Aplikasi Pengenalan Pola Pembelian Konsumen Menggunakan Kombinasi Algoritma FP-Growth Dan ECLAT Method (FEM)

Sales data stored in enterprise databases are usually stored as archives or documentation. In the case of retail companies, data mining science can be used to extract new information from sales database, ie consumer purchase pattern analysis. The algorithm that can be used to analyze consumer purchase pattern is FEM algorithm using combination of Frequent Pattern Growth (FP-Growth) and Eclat algorithm. The construction of FP-Tree tree structure is done by using FP-Growth algorithm, while the process of extraction of items purchased (frequent itemset) is done by using Eclat algorithm. The application designed can be used to analyze consumer purchase pattern by generating associative rules using FEM algorithm through the Analysis form and printing the consumer purchase pattern through the purchase pattern report

Market Basket Analysis for Determination of Consumer Behavior at XYZ Stores Using R Programming

Data mining is one of the fields of science in the world of informatics which has an important role, especially with regard to data. There are many algorithms and methods that can be used to process data. The paper this time the author tries to conduct research on consumer behavior by using one of the data mining techniques, namely market basket analysis. This research uses the R Programming tool, where it is hoped that the research can be carried out effectively and efficiently. Based on the research conducted, it is known that there has been a significant purchase of several items that have been described as a plot. The tendency of consumers to buy several items followed by other items can be a consideration for arranging the layout of goods on the sales shelf or arranging product stock in a supermarket

Clustering of Non-Associated Item Sets for Analyzing Show Room Sales Dataset

Market basket analysis (MBA) is a well-liked method for identifying relationships between products that people purchase in a database. It is predicated on association rule mining (ARM), a data mining technique that pulls valuable data from huge databases. Due to consumers using internet applications for online shopping and insurance, an ever-increasing amount of data is generated online. It produces large amounts and, if mined effectively, will greatly benefit society as a whole as well as individuals. So, numerous data science and machine learning-related techniques have been created to gradually unlock the potential. The Clustering of Non-Associated Item Sets (CNAIS) of the Sales dataset used in the Showroom for choosing customers for benefits and web application design is discussed in this study. The CNAIS algorithm implementation process and dataset for this study are discussed

PENERAPAN DATA MINING MENGGUNAKAN METODE CLUSTERING K-MEANS PADA ANALISA POLA BELANJA KONSUMEN DALAM MENINGKATKAN PENJUALAN (STUDI KASUS : TOKO ABIE JM PASAR PAGI JAYA MUKTI KOTA DUMAI)

This study applied Data Mining method to cluster sales transactions at Abie JM Stores experienced a decline in sales. Therefore, a strategy was needed to increase sales again. One way that can be done to determine customer needs is to analyze sales transaction data. The sales transaction data can be further processed to obtain more helpful information to increase income, sales, and purchase turnover. Data mining by using k-means grouping or clustering. Data mining can be used to find. solutions in making sales decisions to increase revenue. Sales data storage stores a large number of sales transaction records, where each record provides products purchased by consumers in each sales transaction. From the calculation results, it can be concluded that the k-means clustering method can support the system well. Therefore we need a data processing process using a data mining technique. This study's data collection process uses the interview process and shopping transaction data collection Keywords Clustering K-Means, Data Mining, Customer Analysis, Consumers, Transaction Data

ANALISA POLA PEMBELIAN KEBUTUHAN IBU DAN BAYI UNTUK MEMBUAT PAKET BELANJA MURAH DI TOKO LOVE BABY STORE

Competition in the business sector is a common thing and cannot be avoided. And that also applies to every business entity that is run in the online marketplace. The purpose of this study is to (1) apply data mining with association rules in managing sales data at the Love Baby Store store to produce shopping item sets that can help consumers in the check out process (2) make cheap shopping item sets from the results of sales data analysis previously to find out consumer buying patterns (3) Applying the FP-Growth algorithm, which is one of the algorithms in data mining to determine data that often appears, so that it can get information about consumer patterns and tendencies in shopping. (4) Love Baby store can find out consumer buying patterns, so they can develop new business strategies, such as designing cheap shopping item sets that can make it easier for consumers to check out and at a more affordable price. This research is a research that uses data mining in the form of sales transaction data for 3 months. This data was analyzed using FP-Growth. The results of this study are the number of purchase intentions is influenced by several factors, namely the relatively cheaper price and the existence of a discount or free shipping promo. The purchase pattern is designed using the FP-Growth method with the result "If consumers buy the Zap Jip Off Road 4 Drive Inertial Baby Toy Car, they will also buy a Fruit Teether/Baby Tooth Teether [Without Packing]" with a confidence level of 67

Association Rules in Data Mining: An Application on a Clothing and Accessory Specialty Store

Retailers provide important functions that increase the value of the products and services they sell to consumers. Retailers value creating functions are providing assortment of products and services: breaking bulk, holding inventory, and providing services. For a long time, retail store managers have been interested in learning about within and cross-category purchase behavior of their customers, since valuable insights for designing marketing and/or targeted cross-selling programs can be derived. Especially, parallel to the development of information processing and communication technologies, it has become possible to transfer customers shopping information into databases with the help of barcode technology. Data mining is the technique presenting significant and useful information using of lots of data. Association rule mining is realized by using market basket analysis to discover relationships among items purchased by customers in transaction databases. In this study, association rules were estimated by using market basket analysis and taking support, confidence and lift measures into consideration. In the process of analysis, by using of data belonging to the year of 2012 from a clothing and accessory specialty store operating in the province of Osmaniye, a set of data related to 42.390 sales transactions including 9.000 different product kinds in 35 different product categories (SKU) were used. Analyses were carried out with the help of SPSS Clementine packet program and hence 25.470 rules were determined

E-commerce Recommendation by an Ensemble of Purchase Matrices with Sequential Patterns

In E-commerce recommendation systems, integrating collaborative filtering (CF) and sequential pattern mining (SPM) of purchase history data will improve the accuracy of recommendations and mitigate limitations of using only explicit user ratings for recommendations. Existing E-commerce recommendation systems which have combined CF with some form of sequences from purchase history are those referred to as LiuRec09, ChioRec12, and HPCRec18. ChoiRec12 system, HOPE first derives implicit ratings from purchase frequency of users in transaction data which it uses to create user item rating matrix input to CF. Then, it computes the CFPP, the CF-based predicted preference of each target user_u on an item_i as its output from the CF process. Similarly, it derives sequential patterns from the historical purchase database from which it obtains the second output matrix of SPAPP, sequential pattern analysis predicted preference of each user for each item. The final predicted preference of each user for each item FPP is obtained by integrating these two matrices by giving 90\% to SPAPP and 10\% to CFPP so it can recommend items with highest ratings to users. A limitation of HOPE system is that in user item matrix of CF, it does not distinguish between purchase frequency and ratings used for CF. Also in SPM, it recommends items, regardless of whether user has purchased that item before or not. This thesis proposes an E-commerce recommendation system, SEERs (Stacking Ensemble E-commerce Recommendation system), which improves on HOPE system to make better recommendations in the following two ways: i) Learning the best minimum support for SPA, best k similar users for CF and the best weights for integrating the four used matrices. ii) Separating their two intermediate matrices of CFPP and SPAPP into four intermediate matrices of CF not purchased, SPM purchased, SPM not purchased and purchase history matrix which are obtained and merged with the better-learned parameters from (i) above. Experimental results show that by using best weights discovered in training phase, and also separating purchased and not purchased items in CF and sequential pattern mining methods, SEERS provides better precision, recall, F1 score, and accuracy compared to tested systems

Critical Success Factors for the Implementation of Business-To- Business Electronic Procurement

This article investigates the critical success factors of e-procurement—the purchase of goods and services for organizations, which usually represents one of the largest expense items in a firm\u27s cost structure. Data was gathered using the survey method and a random sample drawn from the membership of the Institute for Supply Management and the Council of Logistics Management. Data was analyzed from 74 firms that implemented e-procurement. Factor analysis resulted in a four-factor solution: (1) factor one suggests the rationalization of the firm\u27s management of its suppliers; (2) factor two calls for redesigning affected business processes and influencing end-user/employee procurement-related behaviors; (3) factor three refers to carefully orchestrating an e-procurement technology planning process with one\u27s suppliers and using intelligence in designing the software and mining the data it produces; and (4) factor four relates to selecting an e-procurement solution and/or simultaneously participating in a number of electronic environments supporting e-procurement

Time Series Data Mining: A Retail Application Using SAS Enterprise Miner

Modern technologies have allowed for the amassment of data at a rate never encountered before. Organizations are now able to routinely collect and process massive volumes of data. A plethora of regularly collected information can be ordered using an appropriate time interval. The data would thus be developed into a time series. With such data, analytical techniques can be employed to collect information pertaining to historical trends and seasonality. Time series data mining methodology allows users to identify commonalities between sets of time-ordered data. This technique is supported by a variety of algorithms, notably dynamic time warping (DTW). This mathematical technique supports the identification of similarities between numerous time series. The following research aims to provide a practical application of this methodology using SAS Enterprise Miner, an industry-leading software platform for business analytics. Due to the prevalence of time series data in retail settings, a realistic product sales transaction data set was analyzed. This information was provided by dunnhumbyUSA. Interpretations were drawn from output that was generated using “TS nodes” in SAS Enterprise Miner

PENERAPAN ALGORITMA C.45 DALAM MENGANALISA KEPUASAN PELANGGAN PADA PENJUALAN FASHION TRIFT

Fashion thrift sales have become one of the fastest growing industries, especially in this digital era. This research was conducted at one of the thrift businesses in the city of Batam, while the problem with this thrift fashion shop is the lack of stock of goods that attracts customers, because the store buys sack items, likewise this store does not serve purchase orders because items in sacks are available in various types. This study aims to analyze customer satisfaction in data mining techniques to generate new knowledge using the C4.5 classification algorithm, this algorithm uses a data mining method that carries out the process of extracting information about previous decisions. This information is used as information to form a decision tree pattern. Based on all the results of the research stages that have been carried out on the Application of the Decision Tree Classification with the C4.5 Algorithm, it can be concluded that the decision tree analysis produced by the calculation of the C4.5 algorithm shows that the variables that have the highest gain or which are the main factors in determining customer satisfaction are discount variables, product quality and pric