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 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 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.

scholarly journals Data Mining Menggunakan Algoritma Apriori untuk Rekomendasi Produk bagi Pelanggan

2019 ◽  
Vol 7 (3) ◽  
pp. 103-108
Author(s):  
Ariefana Ria Riszky ◽  
Mujiono Sadikin
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Marketing Strategy ◽  
Association Rule ◽  
Apriori Algorithm ◽  
Processing Data ◽  
Total Data ◽  
Product Recommendations ◽  
Rule Method ◽  
Confidence Value

The implementation of a marketing strategy requires a reference so that promotion can be on target, such as by looking for similarities between product items. This study examines the application of the association rule method and apriori algorithm to the purchase transaction dataset to assist in forming candidate combinations among product items for customer recommended product promotion. The purchase transaction dataset was collected in October and November 2018 with a total data of 1027. In the experiment, the minimum value of support is 85%, and the minimum confidence value is 90% by processing data using the Weka software 3.9 version. Apriori algorithm can form association rules as a reference in the promotion of company products and decision support in providing product recommendations to customers based on defined minimum support and confidence values.

XML-Enabled Association Analysis

2011 ◽  
pp. 2117-2122
Author(s):  
Ling Feng
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Marketing Strategy ◽  
Association Rules ◽  
Association Rule ◽  
Rule Mining ◽  
Xml Data ◽  
Market Basket ◽  
Transactional Databases ◽  
Mining Association Rules

The discovery of association rules from large amounts of structured or semi-structured data is an important data mining problem [Agrawal et al. 1993, Agrawal and Srikant 1994, Miyahara et al. 2001, Termier et al. 2002, Braga et al. 2002, Cong et al. 2002, Braga et al. 2003, Xiao et al. 2003, Maruyama and Uehara 2000, Wang and Liu 2000]. It has crucial applications in decision support and marketing strategy. The most prototypical application of association rules is market basket analysis using transaction databases from supermarkets. These databases contain sales transaction records, each of which details items bought by a customer in the transaction. Mining association rules is the process of discovering knowledge such as “80% of customers who bought diapers also bought beer, and 35% of customers bought both diapers and beer”, which can be expressed as “diaper ? beer” (35%, 80%), where 80% is the confidence level of the rule, and 35% is the support level of the rule indicating how frequently the customers bought both diapers and beer. In general, an association rule takes the form X ? Y (s, c), where X and Y are sets of items, and s and c are support and confidence, respectively. In the XML Era, mining association rules is confronted with more challenges than in the traditional well-structured world due to the inherent flexibilities of XML in both structure and semantics [Feng and Dillon 2005]. First, XML data has a more complex hierarchical structure than a database record. Second, elements in XML data have contextual positions, which thus carry the order notion. Third, XML data appears to be much bigger than traditional data. To address these challenges, the classic association rule mining framework originating with transactional databases needs to be re-examined.

Application of Data Mining Technology Based on Apriori Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 2036-2039
Author(s):  
Jian Xing Chen
Keyword(s):  
Data Mining ◽  
Computer Simulation ◽  
Association Rule ◽  
Computer Time ◽  
Data Mining Algorithm ◽  
Apriori Algorithm ◽  
Mining Technology ◽  
Computer Data ◽  
Mining Algorithm ◽  
Step Distance

With the continuous expansion of computer simulation scale, the demand for data mining algorithm is also more and more big. The difficulties in computer data mining technology are focused on algorithm development. Apriori algorithm is a kind of computer data mining algorithm which can greatly improve the computational efficiency. The algorithm uses association rule, which can avoid repeated frequently by layer scanning, reducing the computer time. This paper uses Apriori algorithm to design the data mining parameter optimization model of computer 3D human biology simulation, and applies to improve the step three jump. Through the simulation we found step distance appropriate, it provides technical reference for the application of computer simulation technology in sports.

scholarly journals Penerapan Data Mining Apriori Pada Persediaan Obat (Studi Kasus Apotek Rafif Farma Medan)

2019 ◽  
Vol 3 (2) ◽  
pp. 115
Author(s):  
Mardiah Mardiah
Keyword(s):  
Data Mining ◽  
Phase Analysis ◽  
Customer Service ◽  
Association Rule ◽  
High Frequency ◽  
A Priori ◽  
Inventory Systems ◽  
Frequent Pattern ◽  
Apriori Algorithm ◽  
Analysis Algorithm

<span><em>The importance of inventory systems at a pharmacy and the type of goods which</em><br /><span><em>are a top priority that must be in stock. It is useful to anticipate the void stuff. Due to the</em><br /><span><em>lack of inventory may affect customer service and asset to the pharmacy. Therefore, this</em><br /><span><em>study was conducted to help resolve those problems by designing a data mining</em><br /><span><em>application that serves to predict sales of the drug is needed most knowable a priori</em><br /><span><em>algorithm with the help of Tools Tanagra. One of the interesting association analysis</em><br /><span><em>phase analysis algorithm that generates a high frequency patterns (frequent pattern</em><br /><span><em>mining).</em><br /><span><em>Keywords: Data Mining, Apriori Algorithm, Association Rule</em></span></span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span>

scholarly journals Penerapan Assosiation Rule Untuk Mendukung Sistem Pencapaian Target Pajak Pendapatan Kabupaten Deli Serdang

2018 ◽  
Vol 2 (2) ◽  
pp. 179
Author(s):  
Muhammad Eka ◽  
Rara Astili Siregar
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Income Tax ◽  
Line Graph ◽  
Tax Planning ◽  
Tax Revenue ◽  
Apriori Algorithm ◽  
Regional Income ◽  
Achievement Data ◽  
Tax Management

Deli Serdang Regency has a complete and unique topography because there are coastal areas, lowlands and mountainous highlands with an area of 2,497.72 Ha consisting of 22 sub-districts, 380 villages and 14 villages. The main potential of the Deli Serdang regency are agriculture, smallholder plantations, large plantations, fisheries, aquaculture, livestock, industry, trade and tourism. Based on this big potential, Deli Serdang District Region has a large potential income tax. Planning and regional income tax management system of Deli Serdang Regency are carried out at the Regional Revenue Department of Deli Serdang Regency. Based on achievement data of tax revenue which is posted on Regional Deliberation Separtment of Deli Serdang Regency website, rate of regional income is still slow. The target line graph and the realization of regional revenues indicate that the targets have to be achieved are still far, because until June 2017 achievement only Rp. 56,950,403,904.02, - while the target to the end of December 2017 is Rp. 484,520,000,000. To achieve the annual target as expected need a strategy. Association analysis is also known as one of the data mining techniques basis of various other mining data techniques. By using the Apriori Algorithm, an analysis of the obstacles in achieving targets can be done to find interesting rules that are useful in supporting the system of achieving local tax targets. The implementation of Association Rule to support the achievement of the tax target is expected to help the process of achieving the target of regional income tax, especially in the Regional Revenue Department of Deli Serdang Regency.

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