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 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 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 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. 

A Hybrid Algorithm of Mining Closed Itemsets for Large Databases

2011 ◽  
Vol 145 ◽  
pp. 292-296
Author(s):  
Lee Wen Huang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Execution Time ◽  
Hybrid Algorithm ◽  
Hybrid Approach ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Large Databases ◽  
Closed Itemsets ◽  
Simulation Results

Data Mining means a process of nontrivial extraction of implicit, previously and potentially useful information from data in databases. Mining closed large itemsets is a further work of mining association rules, which aims to find the set of necessary subsets of large itemsets that could be representative of all large itemsets. In this paper, we design a hybrid approach, considering the character of data, to mine the closed large itemsets efficiently. Two features of market basket analysis are considered – the number of items is large; the number of associated items for each item is small. Combining the cut-point method and the hash concept, the new algorithm can find the closed large itemsets efficiently. The simulation results show that the new algorithm outperforms the FP-CLOSE algorithm in the execution time and the space of storage.

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 Menggunakan Algoritma Apriori: Kasus Transaksi 212 Mart Soebrantas Pekanbaru

2021 ◽  
Vol 5 (1) ◽  
pp. 31-40
Author(s):  
Deni Rizaldi ◽  
Arisman Adnan
Keyword(s):  
Data Mining ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Minimum Support ◽  
Body Care

Market Basket Analysis (MBA) merupakan salah satu teknik penemuan aturan asosiasi dalam data mining. MBA memanfaatkan data transaksi pada suatu toko untuk menentukan strategi penjualan. Konsep utama analisis ini adalah menentukan barang yang dibeli secara bersamaan oleh konsumen. Penentuan asosiasi dalam MBA berdasarkan kriteria minimum support dan confidence. Penelitian ini menggunakan algoritma apriori untuk data transaksi 212 Mart Soebrantas Pekanbaru periode Januari-Desember 2020. Algoritma apriori merupakan algoritma yang efisien untuk menentukan kandidat aturan asosiasi pada data dengan jumlah besar. Aturan asosiasi yang akan dibangkitkan adalah aturan asosiasi antar kelompok item dan asosiasi antar item. Berdasarkan hasil analisis ditemukan aturan asosiasi antar kelompok yang terbaik berdasarkan nilai lift tertinggi yaitu asosiasi antara clothing care dan body care dengan support 6,1% dan confidence 45,88 %. Aturan asosiasi terbaik untuk item yaitu asosiasi Lemonilo Mie Instan Ayam Bawang 7 dan Lemonilo Mie Instan Kari Ayam dengan support 0,17% dan confidence 42,11%.

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.

IMPLEMENTASI APLIKASI DATA MINING PADA APOTEK KIMIA FARMA BAHTERAMAS MENGGUNAKAN ALGORITMA APRIORI

2017 ◽  
Vol 2 (1) ◽  
pp. 87-95
Author(s):  
Jayadi Jayadi ◽  
Andi Patombongi
Keyword(s):  
Data Mining ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Minimum Support ◽  
Data History ◽  
Database Support

Apotek Kimia Farma juga sudah menerapkan aplikasi dalam sistem penjualannya, seiring dengan berjalannya waktu data yang dihasilkan aplikasi penjualan pada apotek semakin melimpah dan membuat tumpukan data yang tidak bermanfaat, Sehingga dibutuhkan aplikasi yang dapat mempermudah pihak apotek dalam menganalisis data tranksaksi tersebut. Metode yang digunakan dalam pembuatan aplikasi Data mining yaitu metode MBA (market basket analysis ), dengan bantuan Algoritma Apriori. Proses yang dilakukan dalam implementasi Algoritma Apriori yaitu dengan cara mengambil data history penjualan dari Apotek Kimia Farma, kemudian menghitung nilai persentase tiap barang yang dibeli dalam database (support ), selanjutnya memangkas data yang tidak memenuhi syarat dari nilai minimum support, setelah semua pola frekuensi tinggi ditemukan, barulah dicari aturan asosisasi yang memenuhi syarat minimum confidence. Hasil dari aplikasi yang menggunakan teknik Data Minig dan Algoritma Apriori ini yaitu mampu menampilkan pola pembeliaan konsumen dengan menganalisa data transaksi yang ada, dan membantu pihak apotek untuk mengetahui pola konsumsi konsumen sehingga dapat meningkatkan strategi penjualan.

scholarly journals MARKET BASKET ANALYSIS (MBA) PADA SITUS WEB E-COMMERCE ZAKIYAH COLLECTION

2016 ◽  
Vol 7 (2) ◽  
pp. 459
Author(s):  
Ari Muzakir ◽  
Laili Adha
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Product Delivery ◽  
Customer Profiles

E-commerce menghubungkan antara produsen dengan produsen, produsen dengan konsumen, konsumen dengan produsen, konsumen dengan konsumen. Untuk mengimplementasi e-commerce dalam mendukung bisnis organisasi perlu di perhatikan 5 komponen utama yaitu ; pengembangan produk, promosi, transaksi online, product delivery dan after sales support. Hal ini yang tengah diterapkan pada Zakiyah Collection. Zakiyah Collection bergerak dibidang penjualan aneka macam kain khas Palembang seperti songket, blongket,tanjung, dan lain sebagainya. Untuk melakukan analisis terhadap pangsa pasar yang ada agar dapat bersaing dengan toko online lainnya dilakukan dengan strategi pemasaran dengan menggunakan pendekatan market basket analysis (MBA). MBA merupakan salah satu teknik dari data mining yang digunakan untuk menentukan produk-produk manakah yang akan dibeli oleh pelanggan secara bersamaan dengan melakukan analisa terhadap daftar transaksi pelanggan. Dengan mengetahui produk-produk tersebut, maka sebuah sistem e-commerce dapat membuat maupun mengembangkan sebuah sistem customer profiles dan dapat menentukan layout katalog pelanggannya sendiri. Model pengembangan sistem yang dilakukan menggunakan prototype dimana pelanggan dan pengguna akan dilibatkan secara langsung dalam proses ini. Hasil akhir dalam penelitian ini adalah berupa analisis data transaksi menggunakan market basket analysis dengan dilakukan 4 kali kombinasi produk yang berdasarkan nilai support x confidence terbesar dengan hasil berupa angka-angka kemungkinan transasksi yang berkaitan dengan produk yang dijual. Jika dengan menggunakan 1 kali kombinasi, maka didapatkan blongket dengan nilai support sebesar 0.5625. Jika dilakukan 2 kali kombinasi diperoleh kombinasi blongket dan songket dengan nilai support 0.375. Kata kunci: e-commerce, market basket analysis, association 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.

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