scholarly journals Perancangan Aplikasi Data Mining Transaksi Penjualan untuk Mengetahui Pola Beli Konsumen pada Toko Singgalang Padang Menggunakan Algoritma Apriori Berbasis Web

2018 ◽  
pp. 37-44
Author(s):  
Nelisa ◽  
Aulia Fitrul Hadi
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Association Analysis ◽  
Frequent Itemset ◽  
Business Decision ◽  
Transaction Data ◽  
Analysis Application ◽  
Two Parameters

It takes a method or technique that can transform mountains of data into a valuable information or knowledge (knowledge) that are useful to support business decision making. Therefore in this paper association analysis application developed for extracting and interpreting the pattern of trend of sales of goods are often sold simultaneously from the transaction data using algorithms apriori. Apriori algorithms will from a frequent itemset as predetermined based on two parameters, support and confidence, to find the rules of the association between a combination of items. Knowledge of a product can be used by companies to increase production ansd sales of a product..

scholarly journals Analisis Pola Pembelian Obat di Apotek Sekar Adi Menggunakan Metode Algoritma Apriori Depok

2020 ◽  
Vol 4 (1) ◽  
pp. 112
Author(s):  
Siti Awaliyah Rachmah Sutomo ◽  
Frisma Handayanna
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Association Rules ◽  
High Frequency ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Mining Methods ◽  
Using Data ◽  
Purchase Patterns

By using data mining methods can be processed to obtain information and assist in decision making, the amount of data on sales transactions in each drug purchase can cause a data accumulation and various problems, such as drug stock inventory, and sales transaction data, with Data mining techniques, the behavior of consumers in making transactions of drug purchase patterns can be analyzed, It can be known what drugs are commonly purchased by mostly people, the application of Apriori Algorithm is expected to help in forming a combination of itemset. The process of determining drug purchase patterns can be carried out by applying the Appriori algorithm method, determination of drug purchase patterns can be done by looking at the results of the consumer's tendency to buy drugs based on a combination of 3 itemset. By calculating the Analysis of High Frequency Patterns and the Formation of Association Rules, with a minimum of 30% support, there is a combination of 3 itemsset namely MOLAGIT PER TAB (M1), VIT C TABLET (V2), and PARACETAMOL 500 MG TABLET (P2) with 33.33 % support results obtained, and with minimum confidence of 65% there are 6 final association rules.

Research on Customer Value Evaluation Based on Data Mining and Mathematical Statistics

2014 ◽  
Vol 926-930 ◽  
pp. 3890-3893
Author(s):  
Bin Yang
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Customer Value ◽  
Customer Relationship ◽  
Management Decision ◽  
Value Assessment ◽  
Business Decision ◽  
Value Evaluation ◽  
Customer Information ◽  
Management Decision Making

In business you can get a number of data about customer information. How to find useful information for business decision-making from so many complicated, messy data and then to perform customer value assessment is a very important and complicated process. In this paper, data mining topics is identified as customer value assessment to assess customer value through data mining and statistical methods, in order to support the company's marketing decision making and customer relationship management decision making.

scholarly journals Data warehousing and data mining: A case study

2005 ◽  
Vol 15 (1) ◽  
pp. 125-145 ◽  
Author(s):  
Milija Suknovic ◽  
Milutin Cupic ◽  
Milan Martic ◽  
Darko Krulj
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Warehouse ◽  
Business Decision ◽  
Serbia And Montenegro ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
Business System ◽  
Time Period ◽  
Mining Algorithms

This paper shows design and implementation of data warehouse as well as the use of data mining algorithms for the purpose of knowledge discovery as the basic resource of adequate business decision making process. The project is realized for the needs of Student's Service Department of the Faculty of Organizational Sciences (FOS), University of Belgrade, Serbia and Montenegro. This system represents a good base for analysis and predictions in the following time period for the purpose of quality business decision-making by top management. Thus, the first part of the paper shows the steps in designing and development of data warehouse of the mentioned business system. The second part of the paper shows the implementation of data mining algorithms for the purpose of deducting rules, patterns and knowledge as a resource for support in the process of decision making.

Decision-making framework with double-loop learning through interpretable black-box machine learning models

2017 ◽  
Vol 117 (7) ◽  
pp. 1389-1406 ◽  
Author(s):  
Marko Bohanec ◽  
Marko Robnik-Šikonja ◽  
Mirjana Kljajić Borštnar
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Making ◽  
Organizational Learning ◽  
Decision Making Process ◽  
Organizational Setting ◽  
Business Decision ◽  
Content Type ◽  
Industry Standard ◽  
General Explanation

Purpose The purpose of this paper is to address the problem of weak acceptance of machine learning (ML) models in business. The proposed framework of top-performing ML models coupled with general explanation methods provides additional information to the decision-making process. This builds a foundation for sustainable organizational learning. Design/methodology/approach To address user acceptance, participatory approach of action design research (ADR) was chosen. The proposed framework is demonstrated on a B2B sales forecasting process in an organizational setting, following cross-industry standard process for data mining (CRISP-DM) methodology. Findings The provided ML model explanations efficiently support business decision makers, reduce forecasting error for new sales opportunities, and facilitate discussion about the context of opportunities in the sales team. Research limitations/implications The quality and quantity of available data affect the performance of models and explanations. Practical implications The application in the real-world company demonstrates the utility of the approach and provides evidence that transparent explanations of ML models contribute to individual and organizational learning. Social implications All used methods are available as an open-source software and can improve the acceptance of ML in data-driven decision making. Originality/value The proposed framework incorporates existing ML models and general explanation methodology into a decision-making process. To the authors’ knowledge, this is the first attempt to support organizational learning with a framework combining ML explanations, ADR, and data mining methodology based on the CRISP-DM industry standard.

scholarly journals PENERAPAN DATA MINING MENGGUNAKAN ALGORITMA K-MEANS UNTUK MENGETAHUI MINAT CUSTOMER DI TOKO HIJAB

2019 ◽  
Vol 15 (2) ◽  
pp. 241-246
Author(s):  
Yulianti Yulianti ◽  
Dwi Yuni Utami ◽  
Noer Hikmah ◽  
Fuad Nur Hasan
Keyword(s):  
Data Mining ◽  
Transaction Data ◽  
Business People ◽  
Sales Strategy ◽  
Two Parameters

Hijab is not a foreign thing for the population in Indonesia, because most of the population of Indonesia is Muslim. Today, many business people, especially hijab sellers, provide a variety of brands and models in the hijab they sell. Therefore sellers are required to be able to think intelligently in making a sales strategy that will certainly be useful to know clearly which products are most in demand by customers, and also to increase sales in their stores. Then there needs to be an alternative that can realize the recording of sales transaction data more quickly and structured. In this study the authors applied the k-means algorithm to determine customer interest in the products they sell. In the calculation that has been done by using two parameters, namely the transaction and the number of sales and passing three iterations with the results of iterations one gets a ratio of 0.374324132, the iteration two gets the ratio 0.543018325, and the iteration three gets the same ratio value as second iteration. So it can be concluded that the hijab that is most desirable by the customers is the hijab with the brand Rabbani, Elzatta, and Zoya, the low-interest hijab branded by Dian Pelangi, Kami Idea, and Meccanism. And the hijab with those who are not high and also not low is the hijab under the brand Ria Miranda, Jenahara, Shasmira, and Shafira.

scholarly journals Sales Transaction Data Analysis using Apriori Algorithm to Determine the Layout of the Goods

2018 ◽  
Vol 1 (1) ◽  
pp. 16-23
Author(s):  
Taqwa Hariguna ◽  
Uswatun Hasanah ◽  
Nindi Nofi Susanti
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Association Rules ◽  
High Frequency ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Sales Strategy ◽  
Processing Data ◽  
Using Data

In a shop, usually apply a sales strategy in order. The sales strategy can be in the form of determining the layout of goods so that they are close to one another. Determining the layout of items can be based on items that are often purchased simultaneously. Searching for items that are often purchased together can be done using data mining techniques, which is processing data to become more useful information. Sales transaction data processing can be done using apriori algorithm. Apriori algorithm is the most famous algorithm for finding high-frequency patterns and generating association rules. From the results of the discussion and data analysis, there were 3 (three) association rules formed, namely "If you buy Milo Active 18 grm, then buy ABC Kopi Susu 31G" with support 0.36% and 75% confidence, "If you buy Dancow 1 + Honey 200 grm, then buy Ice Cream Corneto" wit H Support 0.36% and confidence 60%, "If you buy SIIP Roasted 6.5 grm, then buy Davos Strong 10 grm" with support 0.36% and 75% confidence. From the association's rules can be used as decision making to determine the layout of goods that are likely to be purchased simultaneously by the buyer

scholarly journals PENERAPAN ASSOCIATION RULE DATA MINING UNTUK REKOMENDASI PRODUK KOSMETIK PADA PT. FABIANDO SEJAHTERA MENGGUNAKAN ALGORITMA APRIORI

ALGOR ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Febri Antho ◽  
Dram Renaldi ◽  
Edy ◽  
Yakub
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Association Rule ◽  
Policy Decision ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Cosmetic Product ◽  
Product Recommendations ◽  
Using Data ◽  
Product Sales

In some companies that have sales transaction data and this data will increase from day to day so that it will accumulate and become garbage if it is not managed and utilized properly. Sales transaction data is one thing that can be used to increase product sales. Not only to increase product sales but also to provide product recommendations for each sale. As in the product stock setting section, it can provide recommendations for the number of products so that problems such as over stock will not occur which will cause the amount in a product to expire. In this study, an association rule data mining will be implemented for cosmetic product recommendations using the Apriori algorithm. Testing the results of using data mining and the Apriori algorithm is carried out to find out that the results of the study can find association rules from existing datasets to recommend cosmetic products. The association rule method is used in the search for product attachment patterns for sales strategies in policy decision making. So that it can be seen that the cosmetics that are often purchased by consumers, based on the rules generated from the data contained in the database. Tests were carried out using the Rapidminer 9.5 application. The results obtained from this test are that there are 16 rules (rules) that will be used for decision making in cosmetic product recommendations.

scholarly journals Penerapan Metode Association Rule Dalam Menganalisa Data Penjualan Obat Mengunakan Algoritma FP-GROWTH (Studi Kasus Rumah Sakit Haji Medan)

2019 ◽  
Vol 3 (2) ◽  
pp. 9
Author(s):  
Ibnu Rusydi
Keyword(s):  
Data Mining ◽  
Data Structure ◽  
Association Rule ◽  
Hospital Pharmacy ◽  
Business Processes ◽  
Frequent Itemset ◽  
Transaction Data ◽  
New Knowledge ◽  
Transaction Database ◽  
Tree Development

<p><em>Sales transaction data is a very valuable asset in business processes. Not only is it used to calculate profits and money, but large amounts of transaction data can also be used for various purposes to generate new knowledge (knowledge) in the transaction database. Ways that can be done for data processing and generate new knowledge from the data is to use data mining techniques. The technique used in this case is the FP-Growth Algorithm. The data structure used is a tree called FP-Tree. By using FP-Tree, FP-growth Algorithm can directly extract Itemset from FP-Tree. Research conducted by collecting data related to research in the case studio at Medan Haji Hospital Pharmacy where the variables taken are daily drug transaction data. The results of this study are part of the new knowledge of this sales data by applying the FP-Growth Algorithm that uses the concept of FP-Tree development in finding Frequent Itemset that is useful for the development of investment plans in the study areas taken.</em></p><p><em> </em></p><p><em>Keywords: Data Mining, Association Rules, Frequent Itemset, FP-Growth.</em></p>

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