scholarly journals KAJIAN PERILAKU BELANJA KONSUMEN MENGGUNAKAN TEKNIK ASOSIASI DI SUPERMARKET (STUDI KASUS: TOSERBA X). [STUDY OF CONSUMER SHOPPING BEHAVIOR USING ASSOCIATION TECHNIQUES IN SUPERMARKET (CASE STUDY: TOSERBA X)]

2020 ◽  
Vol 15 (2) ◽  
pp. 282
Author(s):  
Ronny Samsul Bahri ◽  
Laura Lahindah
Keyword(s):  
Data Mining ◽  
Shopping Behavior ◽  
Mining Method ◽  
Consumer Spending ◽  
Market Basket Analysis ◽  
Association Pattern ◽  
Use Of Data ◽  
Spending Patterns ◽  
Bahasa Indonesia

<p><em>The development of retail companies in Indonesia is quite rapid causing the need for the use of data as a basis for decision making. As one of the developing retail stores, the floor display pattern has not been well managed and has not been linked to the pattern of consumer spending. Market basket analysis is one of the data mining method techniques to determine the association of consumer spending patterns in a purchase transaction. This study aims to determine whether there is an association pattern in each term of consumer spending in five divisions of supermarket products (all divisions, food, non-food, household or GMS &amp; fresh). The term is divided into three, namely, term I (1-10), term II (11-20) and term III (21-month end). The data is processed using software Rapidminer version 5. The data processing results show an association relationship in several terms, namely all divisions in term I have influence, term II has no influence, term III has influence. Food division in term I has an influence, term II has no influence, term III has an effect. The nonfood division in term I has no influence, term II has no influence, term III has no effect. The GMS division in term I has no influence, term II has no influence, term III has no effect. The fresh division in term I has influence, term II has influence, term III has no effect. By using the results of the analysis, floor display and promotion patterns can be adjusted according to the consumer's shopping patterns.</em><strong> </strong></p><p><strong>Abstrak dalam Bahasa Indonesia.</strong>Perkembangan perusahaan ritel di Indonesia yang cukup pesat menyebabkan perlunya pemanfaatan data sebagai dasar dalam pengambilan keputusan.  Sebagai salah satu toko ritel yang sedang berkembang, pola pemajangan floor diplay belum dikelola dengan baik dan belum dikaitkan dengan pola belanja konsumennya.  M<em>arket basket analysi</em><em>s merupakan salah satu teknik metoda</em> <em>data mining</em> untuk menentukan hubungan asosiasi pola belanja kosumen dalam suatu transaksi pembelian.  Penelitian ini bertujuan untuk mengetahui apakah terdapat pola asosiasi pada setiap termin pembelanjaan konsumen pada lima divisi produk supermarket (seluruh divisi, food, nonfood, household atau GMS &amp; fresh). Termin terbagi menjadi tiga yaitu, termin I (tanggal 1-10), termin II (tanggal 11-20) dan termin III (tanggal 21-akhir bulan).  Data diolah dengan menggunakan Software Rapidminer versi 5. Hasil pengolahan data menunjukkan adanya hubungan asosiasi pada beberapa termin yaitu Seluruh divisi dalam termin I ada pengaruh, termin II tidak ada pengaruh, termin III ada pengaruh. Divisi food dalam termin I ada pengaruh, termin II tidak ada pengaruh, termin III ada pengaruh.  Divisi nonfood dalam termin I tidak ada pengaruh, termin II tidak ada pengaruh, termin III tidak ada pengaruh. Divisi GMS dalam termin I ada pengaruh, termin II tidak ada pengaruh, termin III tidak ada pengaruh. Divisi fresh dalam termin I ada pengaruh, termin II ada pengaruh, termin III tidak ada pengaruh. Dengan menggunakan hasil analisis, pola pemajangan floor display dan promosi dapat diselaraskan sesuai dengan pola belanja konsumen tersebut.</p>

scholarly journals Análise do banco de atendimentos da Defesa Civil do Estado de São Paulo

2010 ◽  
Vol 33 (1) ◽  
pp. 35-43
Author(s):  
Diego José Chagas ◽  
Chou Sin Chan ◽  
Alessandra Cristina Corsi
Keyword(s):  
Data Mining ◽  
Sao Paulo ◽  
Data Organization ◽  
São Paulo ◽  
Data Mining Technique ◽  
Landslide Occurrence ◽  
Serra Do Mar ◽  
Use Of Data ◽  
Relevant Factors

In recent years the simple data organization is no longer a differential factor for institutions, since, depending on their volume, the traditional method of analysis and interpretation is extremely slow and costly. The use of data mining techniques is an alternative to allow this process semi-automatic. The objective of this work is to carry out a case study of data mining technique based on the WEKA software applied to hydrometeorological and geomorphological data which were collected in the Serra do Mar region of São Paulo State. Results obtained from the application of the association technique indicate that the presence of rock and boulders at terrains with scars and high declivity are relevant factors for the landslide occurrence.

scholarly journals Application Of Pizza Sales Data Mining Using Apriori Method

SinkrOn ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 1 ◽  
Author(s):  
Rusdiansyah Rusdiansyah ◽  
Nining Suharyanti ◽  
Triningsih Triningsih ◽  
Muhammad Darussalam
Keyword(s):  
Data Mining ◽  
Data Collection ◽  
Association Rules ◽  
A Priori ◽  
Apriori Algorithm ◽  
Mining Method ◽  
Processed Food ◽  
Sales Data ◽  
Business Opportunity ◽  
Use Of Data

Pizza is a processed food originating from Italy and has been spread in various other countries including one of them in Indonesia. Pizza is a processed food that is currently sought after by various groups of people so as to make the pizza business opportunity very profitable, if it is run in a food business. Currently the pizza business has very favorable prospects when compared to other businesses. Moreover, the targeted target can be from all walks of life from children to adults. Pizza sales transactions that produce sales data every day, have not been able to maximize the use of sales data. Sales data is only stored as an archive, so it becomes a pile of data. Therefore the use of data mining is used to solve this problem. A priori algorithm is a data mining method by using minimum support parameters, minimum confidence and will analyze in the period of every month of sales transactions. This study produces data on the results of the process of association rules from the data collection of sales transactions. From the association rules it can be concluded that the pattern of pizza sales, where consumers more often buy Meatzza and Cheese Mania, as evidenced by the results of calculations using Apriori Algorithm and Rapidminer 5.3, with support of 30% and 60% confidence.

scholarly journals PENERAPAN DATA MINING MENGGUNAKAN ALGORITMA C4.5 TEHADAP PENGARUH PENJUALAN KOPI PADA PT. JPW INDONESIA

2020 ◽  
Vol 3 (1) ◽  
pp. 40-54
Author(s):  
Ikong Ifongki
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Decision Rules ◽  
Large Data ◽  
Added Value ◽  
Data Set ◽  
Use Of Data ◽  
Decision Tree Classification ◽  
C4.5 Algorithm

Data mining is a series of processes to explore the added value of a data set in the form of knowledge that has not been known manually. The use of data mining techniques is expected to provide knowledge - knowledge that was previously hidden in the data warehouse, so that it becomes valuable information. C4.5 algorithm is a decision tree classification algorithm that is widely used because it has the main advantages of other algorithms. The advantages of the C4.5 algorithm can produce decision trees that are easily interpreted, have an acceptable level of accuracy, are efficient in handling discrete type attributes and can handle discrete and numeric type attributes. The output of the C4.5 algorithm is a decision tree like other classification techniques, a decision tree is a structure that can be used to divide a large data set into smaller sets of records by applying a series of decision rules, with each series of division members of the resulting set become similar to each other. In this case study what is discussed is the effect of coffee sales by processing 106 data from 1087 coffee sales data at PT. JPW Indonesia. Data samples taken will be calculated manually using Microsoft Excel and Rapidminer software. The results of the calculation of the C4.5 algorithm method show that the Quantity and Price attributes greatly affect coffee sales so that sales at PT. JPW Indonesia is still often unstable.

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 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.

scholarly journals Application of Data Mining Method Using Association Rules Apriori To Shopping Cart Analysis On Sale Transactions (Case Study Alfamidi Burnt Stone)

2020 ◽  
Vol 2 (2) ◽  
pp. 222-226
Author(s):  
Vanessa Siregar ◽  
Paska Marto Hasugian
Keyword(s):  
Data Mining ◽  
Knowledge Discovery In Databases ◽  
Data Sets ◽  
Cart Analysis ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
Large Size ◽  
Using Data ◽  
Mining Algorithms

Also Often data mining is called knowledge discovery in databases (KDD), ie activities include the collection, historical use of data to find regularities, patterns or relationships in data sets with a large size. The company may be interested to know if some groups consistently goods items purchased together. This study analyzes the transaction of data information retrieval from the sale of skin care and hair care using data mining algorithms priori Alfamidi Burnt Stones with the highest support value is 8% and the highest value is 5% confidance

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