scholarly journals Market Basket Analysis for Books Sales Promotion using FP Growth Algorithm, Case Study : Gramedia Matraman Jakarta

2021 ◽  
Vol 4 (2) ◽  
pp. 383-392
Author(s):  
Firmansyah Firmansyah ◽  
Agus Yulianto
Keyword(s):  
Data Mining ◽  
Large Data ◽  
Sales Promotion ◽  
Frequent Pattern ◽  
Analysis Method ◽  
Analysis Framework ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Industry Standard

For retail companies such as Gramedia stores, promotion and strategies to sell books are important, so tools are needed to analyze past sales data. Gramedia does not yet have tools to analyze shopping cart patterns that aim to carry out product promotions appropriately. To promote what books should be promoted using the market basket analysis method or shopping basket analysis. The algorithm used in the data mining process is Frequent Pattern Growth (FP Growth) because it is faster in processing large data. The data analyzed is historical data on book sales from January to March 2020 which is taken randomly (random sampling). The framework used in the data mining process is the Cross Industry Standard Process for Data Mining (CRISP-DM) and the tool used is the Rapid Miner using a market basket analysis framework. With a minimum support of 0.003 and a minimum confidence 0.3 using the FP-Growth algorithm to produce an item set of 7 rules to recommend product promotions. The algorithm results are also in accordance with the business understanding phase of CRISP-DM.

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.

scholarly journals Market Basket Analysis as a Support Tool for The Management of Public Transport

2014 ◽  
Vol 37 (1) ◽  
pp. 219-237
Author(s):  
Dorota Sokołowska
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Public Transport ◽  
Analysis Method ◽  
Market Basket Analysis ◽  
Support Tool ◽  
Market Basket ◽  
City Area ◽  
Communication Lines ◽  
The City

Abstract The aim of this paper is to characterize a non-standard use of the method of market basket analysis in one of the areas of economy, i.e. public transport. Generally, one of the aims of the market basket analysis method is associating the consumer's market basket – in the case of public transport this being the choice of bus stops in the city area made by passengers. Owing to a new, practical use of this method, it was possible to build an efficient model characterizing the movement of flows of public transport passengers, and assess the degree of transferring (changing lines), thus making it possible to adapt the routes of buses to the needs of people using this particular means of transport, as well as to plot new communication lines. The data analysis was performed using the Statistica statistical package and its SAL application, i.e. the algorithms used in Data Mining.

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 Market Basket Analysis untuk Swalayan KSU Sumber Makmur dengan Algoritma FP Growth

2021 ◽  
Vol 2 (1) ◽  
pp. 34-39
Author(s):  
Ramadhan Ramadhan ◽  
Esther Irawati Setiawan
Keyword(s):  
Data Mining ◽  
Frequent Itemset ◽  
Frequent Pattern ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Pattern Growth ◽  
Association Data

Salah satu teknik data mining yang populer digunakan adalah association data mining atau yang biasa disebut dengan istilah market basket analysis. Market basket didefinisikan sebagai suatu itemset yang dibeli secara bersamaan oleh pelanggan dalam suatu transaksi. Market basket analysis adalah suatu sarana untuk meningkatkan penjualan. Metode ini dimulai dengan mencari sejumlah frequent itemset dan dilanjutkan dengan pembentukan aturan-aturan asosiasi. Algoritma Apriori dan frequent pattern growth adalah dua algoritma yang sangat populer untuk menemukan sejumlah frequent itemset dari data-data transaksi yang tersimpan dalam basis data. Dalam penelitian ini algoritma frequent pattern growth (FP Growth) digunakan untuk menemukan sejumlah aturan asosiasi dari basis data transaksi penjualan di Swalayan KSU Sumber Makmur (Trenggalek). Dari hasil pengolahan data didapatkan pola pembelian paling kuat berupa jika membeli pasta gigi maka dimungkinkan juga akan membeli sabun dan jika membeli shampo juga akan membeli sabun dengan tingkat keyakinan (confidence) 63% dan 62%.

MODEL SISTEM INFORMASI PEMASARAN SEBAGAI PENUNJANG KEPUTUSAN PROMOSI PRODUK MENGGUNAKAN ALGORITMA MAX -MINER

ICIT Journal ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 94-104
Author(s):  
Fernando Siboro ◽  
Capri Eriansyah ◽  
Muhammad Adi Sofyan
Keyword(s):  
Data Mining ◽  
Life Cycle ◽  
Market Basket Analysis ◽  
Market Basket

Teknologi informasi saat ini terus berkembang semakin cepat, membuat pola berfikir manusia berubah, dengan proses pertumbuhan yang seperti ini, generasi akan datang diharuskan mempunyai keahlian yang lebih baik di bidang pemanfaatan teknologi informasi. Kebutuhan adanya kemudahan dari segi pemasaran, saat ini dirasa sangat penting, terutama bagi perusahaan yang bergerak dibidang penjulan atau distributor guna menunjang meningkatkan akurasi dan kualitas pemasaran itu sendiri. Namun pada kenyataanya, sistem yang berjalan masih tergolong kurang efektif dan efesien dalam melayani kebutuhan pelanggan, hal ini dikarenakan sistem pemasaran produk hanya bisa diakses secara manual, dan belum adanya media informasi seputar produk yang ditawarkan, oleh sebab itu dibuatlah suatu perancangan sistem informasi yang mengatur pemasaran produk dan dapat menjadi bahan dalam pembuatan laporan sistem penunjang keputusan. Dalam perancangan ini menggunakan metode data mining market basket analysis dan Max-Miner sebagai algoritma. Serta menggunakan metode penerapan sistem waterfall atau sering dinamakan siklus hidup klasik (classic life cycle). Dengan demikian rancang bangun sistem informasi ini, mengacu kepada bagaimana cara agar pemasaran produk dapat di akses dengan mudah, cepat, dan akurat dimanapun dan kapanpun, calon customer dapat mengakses tanpa terkendala waktu dan tempat, serta menjadi wadah dalam pengambilan keputusan oleh perusahaan. Metodologi desain menggunakan uml yang melimuti usecase, activity, squence dan untuk pengelolaan basis data menggunakan mysql. Sistem ini diharapkan mampu dijadikan salah satu penunjang keputusan untuk kebutuhan promosi produk. Kata Kunci: Penunjang pemasaran, promosi produk, algoritma Max-Miner

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.

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