scholarly journals PENERAPAN DATA MINING MENGGUNAKAN ALGORITMA K-MEANS UNTUK MENGETAHUI MINAT CUSTOMER DI TOKO HIJAB

2019 ◽  
Vol 15 (2) ◽  
pp. 241-246
Author(s):  
Yulianti Yulianti ◽  
Dwi Yuni Utami ◽  
Noer Hikmah ◽  
Fuad Nur Hasan
Keyword(s):  
Data Mining ◽  
Transaction Data ◽  
Business People ◽  
Sales Strategy ◽  
Two Parameters

Hijab is not a foreign thing for the population in Indonesia, because most of the population of Indonesia is Muslim. Today, many business people, especially hijab sellers, provide a variety of brands and models in the hijab they sell. Therefore sellers are required to be able to think intelligently in making a sales strategy that will certainly be useful to know clearly which products are most in demand by customers, and also to increase sales in their stores. Then there needs to be an alternative that can realize the recording of sales transaction data more quickly and structured. In this study the authors applied the k-means algorithm to determine customer interest in the products they sell. In the calculation that has been done by using two parameters, namely the transaction and the number of sales and passing three iterations with the results of iterations one gets a ratio of 0.374324132, the iteration two gets the ratio 0.543018325, and the iteration three gets the same ratio value as second iteration. So it can be concluded that the hijab that is most desirable by the customers is the hijab with the brand Rabbani, Elzatta, and Zoya, the low-interest hijab branded by Dian Pelangi, Kami Idea, and Meccanism. And the hijab with those who are not high and also not low is the hijab under the brand Ria Miranda, Jenahara, Shasmira, and Shafira.

Pemanfaatan Metode K-Means Dalam Penentuan Persediaan Barang

2018 ◽  
Vol 6 (1) ◽  
pp. 41-48
Author(s):  
Santoso Setiawan
Keyword(s):  
Data Mining ◽  
Stock Management ◽  
Transaction Data ◽  
Business People ◽  
Cluster Data ◽  
Data Clusters ◽  
Inventory Strategy ◽  
Transactional Data

Abstract   Inaccurate stock management will lead to high and uneconomical storage costs, as there may be a void or surplus of certain products. This will certainly be very dangerous for all business people. The K-Means method is one of the techniques that can be used to assist in designing an effective inventory strategy by utilizing the sales transaction data that is already available in the company. The K-Means algorithm will group the products sold into several large transactional data clusters, so it is expected to help entrepreneurs in designing stock inventory strategies.   Keywords: inventory, k-means, product transaction data, rapidminer, data mining   Abstrak   Manajemen stok yang tidak akurat akan menyebabkan biaya penyimpanan yang tinggi dan tidak ekonomis, karena kemungkinan terjadinya kekosongan atau kelebihan produk tertentu. Hal ini sangat berbahaya bagi para pelaku bisnis. Metode K-Means adalah salah satu teknik yang dapat digunakan untuk membantu dalam merancang strategi persediaan yang efektif dengan memanfaatkan data transaksi penjualan yang telah tersedia di perusahaan. Algoritma K-Means akan mengelompokkan produk yang dijual ke beberapa cluster data transaksi yang umumnya besar, sehingga diharapkan dapat membantu pengusaha dalam merancang strategi persediaan stok.   Kata kunci: data transaksi produk, k-means, persediaan, rapidminer, data mining.

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

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.

Decision Support System using Data Mining Method for a Cross Selling Strategy in Retail Stores

2014 ◽  
Vol 3 (3) ◽  
pp. 171
Author(s):  
Imam Tahyudin ◽  
Mohammad Imron ◽  
Siti Alvi Solikhatin
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Large Data ◽  
Retail Stores ◽  
Mining Method ◽  
Transaction Data ◽  
Sales Strategy ◽  
Using Data ◽  
Retail Company ◽  
Confidence Value

<p>A sales transaction dataof a retail company which is collect edevery day is enormous. Very large data will bemore meaning fultoin crease the company’s profitsif itcanbe extracted properly. Based on the research resultsof Andhika, et al[1], ZhangandRuan[6], Herera et al [7], Witten [11], explained that one of the methods that can gather information from the transaction data is the method of association. With this method it can be determined the patterns of transactions performed simultaneously and repeatedly. Thus, it can be obtained amodel that can be used as a reference for cross selling sales strategy. The purpose of this research is to apply data mining association methods of data mining by using <em>apriori </em>algorithm to create a new sales strategy for cross selling. Based on calculations, Association Rule is implemented by applying Confidence value=0.8while the value of Support=0.1 of the defined minimum value, the total result are 77 rules.</p><p>Keywords: Data Mining, Association, <em>Apriori</em> Algorithm, Cross Selling, Retail Stores</p>

scholarly journals Penerapan Asosiasi Algoritma Apriori Pada Data Penjualan Alat-Alat Listrik Dan Tekhnik

METIK JURNAL ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 71-76
Author(s):  
Regina Pihu Atadjawa ◽  
Tuti Haryanti ◽  
Laela Kurniawati
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Microsoft Excel ◽  
Transaction Data ◽  
Sales Data ◽  
Business People ◽  
Electronic Product ◽  
Sales And Marketing ◽  
Product Sales

The incompatibility of information in reporting productsthat are sold, and data storage is very large, business people, especially in the sales business are required to find an appropriate strategy that can increase sales and marketing of products sold, one of which is by using electronic product sales data. Therefore, anapplication is needed that is able to sort and select data, so that information can be obtained that is useful for users, namely data mining. Associate patterns can be used to place products that are often purchased together into an area that is close together so as to facilitate the customer in finding the desired product and designing the appearance of products in the catalog. The method used is theApriori Algorithm method, with the help ofTanagra 1.4.50 tools and processing transaction data using Microsoft Excel 2007.

scholarly journals Analysis of Customer Transaction Data Associations Based on The Apriori Algorithm

2020 ◽  
Vol 3 (1) ◽  
pp. 23-28
Author(s):  
Tri Astuti ◽  
Bella Puspita
Keyword(s):  
Data Mining ◽  
Building Materials ◽  
Main Product ◽  
Small And Medium Enterprises ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Sales Strategy ◽  
Data Mining Approach ◽  
Sales Process ◽  
Medium Enterprises

UD Dian Pertiwi is one of the small and medium enterprises engaged in materials with the main product is building materials. This business experiences large amounts of transactions every day, the data obtained becomes increasingly large, and it will only be limited to a pile of useless data or commonly called junk. By utilizing a data mining approach apriori algorithm technique, the data can be utilized to support the sales process and achieve a target of UD Dian Pertiwi. Based on research and data mining that has been done using association analysis and apriori algorithms by applying a minimum of support = 1% and a minimum of confidence = 70% resulted in the ten strongest association rules can be used by UD Dian Pertiwi in the process of applying a sales strategy including determining interrelationships, in short, the product has the potential to be purchased at the same time, increasing the amount of product stock and conducting promotions.

scholarly journals Perancangan Aplikasi Data Mining Transaksi Penjualan untuk Mengetahui Pola Beli Konsumen pada Toko Singgalang Padang Menggunakan Algoritma Apriori Berbasis Web

2018 ◽  
pp. 37-44
Author(s):  
Nelisa ◽  
Aulia Fitrul Hadi
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Association Analysis ◽  
Frequent Itemset ◽  
Business Decision ◽  
Transaction Data ◽  
Analysis Application ◽  
Two Parameters

It takes a method or technique that can transform mountains of data into a valuable information or knowledge (knowledge) that are useful to support business decision making. Therefore in this paper association analysis application developed for extracting and interpreting the pattern of trend of sales of goods are often sold simultaneously from the transaction data using algorithms apriori. Apriori algorithms will from a frequent itemset as predetermined based on two parameters, support and confidence, to find the rules of the association between a combination of items. Knowledge of a product can be used by companies to increase production ansd sales of a 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 Sales Transaction Data Analysis using Apriori Algorithm to Determine the Layout of the Goods

2018 ◽  
Vol 1 (1) ◽  
pp. 16-23
Author(s):  
Taqwa Hariguna ◽  
Uswatun Hasanah ◽  
Nindi Nofi Susanti
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Association Rules ◽  
High Frequency ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Sales Strategy ◽  
Processing Data ◽  
Using Data

In a shop, usually apply a sales strategy in order. The sales strategy can be in the form of determining the layout of goods so that they are close to one another. Determining the layout of items can be based on items that are often purchased simultaneously. Searching for items that are often purchased together can be done using data mining techniques, which is processing data to become more useful information. Sales transaction data processing can be done using apriori algorithm. Apriori algorithm is the most famous algorithm for finding high-frequency patterns and generating association rules. From the results of the discussion and data analysis, there were 3 (three) association rules formed, namely "If you buy Milo Active 18 grm, then buy ABC Kopi Susu 31G" with support 0.36% and 75% confidence, "If you buy Dancow 1 + Honey 200 grm, then buy Ice Cream Corneto" wit H Support 0.36% and confidence 60%, "If you buy SIIP Roasted 6.5 grm, then buy Davos Strong 10 grm" with support 0.36% and 75% confidence. From the association's rules can be used as decision making to determine the layout of goods that are likely to be purchased simultaneously by the buyer

scholarly journals Implementasi Data Mining Menggunakan Algoritma Apriori Untuk Analisis Keranjang Belanja Pada Transaksi Penjualan Pada PT Madu Kembang Joyo

2021 ◽  
Vol 4 (1) ◽  
pp. 69-74
Author(s):  
Nasib Ratna Sari Purba ◽  
Fristi Riandari
Keyword(s):  
Data Mining ◽  
Consumer Spending ◽  
Transaction Data ◽  
Cart Analysis ◽  
Sales Strategy ◽  
Effective Product ◽  
Product Sales ◽  
Product Layout ◽  
Confidence Value

In the sale of goods (products), companies often experience problems because of the irregular level of consumer spending. Determination of product layout is done to make it easier for consumers to find honey products so as not to disappoint consumers in finding the location of which products are suitable to be combined with other products that are often in demand by consumers, so that consumers can save time. Based on the problems faced by the company, data mining analysis tools are needed. Currently, the utilization of data that is owned is not fully maximized, it is limited to making reports. The problem of research is the accumulation of unused transaction data, the difficulty of placing products according to consumer needs. The absence of an effective product sales strategy. The application is built using the PHP programming language with the MySQL database. The data used for shopping cart analysis on the sales transaction of Joyo Flower Honey is 1 month transaction data. The combination of items with the highest support x confidence value will be used as a combination to determine the placement of the suitable item to be connected between the two products that consumers are most interested in. In addition, the combination of these items can be used by management to position the product on the shelf which will make it easier for consumers to find the product they need.

scholarly journals Implementasi Algoritma Apriori Dalam Menentukan Penjualan Mobil Yang Paling Diminati Pada Honda Permata Serpong

2020 ◽  
Vol 4 (2) ◽  
pp. 302
Author(s):  
Dewi Anggraini ◽  
Sukmawati Anggraeni Putri ◽  
Lilyani Asri Utami
Keyword(s):  
Data Mining ◽  
Information Needs ◽  
Decision Makers ◽  
Accurate Information ◽  
Apriori Algorithm ◽  
Analysis Techniques ◽  
Business People ◽  
Car Sales ◽  
Two Parameters ◽  
Confidence Value

The development of information technology is growing rapidly so that it enters various fields, the need for fast, accurate and accurate information is needed. But the fact is that high information needs are not balanced by the presentation of adequate information. Business development and competition are increasingly complex because consumers are very perspective making business people have to be smart in reading situations. So that business people can make a prediction of consumer interest to be used as a prediction of the company in making a decision, and change a strategy that is most appropriate for consumers. Decision makers try to utilize the available data warehouse, this encourages the emergence of new branches of science to overcome the extraction of information in very large amounts of data. To find out which Honda cars are most in demand by consumers, Data Mining techniques are required using the Apriori Algorithm method, and supported by the Tanagra Application by examining sales data for 1 year. Data Mining is an amalgamation of data analysis techniques, while Apriori Algorithm is the most frequently used method because it is very simple, easy and most widely proposed by some researchers, because there are two parameters namely Support Value and Confidence Value. Then the prediction results of the study found that Honda's car sales that most demanded by consumers were Brio Satya, HRV, Mobillio, Jazz, and CRV

Sign in / Sign up

Export Citation Format

Share Document