  
Computing Technology ◽  
Binary Matrix ◽  
Transaction Data ◽  
Big Data Mining ◽  
Parallel Mining

It occurs time and space performance bottlenecks when traditional association rules algorithms are used to big data mining. This paper proposes a parallel algorithm based on matrix under cloud computing to improve Apriori algorithm. The algorithm uses binary matrix to store transaction data, uses matrix "and" operation to replace the connection between itemsets and combines cloud computing technology to implement the parallel mining for frequent itemsets. Under different conditions, the simulation shows it improves the efficiency, solves the performance bottleneck problem and can be widely used in big data mining with strong scalability and stability.

The Application of Apriori Algorithm in Analysis on Admitted Students of Colleges and Universities

2013 ◽  
Vol 321-324 ◽  
pp. 2578-2582
Author(s):  
Qian Zhang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Colleges And Universities ◽  
Apriori Algorithm ◽  
Data Mining Techniques ◽  
Minimum Support ◽  
Sample Data ◽  
Mining Association Rules

This paper examined the application of Apriori algorithm in extracting association rules in data mining by sample data on student enrollments. It studied the data mining techniques for extraction of association rules, analyzed the correlation between specialties and characteristics of admitted students, and evaluated the algorithm for mining association rules, in which the minimum support was 30% and the minimum confidence was 40%.

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 MARKET BASKET ANALYSIS USING THE FP-GROWTH ALGORITHM TO DETERMINE CROSS-SELLING

2021 ◽  
Vol 7 (4) ◽  
pp. 49-54
Author(s):  
Fildzah Zia Ghassani ◽  
Asep Jamaludin ◽  
Agung Susilo Yuda Irawan
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Basic Needs ◽  
Market Basket Analysis ◽  
Rice Cake ◽  
Market Basket ◽  
Transaction Data ◽  
Minimum Support ◽  
A Value ◽  
Association Method

KAOCHEM Sinergi Mandiri Cooperative is a cooperative that provides various kinds of basic needs such as basic foodstuffs that can meet the needs of its members. The cooperative transaction data is only stored as a report. Association rules are a method in data mining that functions to identify items that have a value that is likely to appear simultaneously with other items. One implementation of the association method is Market Basket Analysis. The data used are transaction data for November 2019. Data mining is one of the processes or stages of the KDD method. The data mining process is carried out using the FP-Growth algorithm, which is one of the algorithms for calculating the sets that often appear from data. Researchers analyzed transaction data using the Rapid Miner Studio tools. In the data mining process using FP-Growth the researcher determines a minimum support value of 3% and a minimum confidence of 50%. The association process using these values ​​produces 3 strong rules, namely if ades 350 ml, then fried / lontong with a support value of 0.030 and confidence 0.556 and if fried st, then fried / lontong with a support value of 0.048 and confidence 0.639, and if nasi uduk / bacang , then fried / rice cake with a support value of 0.031 and confidence 0.824. The results of the association rules can be applied using one of the marketing techniques, namely cross-selling to increase the sales of the cooperative.

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.

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

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