scholarly journals Kombinasi Transaksi Penjualan (Merk Beras) Menggunakan Algoritma Apriori (Studi Kasus di UD. SRD)

2021 ◽  
Vol 9 (1) ◽  
pp. 45
Author(s):  
Iwan Ady Prabowo ◽  
Wawan Laksito Yuly Saptomo ◽  
Nungky Kurnia Candra
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Apriori Algorithm ◽  
Sales Data ◽  
The Creation ◽  
Mining Association Rule

UD. SRD is an individual company that produces and sells rice with various brands. UD. SRD is an individual company that produces and sells rice with various brands. Rice brands sold include C4 Kelapa, C4 Raja, C4 Merak, Nogo Hitam, Nogo Merah, Nogo Hijau, Pandan Wangi, Mentik Wangi and Rojo Lele. In processing sales data is still done manually that is only using notes or receipts, so the data only serves as an archive. In this study, the data amounted to 404 rice sales transactions from January to September 2016. The purpose of this research is the creation of Apriori algorithm system to predict combination of rice brand in sales transaction in UD. SRD so as to help the company know the sales of the most frequently purchased rice brands simultaneously. The result of combination rice brand is getting using Apriori Algorithm which supported minimum 30% and confidence minimum 70%. It appears from thus statements "If buy C4R, it will buy C3M with 51.54% support and 77.31% confidence", "If buy MW , It will buy C4M with the value of support 48.27% and the value of confidence 79.92% "," If buy C45 and C4M, it will buy MW with the value of support 32.43% and the value of confidence 71.20% "and" If buy MW and C4R , It will buy C4M with the value of support 32.43% and the value of confidence 82.91%".Keywords: Rice Transaction, Selling, Data mining, Association Rule, Apriori Algorithm

scholarly journals Analisa Pola Penjualan Produk Sepeda Motor Yamaha Menggunakan Metode Algoritma Apriori

2021 ◽  
Vol 5 (3) ◽  
pp. 1107
Author(s):  
Siti Nurlela ◽  
Lilyani Asri Utami
Keyword(s):  
Data Mining ◽  
Marketing Strategy ◽  
Automotive Industry ◽  
Association Rule ◽  
The Other ◽  
Apriori Algorithm ◽  
Sales Data ◽  
Competitive Prices ◽  
Rule Analysis ◽  
Strategy Pattern

The development of automotive industry in Indonesia can be classifiedas very rapid and annually increasing, causing highly competitive circumstances because many companies provide various types of motorcycle brands with quality and competitive prices. The company must create a marketing strategy pattern that can increase the level of sales efficiency of Yamaha motorcycle products. To overcome this problem, a strategy that can help increasing sales of motorcycle products is needed, in which by utilizing sales data owned by the company. Data mining can be used to process company sales data by looking for association rules with apriori algorithm on motorcycle product variables. From the results of the association rule analysis on sales data, with a minimum support of 30% and a minimum confidence of 75% can produce 3 rules with 3 products that are most in demand by consumers, namely the NEW MIOM3 CW, NEWAEROX155VVA and N-MAX, by knowing the most selling products, the company can add the most selling product supply and develop a marketing strategy to market the products with other products by examining the comparative advantage of the most sold products over the other products.

scholarly journals Penerapan Data Mining dengan Memanfaatkan Metode Association Rule untuk Promosi Produk

2018 ◽  
Vol 3 (1) ◽  
pp. 89
Author(s):  
Rintho Rante Rerung
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Association Rule ◽  
Frequent Itemset ◽  
The Other ◽  
Apriori Algorithm ◽  
Online Social Media ◽  
Knowing That ◽  
Other Hand ◽  
Mining Association Rule

Dalam suatu bisnis diperlukan upaya memaksimalkan keuntungan diantaranya dengan melakukan promosi. Banyak cara yang bisa dilakukan untuk mempromosikan produk seperti dengan cara online dengan memanfaatkan media sosial Facebook dan situs-situs yang menyediakan iklan. Namun demikian, untuk memperoleh hasil yang maksimal maka perlu dilakukan perhitungan seberapa besar kemungkinan pelanggan akan tertarik terhadap produk yang ditawarkan. Penelitian ini bertujuan untuk menerapkan data mining untuk promosi produk Distro Nasional. Dalam bidang keilmuan data mining, terdapat suatu metode yang dinamakan association rule. Metode ini bertujuan untuk menunjukkan nilai asosiatif antara jenis-jenis produk yang dibeli oleh pelanggan sehingga terlihatlah suatu pola berupa produk apa saja yang sering dibeli oleh palanggan tersebut. Dengan mengetahui jenis produk yang sering dibeli maka dapat dibuat sebagai sebuah dasar keputusan untuk menentukan produk apa saja yang cocok untuk dipromosikan kepada pelanggan tersebut. Algoritma Apriori juga akan dipergunakan untuk menentukan frequent itemset sehingga hasil akhir yang dicapai yaitu untuk menghitung persentase ketertarikan (confindence) pelanggan terhadap produk yang ditawarkan.Kata kunci: promosi, data mining, association rule, produk In a business, there is required efforts to maximize profits include by promotion. Many ways can be conducted in promoting a product such as by using Facebook as an online social media and sites which provide advertisements. On the other hand, in gaining a maximum result is required a calculation about how big customer probability to get interested in a product offered. This study aims to apply data mining for product promotion of Distro Nasional store. In a science of data mining there is a method called association rule. This method was intended to indicate associative values among product types were bought by customers. So that, it can be seen a pattern which types of product that often bought by customers. By knowing that information it can be made as a decission base to determine which appropriate products get promotted to that customer. Apriori algorithm will also be used to determine the frequent itemset so that the final result achieved is to calculate the percentage of customer interest (confindence) on the product offered.Keywords: promotion, data mining, association rule, product 

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.

Visual Data Mining for Discovering Association Rules

2008 ◽  
pp. 2105-2120
Author(s):  
Kesaraporn Techapichetvanich ◽  
Amitava Datta
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Large Data ◽  
Data Sets ◽  
Visual Data Mining ◽  
Useful Knowledge ◽  
Large Databases ◽  
A New Technique ◽  
Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

Visual Data Mining for Discovering Association Rules

2011 ◽  
pp. 209-226
Author(s):  
Kesaraporn Techapichetvanich ◽  
Amitava Datta
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Large Data ◽  
Data Sets ◽  
Visual Data Mining ◽  
Useful Knowledge ◽  
Large Databases ◽  
A New Technique ◽  
Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

Data Mining Pembentukan Pola Penggunaan Alat Kontrasepsi Dengan Metode Association Rule

2020 ◽  
Vol 4 (1) ◽  
pp. 186-191
Author(s):  
Elisa Hafrida ◽  
◽  
Febrina Sari ◽  
Desyanti Desyanti ◽  
Siti Nurjannah ◽  
...  
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Frequent Item ◽  
Mining Association Rule

Penggunaan Alat Kontrasepsi secara berkelanjutan merupakan faktor yang mempengaruhi keberhasilan Program Keluarga Berencana (KB). Seperti yang diketahui tidak semua alat kontrasepsi cocok dengan kondisi setiap orang, oleh karenanya setiap pribadi harus bisa memilih alat kontrasepsi yang cocok untuk dirinya. Permasalahannya banyak para wanita sulit untuk menentukan pilihan alat kontrasepsi yang akan digunakan, selain kurangnya pengetahuan dan informasi, Sampai saat ini belum ada konsep atau Pola untuk pemilihan alat kontrasepsi. Tujuan dari penelitian ini adalah Menemukan pola penggunaan alat kontrasepsi dengan menggunakan metode Data Mining Association Rule. Hasil kinerja Algoritma Apriori menghasilkan pola kombinasi yang menggambarkan kumpulan frequent item set dengan nilai confidence tertinggi yakni sebesar 90% pada Rule Jika Alat Kontrasepsi Suntik 3 Bulan Maka Usia Ibu 17-35 Tahun. Pola yang terbentuk merupakan hasil formulasi konsep, sehingga pola ini dapat dijadikan acuan bagi para calon akseptor dalam menentukan pilihan alat kontrasepsi yang cocok untuk digunakan.

Improved Algorithm for Mining Maximum Frequent Patterns Based on FP-Tree

2013 ◽  
Vol 756-759 ◽  
pp. 3692-3695 ◽  
Author(s):  
Nai Li Liu ◽  
Lei Ma
Keyword(s):  
Data Mining ◽  
Fast Algorithm ◽  
Association Rule ◽  
Frequent Patterns ◽  
Memory Space ◽  
The Cost ◽  
Mining Association Rule ◽  
Very High ◽  
Improved Algorithm

Mining association rule is an important matter in data mining, in which mining maximum frequent patterns is a key problem. Many of the previous algorithms mine maximum frequent patterns by producing candidate patterns firstly, then pruning. But the cost of producing candidate patterns is very high, especially when there exists long patterns. In this paper, the structure of a FP-tree is improved, we propose a fast algorithm based on FP-Tree for mining maximum frequent patterns, the algorithm does not produce maximum frequent candidate patterns and is more effectively than other improved algorithms. The new FP-Tree is a one-way tree and only retains pointers to point its father in each node, so at least one third of memory is saved. Experiment results show that the algorithm is efficient and saves memory space.

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