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

scholarly journals Market Basket Analysis Pada Mini Market Ayu Dengan Algoritma Apriori

2018 ◽  
Vol 2 (2) ◽  
pp. 472-478
Author(s):  
Erlin Elisa
Keyword(s):  
Data Mining ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Item Item

Data mining merupakan teknik untuk menggali informasi baru dari gudang data, informasi dipandang sangat penting dan berharga karena dengan menguasai informasi maka dengan mudah untuk mencapai sebuah tujuan, hal ini membuat setiap orang berlomba untuk memperoleh informasi, demikian juga pada usaha perdagangan seperti minimarket Ayu di Kota Batam. Minimarket ini berlokasi dekat dengan rumah penduduk, hal ini tentunya mempengaruhi tingkat penjualan, dengan adanya kegiatan penjualan setiap hari, data transaksi penjualan akan terus bertambah, menyebabkan penyimpanan data semakin besar. Data transaksi penjualan hanya dijadikan arsip tanpa dimanfaatkan dengan baik. Pada dasarnya kumpulan data memiliki informasi yang sangat bermanfaat. Analisis keranjang pasar dengan Algoritma Apriori merupakan salah satu metoda data mining yang bertujuan untuk mencari pola assosiasi berdasarkan pola belanja yang dilakukan konsumen, sehingga bisa diketahui item-item barang apa saja yang dibeli secara bersamaan, Hasil dari penelitian ini menemukan  Nilai support dan confidence tertinggi adalah Minyak dan Susu dengan nilai support 42,85% dan confidence 85,71%.

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 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.

Market Basket Analysis with Apriori Algorithm and Frequent Pattern Growth (Fp-Growth) on Outdoor Product Sales Data

2021 ◽  
Vol 2 (1) ◽  
pp. 132-139
Author(s):  
Wiwit Pura Nurmayanti ◽  
Hanipar Mahyulis Sastriana ◽  
Abdul Rahim ◽  
Muhammad Gazali ◽  
Ristu Haiban Hirzi ◽  
...  
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Frequent Pattern ◽  
Apriori Algorithm ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Transaction Data ◽  
Pattern Growth ◽  
Product Sales ◽  
The Relationship

Indonesia is an equatorial country that has abundant natural wealth from the seabed to the top of the mountains, the beauty of the country of Indonesia also lies in the mountains that it has in various provinces, for example in the province of West Nusa Tenggara known for its beautiful mountain, namely Rinjani. The increase in outdoor activities has attracted many people to open outdoor shops in the West Nusa Tenggara region. Sales transaction data in outdoor stores can be processed into information that can be profitable for the store itself. Using a market basket analysis method to see the association (rules) between a number of sales attributes. The purpose of this study is to determine the pattern of relationships in the transactions that occur. The data used is the transaction data of outdoor goods. The analysis used is the Association Rules with the Apriori algorithm and the frequent pattern growth (FP-growth) algorithm. The results of this study are formed 10 rules in the Apriori algorithm and 4 rules in the FP-Growth algorithm. The relationship pattern or association rule that is formed is in the item "if a consumer buys a portable stove, it is possible that portable gas will also be purchased" at the strength level of the rules with a minimum support of 0.296 and confidence 0.774 at Apriori and 0.296 and 0.750 at FP-Growth.  

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

2019 ◽  
pp. 518-536 ◽  
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 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 PENERAPAN DATA MINING UNTUK ANALISIS MARKET BASKET DENGAN ALGORITMA FP-GROWTH PADA PD PASAR TOHAGA

2020 ◽  
Vol 9 (1) ◽  
pp. 119
Author(s):  
Rusnandi Rusnandi ◽  
Suparni Suparni ◽  
Achmad Baroqah Pohan
Keyword(s):  
Data Mining ◽  
Frequent Pattern ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Pattern Growth ◽  
Study Support ◽  
To Come ◽  
Relationship Of ◽  
The Relationship

Sales data in 3 different shops (shop, Shop Maker Fernando and Son) at Tohaga Market in the form of PD book transactions are only seen in the absence of follow-up to determine the decision on who will come. Party owner only records the transactions of products sold and only see income per month. But with that data should be utilized to strategize on sales to come. By using the method of Frequent Pattern Growth Algorithm, the store can take decisions which require goods inventory more compared to other goods, and the placement of the goods in accordance with the relationship between the goods that are usually purchased a consumer can also be determined based on a Minimum Support and Minimum Confidence. Based on Market Basket Analysis obtained from the calculation of the Association by using the method of Frequent Pattern Growth Algorithm, then search for the value of the support and confidence to use Association Rules, Rules that are generated will be test by using Software RapidMiner. Then the placement of goods and inventory items in 3 different stores can be controlled with either the service so that the consumer will be increased, which in turn can increase the sales turnover. In this study Support is determined using threshold 40% and 83% Confidence. Having regard to the relationship of support and confidence the store owner can provide and put the items to be sold

scholarly journals Analisa Association Rule Pada Algoritma Apriori Untuk Minat Pembelian Alat Kesehatan

2021 ◽  
Vol 5 (1) ◽  
pp. 280
Author(s):  
Andi Rahmadsyah ◽  
Hartono Hartono ◽  
Rika Rosnelly
Keyword(s):  
Data Mining ◽  
Medical Devices ◽  
Association Rule ◽  
A Priori ◽  
Mining Method ◽  
Market Basket Analysis ◽  
Business World ◽  
Market Basket ◽  
Analysis Techniques ◽  
The Relationship

In the competition in the business world, especially the Medical Device industry, it requires developers to find an accurate strategy that can increase sales of goods. One way to overcome this problem is to continue to provide various types of medical devices in the warehouse. To find out what medical devices are purchased by consumers, market basket analysis techniques are carried out, namely analysis of consumer buying habits. In order to make it easier for companies to determine Buyers' interest in medical devices, a data mining method is needed which is accompanied by an a priori algorithm based on the purchasing process carried out by consumers based on the relationship between the products purchased. Based on the sample sales data for medical devices CV Andira Karya Jaya, amounting to 25 transactions and in this study a minimum support = 12% and a minimum confidence = 70% will be used. In the final stage, the results obtained are medical devices that are in demand by buyers at CV. Andira Karya Jaya, namely 1 M3 oxygen cylinder and 1 M3 troley of oxygen. Based on this data, CV. Andira Karya Jaya can provide supplies of medical devices that are of interest to buyers.

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