scholarly journals Perancangan Program Aplikasi Market Basket Analysis untuk Mendukung Persediaan Barang dengan Metode Fuzzy C-Covering

2012 ◽  
Vol 3 (1) ◽  
pp. 241
Author(s):  
Zahedi Zahedi ◽  
Charies Chandra
Keyword(s):  
Data Mining ◽  
Fuzzy Sets ◽  
Market Basket Analysis ◽  
Huge Number ◽  
Market Basket ◽  
Transaction Data ◽  
Sales Data ◽  
Data Market ◽  
A Company ◽  
Data Mining Application

Any difficulty in analyzing sales transaction data is often faced by a company due to the huge number of sales transactions and the limited tools to process the data. It results in losses for the company since it is difficult to estimate the goods supply for subsequent sales. In this paper a data mining application is designed to analyze sales data. Market Basket Analysis is a data mining application that aims to determine most purchased or used products at once by the consumer. The process of Market Basket Analysis is to analyze consumer buying habits by finding associations among products purchased by different customers. The method used in Market Basket Analysis is a method of Fuzzy c-Covering, which is one method to classify the elements of a universal set into partitions of fuzzy sets. This study found that the value of support and confidence is part of the Market Basket Analysis, computed using the Fuzzy c-Covering. The higher the limit, the more selected the analytical results obtained are.

scholarly journals Application of Text Message Held in Image Using Combination of Least Significant Bit Method and One Time Pad

2019 ◽  
Vol 13 (4) ◽  
pp. 323
Author(s):  
Eferoni Ndruru ◽  
Taronisokhi Zebua
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Data Hiding ◽  
Text Message ◽  
Third Party ◽  
Least Significant Bit ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Sales Data ◽  
The Third

Stenography and security are one of the techniques to develop art in securing data. Stenography has the most important aspect is the level of security in data hiding, which makes the third party unable to detect some information that has been secured. Usually used to hide textinformationThe (LSB) algorithm is one of the basic algorithms proposed by Arawak and Giant in 1994 to determine the frequent item set for Boolean association rules. A priory algorithm includes the type of association rules in data mining. The rule that states associations between attributes are often called affinity analysis or market basket analysis. OTP can be widely used in business. With the knowledge of text message, concealment techniques will make it easier for companies to know the number of frequencies of sales data, making it easier for companies to take an appropriate transaction action. The results of this study, hide the text message on the image (image) by using a combination of LSB and Otp methods.

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 PERBANDINGAN ASOSSIATION RULE BERBENTUK BINER DAN FUZZY C-PARTITION PADA ANALISIS MARKET BASKET DALAM DATA MINING

2012 ◽  
Vol 12 (2) ◽  
pp. 135
Author(s):  
Altin J Rindengan
Keyword(s):  
Data Mining ◽  
Fuzzy Sets ◽  
Association Rules ◽  
Fuzzy Set ◽  
Association Rule ◽  
The Other ◽  
Binary Form ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Good Relationship

PERBANDINGAN ASOSSIATION RULE BERBENTUK BINER DAN FUZZY C-PARTITION PADA ANALISIS MARKET BASKET DALAM DATA MININGABSTRAKSalah satu analisis dalam data mining adalah market basket analysis untuk menganalisa kecenderungan pembelian suatu barang yang berasosiasi dengan barang yang lain. Dalam tulisan ini membahas aturan asosiasinya dengan mempertimbangkan jumlah item barang yang dibeli dalam satu transaksi. Asumsinya adalah keterkaitan pembelian suatu barang dengan barang yang lain dalam satu transaksi akan semakin kecil jika jumlah item barang yang dibeli semakin banyak. Tulisan ini menganalisa asosisasi antar item barang dengan membuat tabel transaksi dalam bentuk nilai fuzzy set dibandingkan dengan analisa asosiasi yang biasa dilakukan dalam bentuk biner. Berdasarkan analisis terhadap data yang digunakan memberikan hasil support dan confidence yang cenderung lebih kecil tetapi lebih realistis dibanding aturan asosisasi biasa. Keywords: analisis market basket, association rule, data mining, fuzzy c-partition.COMPARISON OF ASSOCIATION RULE WITH BINARY AND FUZZY C-PARTITION FORM AT MARKET BASKET ANALYSIS ON DATA MININGABSTRACTOne analysis in data mining is market basket analysis to analyze the purchase of a good trends associated with other items. In this paper discussing the association rules by considering the number of items purchased in one transaction. The assumption is that the purchase of a good relationship with the other items in one transaction will be smaller if the number of items purchased items more and more. This paper analyzes the association between the items of goods by making the transaction table in the form of fuzzy sets of values to compare with analysis of the usual associations in binary form. Based on the analysis of the data used to support and confidence of which tend to be smaller but more realistic than usual asosisasi rules. Keywords: market basket analysis, association rule, data mining, fuzzy c-partition.

scholarly journals ASSOCIATION RULE DALAM MENENTUKAN CROSS-SELLING PRODUK MENGGUNAKAN ALGORITMA FP-GROWTH

2020 ◽  
Vol 7 (4) ◽  
pp. 102
Author(s):  
Delila Melati ◽  
Titi Sri Wahyuni
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Analysis Method ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Transaction Data ◽  
Time Processing ◽  
Processing Data ◽  
Analyze Data

Sales transaction data at Bigmart stored in a database will be able to become new knowledge if processed using the data mining process. In addition, inventory is also a problem that is being faced by Bigmart. Data mining is able to analyze data into information in the form of transaction patterns that are useful in increasing revenue, one of which is Cross-Selling products. Association rule is one of the data mining methods included in the Market Basket Analysis method. The algorithm used is the FP-Growth algorithm because it has the virtue of shorter time processing data. The pattern obtained is determined by the value of support (support) and the value of confidence (confidence). To find the association rules the FP-Growth algorithm is used. To get more accurate association rules, use the Weka 8.3 tool. There are 11 association rules obtained using the Weka 8.3 tool which is classified as a Stong Rule that meets the Minimum support value of 10% and Minimum confidence 80%. Keywords: Database, Cross-selling, Market Basket Analysis, Association Rule, FP-Growth

scholarly journals Implementation of K-Medoids and FP-Growth Algorithms for Grouping and Product Offering Recommendations

2019 ◽  
Vol 2 (2) ◽  
pp. 107
Author(s):  
Imaduddin Syukra ◽  
Assad Hidayat ◽  
Muhammad Zakiy Fauzi
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Market Basket ◽  
Transaction Data ◽  
A Value ◽  
Using Data ◽  
The Right ◽  
A Company ◽  
Technical Recommendations

212 Mart Rambutan Street on Pekanbaru City is a company engaged in retail. Meeting the needs of consumers and making the right decision in determining the sales strategy is a must. One way to find out market conditions is to observe sales transaction data using data mining. The data mining method commonly used to analyze market basket (Market Basket Analysis) is the Association Rule. The Association Rule can provide product recommendations and promotions, so that the marketing strategy is more targeted and the items promoted are the customer's needs. At 212 Mart, the determination of product promotion is obtained from the analysis of sales transaction data reports, which are based on the most sold products and the expiration date. Often the product being promoted does not fit the customer's needs. The purpose of this study is to apply the K-Medoids algorithm for clustering on FP-Growth in producing product recommendation rules on a large number of datasets so that they can provide technical recommendations / new ways to the 212 Mart in determining product promotions. The results obtained are from the experiments the number of clusters 3 to 9 obtained optimal clusters of 3 clusters based on the validity test of the Davies Bouldin Index with a value of 0.678. With a minimum support value of 5% - 9% and a minimum value of 50% confidence, the result is that the Association Rule is found only in cluster 3 with 5 rules.

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 Implementation of Apriori Algorithm for Determining Purchase Patterns in One Transaction

2018 ◽  
Vol 7 (4.33) ◽  
pp. 204
Author(s):  
Murnawan . ◽  
Ardiles Sinaga ◽  
Ucu Nughraha
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Apriori Algorithm ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Transaction Data ◽  
Future Developments ◽  
Operational Activities ◽  
Purchase Patterns

The organization data owned is one of the assets of the organization. With the daily operational activities, the longer the data will increase. By using techniques that can do data processing, these data can be obtained important information that can be used for future developments. Association rules are one of these techniques which aims to find patterns in the form of products that are often purchased together or tend to appear together in a transaction from transaction data which is generally very large by using the concept association rules themselves derived from Market Basket Analysis terminology, namely search for relationships from several products in a purchase transaction. In designing this application will build applications that classify the data items based on the tendency to appear together in a transaction using the Apriori Algorithm. The Apriori algorithm is the first algorithm and is often used to find association rules in data mining applications with association rule techniques. 

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 PENERAPAN DATA MINING UNTUK MENGANALISA POLA PERMINTAAN PEMASANGAN CCTV DENGAN MENGGUNAKAN METODE MARKET BASKET ANALYSIS STUDY KASUS CV. MITRA JAYA PERKASA

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Ramadhan Pandapotan Siringo–Ringo ◽  
Melda Panjaitan
Keyword(s):  
Data Mining ◽  
Large Data ◽  
Analysis Method ◽  
Market Basket Analysis ◽  
Mining System ◽  
Market Basket ◽  
Knowledge Based ◽  
Relationship Of ◽  
The Relationship ◽  
A Company

CV. MITRA JAYA PERKASA is a company engaged in CCTV installation services where demand for CCTV installation is one of the important factors. Therefore it is very important to know the cctv installation request pattern so that it can adjust to the warehouse stock and the cctv goods linkage. With the help of computers can be used as a solution to the problem, where by building a data mining system which is a knowledge-based computer program that provides solutions to problems and get information from large data warehouses. In this case the company can analyze the cctv installation request pattern using the market basket analysis method. It is expected that this data mining can provide information on the cctv installation request pattern and adjust the warehouse stock. The results of this study are an application that can help analyze the cctv installation request patterns and analyze the relationship of goods using the market basket analysis method.Keywords: Data Mining, Market Basket Analysis, cctv installation request patterns.

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