scholarly journals Data Mining Penjualan Produk Dengan Metode Apriori Pada Indomaret Galang Kota

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Sheih Al Syahdan ◽  
Anita Sindar
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Product Placement ◽  
Data Mining Technique ◽  
Frequency Pattern ◽  
Food And Beverage ◽  
Minimum Requirements ◽  
Analytical Tools ◽  
Confidence Value ◽  
Problem Data

<p>The large number of transactions, companies need analytical tools to provide information that is useful for the company in determining the layout of goods, what items are most in demand by consumers and others. As experienced by several other supermarkets, product placement is a major problem. Data mining is a technique for digging up information that is hidden or hidden. This study will identify several types of association rules relating to sales transaction data, namely support and confidence values. The data used are 25 food and beverage products. Data mining technique uses associative rule with the Apriori method, aims to find a combination of items with a frequency pattern of the transaction results. After all high frequency patterns are found, then the association rules that meet the minimum requirements are found for confidence associative rules A → B minimum confidence = 25%, confidence value of A → B rules.</p>

scholarly journals Implementation Of Data Mining On Suzuki Motorcycle Sales In Gemilang Motor Prosperous With Apriori Algorithm Method

2020 ◽  
Vol 2 (1) ◽  
pp. 23-29
Author(s):  
Anju Eliarsyam Lubis ◽  
Paska Marto Hasugian
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Strong Correlation ◽  
High Frequency ◽  
Efficient Algorithm ◽  
Pattern Analysis ◽  
Apriori Algorithm ◽  
Frequency Pattern ◽  
One Stage ◽  
Confidence Value

The sale is part of the marketing that determine the survival of the company. With the sale, the company can achieve the goals or targets. To be a company that continues to grow in motorcycle sales, the company should be able to compete in increasing sales volume. Starting from the launch prodak the best in sophistication motorcycles, up to a very attractive price cuts the attention of consumers. Things like that already sanggat often do, so the company can still compete, Motorcycles is a two-wheeled transfortasi tool used more and more common people. From teenagers to old orag, not infrequently motorcycle including important sanggat needs. If we do not have it feels very hard in activity quickly. Make sales without any restriction of sales data accumulate, until finally overwhelmed the company in terms of taking care of customer files. To find the most sales required Apriori Algorithm. Apriori algorithm, including the type of association rules on Data Mining. One stage of association that can produce an efficient algorithm is with high frequency pattern analysis. In an association can be determined by two benchmarks, namely: Support and Confidence. Support "penunang value" is the percentage of combinations of items in a database, and Confidence "value certainty" is strong correlation between the items in an association's rules. Apriori algorithm, including the type of association rules on Data Mining. One stage of association that can produce an efficient algorithm is with high frequency pattern analysis. In an association can be determined by two benchmarks, namely: Support and Confidence. Support "penunang value" is the percentage of combinations of items in a database, and Confidence "value certainty" is strong correlation between the items in an association's rules. Apriori algorithm, including the type of association rules on Data Mining. One stage of association that can produce an efficient algorithm is with high frequency pattern analysis. In an association can be determined by two benchmarks, namely: Support and Confidence. Support "penunang value" is the percentage of combinations of items in a database, and Confidence "value certainty" is strong correlation between the items in an association's rules.

scholarly journals Precipitation Prediction for South West Monsoon Over Karnataka using Supervised Learning Technique

2019 ◽  
Vol 8 (3) ◽  
pp. 4450-4454
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Weather Forecasting ◽  
Grid Point ◽  
Time Series Prediction ◽  
Data Mining Technique ◽  
Data Framework ◽  
South West ◽  
South West Monsoon ◽  
West Monsoon

Weather forecasting is a major field of study in the area of Meteorology. Data Scientists, meteorologists and weather forecasters are implementing the experimentation of weather forecasting base on numerical and statistical methods. Traditional models used the fluid and thermal dynamic strategies for grid-point time series prediction based on few inherited constraints, such as the adoption of incomplete boundary rules, model assumptions and numerical instabilities. The nominated work is focused on finding the south west monsoon months’ precipitation patterns over the specific stations of Karnataka State. A multi-dimensional data framework for climate database with implementation online based data analysis has been developed. This works is carried out on the basis of monsoons that have prevailed during a year for the past 10 years. The proposed model emphasis the implementation of the association rules which has been extracted by the supervised classifier approach of data mining algorithms. The data mining technique of association rules emphasis the occurrence of the precipitation and will be helpful to take decisions in advance to the day to day operations in business, agriculture, water management and etc.

scholarly journals IMPLEMENTASI DATA MINING UNTUK MENGETAHUI POLA PEMBELIAN OBAT MENGGUNAKAN ALGORITMA APRIORI

2020 ◽  
Vol 17 (2) ◽  
pp. 396-402
Author(s):  
Nadya Febrianny Ulfha ◽  
Ruhul Amin
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
A Priori ◽  
Marketing Strategies ◽  
Business World ◽  
Association Pattern ◽  
Green Lake ◽  
Purchasing Patterns ◽  
The Relationship ◽  
Confidence Value

Competition in the business world requires entrepreneurs to think of finding a way or method to increase the transaction of goods sold. The purpose of this research is to provide drug stock data that is widely purchased by pharmacy customers at Kimia Farma, Green Lake branch in Jakarta. The algorithm used in this study is a priori to determine the relationship between the frequency of sales of drug brands most frequently purchased by customers. The association pattern formed with a minimum support of 40% and a minimum value of 70% confidence produces 17 association rules. The strong rules obtained are that if you buy a 500Mg Ponstan KPL @ 100, you will buy an Incidal OD 10Mg Cap with a support value of 59% and a confidence value of 84%. A priori algorithm can be used by companies to develop marketing strategies in marketing products by examining consumer purchasing patterns.

scholarly journals The analysis of agricultural data with regression data mining technique

2015 ◽  
Vol 37 ◽  
pp. 102 ◽  
Author(s):  
Hooman Fetanat ◽  
Leila Mortazavifarr ◽  
Narsis Zarshenas
Keyword(s):  
Data Mining ◽  
Process Model ◽  
Relational Databases ◽  
Ornamental Plants ◽  
Regression Technique ◽  
Data Mining Technique ◽  
Independent Variables ◽  
Mining Technique ◽  
Analytical Tools

Data mining is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. This paper discusses data mining technique such as regression and clustering which is a process model for analyzing data and describes the support that SPSS provides for this model. SPSS-based analysis and application construction process is illustrated through a case study in the agricultural domain-ornamental plants. Cluster analysis or clustering was used is the task of assigning a set of objects into groups so that the objects in the same cluster are more similar to each other than to those in other clusters. In this survey clustering technique divides growth factors into several independent categories. Also, regression technique which was used includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. In this research, analyzed data with regression technique showed the effect of chlorophyll content on the number of flowers.

scholarly journals Implementasi Algoritma Apriori Pada Penyusunan Menu Makanan Rumah Makan Prasmanan

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Asep Budiman Kusdinar ◽  
Daris Riyadi ◽  
Asriyanik Asriyanik
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
High Frequency ◽  
A Priori ◽  
Apriori Algorithm ◽  
Frequency Pattern ◽  
Data Mining Techniques ◽  
Rule Method

A buffet restaurant is a restaurant that provides buffet food that is served directly at the dining table so that customers can order more food according to their needs. This study uses the association rule method which is one of the methods of data mining and a priori algorithms. Data mining is the process of discovering patterns or rules in data, in which the process must be automatic or semi-automatic. Association rules are one of the techniques of data mining that is used to look for relationships between items in a dataset. While  the apriori algorithm is a very well-known algorithm for finding high-frequency patterns, this a priori algorithm is a type of association rule in data mining. High- frequency patterns are patterns of items in the database that have frequencies or support. This high-frequency pattern is used to develop rules and also some other data mining techniques. The composition of the food menu in the Asgar restaurant is now arranged randomly without being prepared on the food menu between one another. The result of this research is  to support the composition of the food menu at the Asgar restaurant so that it is easier to take food menu with one another.  

scholarly journals Prediction of Blood Donors Population Using Data Mining Classification Technique

2021 ◽  
pp. 25-27
Author(s):  
Aman Paul ◽  
Daljeet Singh
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Association Rules ◽  
User Interfaces ◽  
Relational Data ◽  
Easy Access ◽  
Data Mining Technique ◽  
Open Source Data ◽  
Using Data

Data mining is a technique that finds relationships and trends in large datasets to promote decision support. Classification is a data mining technique that maps data into predefined classes often referred as supervised learning because classes are determined before examining data. Different classification algorithms have been proposed for the effective classification of data. Among others, Weka is an open-source data mining software with which classification can be achieved. It is also well suited for developing new machine learning schemes. It allows users to quickly compare different machine learning methods on new datasets. It has several graphical user interfaces that enable easy access to the underlying functionality. CBA is a data mining tool which not only produces an accurate classifier for prediction, but it is also able to mine various forms of association rules. It has better classification accuracy and faster mining speed. It can build accurate classifiers from relational data and mine association rules from relational data and transactional data. CBA also has many other features like cross validation for evaluating classifiers and allows the user to view and to query the discovered rules.

scholarly journals Privacy preserving association rule hiding using border based approach

2021 ◽  
Vol 23 (2) ◽  
pp. 1137
Author(s):  
Suma B. ◽  
Shobha G.
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Sensitive Information ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Large Databases ◽  
Hidden Correlations ◽  
Rule Set

<div>Association rule mining is a well-known data mining technique used for extracting hidden correlations between data items in large databases. In the majority of the situations, data mining results contain sensitive information about individuals and publishing such data will violate individual secrecy. The challenge of association rule mining is to preserve the confidentiality of sensitive rules when releasing the database to external parties. The association rule hiding technique conceals the knowledge extracted by the sensitive association rules by modifying the database. In this paper, we introduce a border-based algorithm for hiding sensitive association rules. The main purpose of this approach is to conceal the sensitive rule set while maintaining the utility of the database and association rule mining results at the highest level. The performance of the algorithm in terms of the side effects is demonstrated using experiments conducted on two real datasets. The results show that the information loss is minimized without sacrificing the accuracy. </div>

Data to Knowledge-Based Transformation

2021 ◽  
Vol 13 (4) ◽  
pp. 141-152
Author(s):  
Fauzan Asrin ◽  
*Saide Saide ◽  
Silvia Ratna
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Industrial Sector ◽  
Growth Patterns ◽  
Advantages And Disadvantages ◽  
Knowledge Based ◽  
Data Mining Approach ◽  
Important Evidence ◽  
Confidence Value ◽  
Computer Maintenance

The objectives of this study is to analyze a large amount of data that often appears to create a knowledge base that can be utilized by firm to enhance their decision support system. The authors used the association rules with rapid miner software, data mining approach, and predictive analysis that contains various data exploration scenarios. The study provides important evidence for adopting data mining methods in the industrial sector and their advantages and disadvantages. Chevron Pacific Indonesia (CPI) has a type of computer maintenance activity. Currently, a numerous errors often occur due to the accuracy in computer maintenance which has a major impact on production results. Therefore, this study focuses on association rules using growth patterns that often appear on variables that have been determined into the algorithm (FP-growth) which results in knowledge with a 100% confidence value and a 97% support value. The value results of this study has support and trust are expected to become knowledge for top management in deciding evergreen IT-business routines.

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