scholarly journals INPLEMENTASI BUSINESS INTELLIGENCE DAN MARKET BASKET ANALYSIS UNTUK ANALISA DATA PENJULAN DI PT. ABC

2022 ◽  
Vol 7 (1) ◽  
pp. 37-42
Author(s):  
I Putu Susila Handika ◽  
I Gusti Agung Ayu Ari Satyawati
Keyword(s):  
Business Intelligence ◽  
Association Rule ◽  
Market Basket Analysis ◽  
Market Basket

Ditengah merebaknya kasus pandemi Covid-19 pada tahun 2020 di Indonesia, terjadi perubahan kecenderungan perilaku pelanggan dalam melakukan proses transaksi belanja khususnya pada gerai minimarket. Dengan diberlakukannya pysical distancing, pelanggan dituntut untuk berbelanja seefektif mungkin untuk menghindari penumpukan di dalam gerai. Manajemen perusahaan harus membuat setrategi untuk menyikapi perubahan perilaku dari pelanggan. Pada penelitian ini dikembangkan Business Intelligence dan metode Market Basket Analysis yaitu Apriori untuk menganalisa perilaku pelanggan dengan cara menganalisa riwayat transaksi penjualan. Hasil penelitian menunjukkan dashboard Business Intelligence dapat menampilkan data dalam bentuk grafik dan tabel sehingga memudahkan pengguna dalam proses analisa. Selain itu Association Rule menggunakan metode Apriori menghasilkan nilai support dan confidence sebagai gambaran produk-produk yang saling terkait, sehingga pihak merchendaising dapat dengan  mudah membuat keputusan. Hasil pengujian blackbox menunjukkan aplikasi yang dikembangkan dapat diterima oleh pengguna karena semua kebutuhan pengguna dapat diselesaikan oleh aplikasi.

scholarly journals Aplikasi Data Mining dalam penentuan layout swalayan dengan menggunakan metode MBA

2020 ◽  
Vol 5 (2) ◽  
pp. 130
Author(s):  
Muhammad Rizki ◽  
Desi Devrika ◽  
Isnaini Hadiyul Umam ◽  
Fitriani Surayya Lubis
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Pattern Growth ◽  
Item Item

Data mining merupakan salah satu cara untuk mendapatkan informasi yang tersimpan pada dabased yang berjumlah besar. Data transaksi penjualan pada sebuah swalayan sering kali hanya digunakan sebagai laporan penjualan saja. Dalam kenyataannya, data tersebut dapat memberikan informasi yang lebioh dari sekedar laporan penjualan saja. Salah satu informasi yang dapat kita ambil dari data transaksi penjualan adalah hubungan antar item. Kita dapat mengetahui kelompok item yang cenderung dibeli bersamaan oleh pelanggan dalam satu transaksi pembelian.. Market Basket Analysis (MBA) merupakan salah satu metode untuk menentukan kelompok item yang cenderung dibeli oleh pelanggan dalam satu waktu atau dalam satu transaksi pembelian. Informasi keterkaitan antar kelompok item tersebut dapat kita jadikan sebagai referensi untuk menentukan layout, dimana item yang sering dibeli bersamaan kita dekatkan dalam penataan layoutnya sehingga pelanggan tidak perlu lagi susah payah untuk mencari item tersebut. Berdasarkan studi kasus awal pada salah satu swalayan yang berada di Pekanbaru, penataan layout per clusternya dilakukan secara acak, sehingga pelanggan kesulitan untuk mencari item-item yang biasanya dibeli dalam satu kali transaksi. Pemilik swalayan menginginkna penataan layout ulang mengikuti pola pembelian pelanggan. Pettern growth merupakan salah satu Teknik dari MBA, dimana hasil analisis dapat diketahui kelompok item yang memiliki kecendrungan untuk dibeli bersamaan oleh pelanggan. Kata Kunci:  Data mining, MBA, Association rule, pattern growth, layout                         Data mining merupakan salah satu cara untuk mendapatkan informasi yang tersimpan pada dabased yang berjumlah besar. Data transaksi penjualan pada sebuah swalayan sering kali hanya digunakan sebagai laporan penjualan saja. Dalam kenyataannya, data tersebut dapat memberikan informasi yang lebioh dari sekedar laporan penjualan saja. Salah satu informasi yang dapat kita ambil dari data transaksi penjualan adalah hubungan antar item. Kita dapat mengetahui kelompok item yang cenderung dibeli bersamaan oleh pelanggan dalam satu transaksi pembelian.. Market Basket Analysis (MBA) merupakan salah satu metode untuk menentukan kelompok item yang cenderung dibeli oleh pelanggan dalam satu waktu atau dalam satu transaksi pembelian. Informasi keterkaitan antar kelompok item tersebut dapat kita jadikan sebagai referensi untuk menentukan layout, dimana item yang sering dibeli bersamaan kita dekatkan dalam penataan layoutnya sehingga pelanggan tidak perlu lagi susah payah untuk mencari item tersebut. Berdasarkan studi kasus awal pada salah satu swalayan yang berada di Pekanbaru, penataan layout per clusternya dilakukan secara acak, sehingga pelanggan kesulitan untuk mencari item-item yang biasanya dibeli dalam satu kali transaksi. Pemilik swalayan menginginkna penataan layout ulang mengikuti pola pembelian pelanggan. Pettern growth merupakan salah satu Teknik dari MBA, dimana hasil analisis dapat diketahui kelompok item yang memiliki kecendrungan untuk dibeli bersamaan oleh pelanggan. Kata Kunci:  Data mining, MBA, Association rule, pattern growth, layout

Comparative Analysis of Association Rule Mining Algorithms in Market Basket Analysis Using Transactional Data

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
AA Izang ◽  
SO Kuyoro ◽  
OD Alao ◽  
RU Okoro ◽  
OA Adesegun
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Market Basket Analysis ◽  
Rule Mining ◽  
Business Decisions ◽  
Market Basket ◽  
Minimum Support ◽  
Support Threshold ◽  
Transactional Data

Association rule mining (ARM) is an aspect of data mining that has revolutionized the area of predictive modelling paving way for data mining technique to become the recommended method for business owners to evaluate organizational performance. Market basket analysis (MBA), a useful modeling technique in data mining, is often used to analyze customer buying pattern. Choosing the right ARM algorithm to use in MBA is somewhat difficult, as most algorithms performance is determined by characteristics such as amount of data used, application domain, time variation, and customer’s preferences. Hence this study examines four ARM algorithm used in MBA systems for improved business Decisions. One million, one hundered and twele thousand (1,112,000) transactional data were extracted from Babcock University Superstore. The dataset was induced with Frequent Pattern Growth, Apiori, Association Outliers and Supervised Association Rule ARM algorithms. The outputs were compared using minimum support threshold, confidence level and execution time as metrics. The result showed that The FP Growth has minimum support threshold of 0.011 and confidence level of 0.013, Apriori 0.019 and 0.022, Association outliers 0.026 and 0.294 while Supervised Association Rule has 0.032 and 0.212 respectively. The FP Growth and Apirori ARM algorithms performed better than Association Outliers and Supervised Association Rule when the minimum support and confidence threshold were both set to 0.1. The study concluded by recommending a hybrid ARM algorithm to be used for building MBA Applications. The outcome of this study when adopted by business ventures will lead to improved business decisions thereby helping to achieve customer retention. Keywords: Association rule mining, Business ventures, Data mining, Market basket analysis, Transactional data.

Concept of Association Rule of Data Mining Assists Mitigating the Increasing Obesity

2017 ◽  
Vol 7 (2) ◽  
pp. 1-18
Author(s):  
Sugam Sharma
Keyword(s):  
Data Mining ◽  
Physical Exercise ◽  
Human Health ◽  
Chronic Diseases ◽  
Association Rule ◽  
Health Informatics ◽  
Daily Activity ◽  
Market Basket Analysis ◽  
Market Basket

Association rule of data mining is known to encompass a wide set of intelligent techniques that intent to unveil and analyze correlations and associations between items in a set. Market basket analysis is one such, possibly the most popular technique in business domain that is used to analyze combinations of items that often are listed together in various transactions. In this paper, the author strives to expand applicability of the same concept to human health under purview of health informatics. The present growing rate of obesity has raised alarming concept to the communities globally. It entails several chronic diseases that may be fatal eventually. This work aims to aid in the ongoing efforts to alleviate the obesity, primarily caused by lack of physical exercise. Concept of association rule of data mining may help regulating mild exercise by associating it with a daily activity, sleeping at night. Mild but regular short exercise just before sleep may help ameliorating individual's health.

scholarly journals Association Rule Mining for Intrusion Detection System: A Survey

2019 ◽  
Vol 8 (1) ◽  
pp. 20-24
Author(s):  
D. Selvamani ◽  
V. Selvi
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Association Rule ◽  
Association Rule Mining ◽  
Detection System ◽  
Market Basket Analysis ◽  
Rule Mining ◽  
Market Basket ◽  
Detection Systems ◽  
Large Sets

Many modern intrusion detection systems are based on data mining and database-centric architecture, where a number of data mining techniques have been found. Among the most popular techniques, association rule mining is one of the important topics in data mining research. This approach determines interesting relationships between large sets of data items. This technique was initially applied to the so-called market basket analysis, which aims at finding regularities in shopping behaviour of customers of supermarkets. In contrast to dataset for market basket analysis, which takes usually hundreds of attributes, network audit databases face tens of attributes. So the typical Apriori algorithm of association rule mining, which needs so many database scans, can be improved, dealing with such characteristics of transaction database. In this paper, a literature survey on the Association Rule Mining has carried out.

scholarly journals Optimization of Store Layout using Market Basket Analysis

2019 ◽  
Vol 8 (2) ◽  
pp. 6459-6463
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Product Placement ◽  
Retail Store ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Arduous Task ◽  
History Of ◽  
Shopping Basket ◽  
Grid Layout

Store layout is a crucial factor for attracting customers in a retail store. Use of appropriate store layout results in an increase in sales of the store. Grid layout, free flow layout, spine layout is a few commonly used store layouts in the retail store. The grid layout is used for supermarkets but the placement of different products as per the preference of the customer is quite an arduous task there. Purchase history of a supermarket can be utilized to predict the preferences of the customers and can be utilized as an aid for designing a better store layout. Market basket analysis is employed to get insights from the POS data of the supermarket. Market basket analysis (MBA) helps to extract the various association rules from the purchase data of the shoppers. A customer can pick different items identified with the items that the person has just put in his or her shopping basket or cart which frames an association rule. The extraction of such rules can help in the appropriate product placement in the store as per the shopper’s preference.

scholarly journals Market Basket Analysis to Identify Customer Behaviours by Way of Transaction Data

2017 ◽  
Vol 1 (1) ◽  
pp. 20 ◽  
Author(s):  
Fachrul Kurniawan ◽  
Binti Umayah ◽  
Jihad Hammad ◽  
Supeno Mardi Susiki Nugroho ◽  
Mochammad Hariadi
Keyword(s):  
Association Rule ◽  
Market Basket Analysis ◽  
Data Mining Technique ◽  
Market Basket ◽  
Transaction Data ◽  
New Information ◽  
Analysis Application ◽  
Rule Method ◽  
Confidence Value

Transaction data is a set of recording data result in connections with sales-purchase activities at a particular company. In these recent years, transaction data have been prevalently used as research objects in means of discovering new information. One of the possible attempts is to design an application that can be used to analyze the existing transaction data. That application has the quality of market basket analysis. In addition, the application is designed to be desktop-based whose components are able to process as well as re-log the existing transaction data. The used method in designing this application is by way of following the existing steps on data mining technique. The trial result showed that the development and the implementation of market basket analysis application through association rule method using apriori algorithm could work well. With the means of confidence value of 46.69% and support value of 1.78%, and the amount of the generated rule was 30 rules.

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 Implying Association Rule Mining and Market Basket Analysis for Knowing Consumer Behavior and Buying Pattern in Lockdown - A Data Mining Approach

2021 ◽  
Author(s):  
Anurag Sinha
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Indian Market ◽  
Market Basket Analysis ◽  
Rule Mining ◽  
Innovation Development ◽  
Market Basket ◽  
Data Mining Approach ◽  
Profound Knowledge ◽  
Online Stores

Buyer practices have changed as individuals are figuring out how to live with the new truth of COVID-19. Take-out and conveyance orders have expanded, and our customer has added new items to their menu because of new client inclinations. With every one of the continuous changes, the customer had numerous unanswered inquiries, for example, Smartbridge has broad involvement with café innovation development Café TECHNOLOGY CAPABILITIES :Are the most famous items as yet unchanged after COVID? :Which are the most sold item blends now? :What is the acknowledgment of new things? :What are clients purchasing alongside new things? :How have liquor deals changed? The customer previously had reports that followed item deals and operational measurements, notwithstanding, there was a need to get a more profound knowledge into item examination. The customer expected to recognize what items and introductions were being sold all the more frequently, measure the acknowledgment of new items, and figure out what items clients buy together to improve advertising efforts, advancements, and deals. he E-business industry is filling immensely in the Indian market. The modest 4G web bundles in India clearly gives a push to these ventures. Thus, as Covid19 first hit in Quite a while, individuals got terrified to go out from their homes in light of the fact that, in their mind, it's a dread of Covid. They even wonder whether or not to go out to purchase fundamental (FMCG) products. Frenzy purchasing additionally has seen and to stay away from this dread of COVID-19, individuals are offering inclinations to the E-Commerce destinations to purchase fundamental products and a few clients are new which joined to purchase fundamental merchandise during this Pandemic Lockdown period. Numerous clients are moving their purchasing conduct from disconnected retail locations to online stores. This paper examines the customer buying pattern during lockdown.

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