scholarly journals Implementasi Data Mining Algoritma Apriori Pada Sistem Persediaan Bahan Bangunan Di Karang Sari

2021 ◽  
Vol 2 (2) ◽  
pp. 107-115
Author(s):  
Zahra Syahara ◽  
Rika Nur Adiha ◽  
Agus Perdana Windarto
Keyword(s):  
Data Mining ◽  
Building Materials ◽  
A Priori ◽  
Inventory System ◽  
Or Manager ◽  
Confidence Value

The development of construction in Indonesia is increasing rapidly, therefore building materials are needed to be used in building a construction. Every building materials store must have a transaction system and an inventory system. Whether it's an efficient or less efficient inventory system. For this reason, this research was conducted to help the owner or manager of a building shop to more easily determine the combination pattern of supplies and purchases of building goods. The a priori algorithm is used in this study because the a priori algorithm is suitable in connecting the itemset combination patterns. Therefore the a priori algorithm is suitable in determining the purchase combination pattern in order to get a good inventory system. And from the process carried out, a confidence value of 75% was obtained, not only that this research was also assisted by a priori algorithm supporting application, namely the Tanagra application.

scholarly journals Implementation of Apriori Algorithm Data Mining for Increase Sales

SinkrOn ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 17
Author(s):  
Reza Alfianzah ◽  
Rani Irma Handayani ◽  
Murniyati Murniyati
Keyword(s):  
Data Mining ◽  
Business Strategy ◽  
A Priori ◽  
Data Mining Algorithm ◽  
Sales Data ◽  
The Right ◽  
Or Organization ◽  
Product Sales ◽  
The Relationship ◽  
Confidence Value

Any company or organization that wants to survive needs to determine the right business strategy. The product sales data carried out by Lakoe Dessert Pondok Kacang will eventually result in a pile of data, so it is unfortunate if it is not re-analyzed. The products offered vary with a wide variety of products as many as 45 products, to find out the products with the most sales and the relationship between one product and another, one of the algorithms is needed in the data mining algorithm, namely the a priori algorithm to find out, and with the help of the Rapidminer 5 application, with a support value 2,4% and a confidence value 50%, products that customers often buy or are interested in can be found. This study used sales data for March 2020, which amounted to 209 transaction data. From the research, it was found that the item with the name Pudding Strawberry and Pudding Vanilla was the product most purchased by consumers. With knowledge of the most sold products and the patterns of purchasing goods by consumers, Lakoe Dessert Pondok Kacang can develop marketing strategies to market other products by analyzing the profits from selling the most sold products and anticipating running out or empty of stock or materials at a later date.

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 Penerapan Metode Data Mining Pada Point of Sale Berbasis Web Menggunakan Algoritma Apriori

2021 ◽  
Vol 5 (3) ◽  
pp. 1158
Author(s):  
Adam Firmansyah ◽  
M Iwan Wahyudin ◽  
Ben Rahman
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
A Priori ◽  
Point Of Sale ◽  
Minimum Support ◽  
Confidence Value

To be able to understand which products have been purchased by customers, it is done by describing the habits when customers buy. Use association rules to detect items purchased at the same time. This study uses an a priori algorithm to determine the association rules when buying goods. The results of the study and analyzing the data obtained a statement that using the a priori algorithm to select the combined itemset using a minimum support of 25% and a minimum confidence of 100%, found the association rule, namely, if the customer buys at the same time. Buying goods has the highest value of support and trust. Likewise with the support value of 25%, the confidence value is 100%. In this way, if a customer buys an item, the probability that the customer buys the item is 100%

scholarly journals Application of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom

2021 ◽  
Vol 1 (2) ◽  
pp. 54-66
Author(s):  
M. Hamdani Santoso
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
A Priori ◽  
Apriori Algorithm ◽  
Rule Discovery ◽  
Association Rule Discovery ◽  
Rule Method ◽  
Confidence Value ◽  
Method Show

Data mining can generally be defined as a technique for finding patterns (extraction) or interesting information in large amounts of data that have meaning for decision support. One of the well-known and commonly used association rule discovery data mining methods is the Apriori algorithm. The Association Rule and the Apriori Algorithm are two very prominent algorithms for finding a number of frequently occurring sets of items from transaction data stored in databases. The calculation is done to determine the minimum value of support and minimum confidence that will produce the association rule. The association rule is used to produce the percentage of purchasing activity for an itemset within a certain period of time using the RapidMiner software. The results of the test using the priori algorithm method show that the association rule, that customers often buy toothpaste and detergents that have met the minimum confidence value. By searching for patterns using this a priori algorithm, it is hoped that the resulting information can improve further sales strategies.

scholarly journals Penerapan Data Mining Terhadap Data Penjualan Lapis Bogor Sangkuriang Dengan Metode Algoritma Apriori

2020 ◽  
Vol 6 (1) ◽  
pp. 61-70
Author(s):  
Bella Audi Najib ◽  
Nining Suryani
Keyword(s):  
Data Mining ◽  
A Priori ◽  
Data Mining Algorithms ◽  
A Value ◽  
Cake Layer ◽  
Growth Method ◽  
Frequent Items ◽  
Mining Algorithms ◽  
Confidence Value

Determination of the pattern of purchasing goods and layout of goods based on the tendency of consumers to buy goods canbe one solution for the Bogor Sangkuriang Lapis Shop in developing marketing strategies so as to increase sales of Lapis Bogor Sangkuriang products. The algorithm that can be used to determine the pattern of purchasing goods and this layout is the Apriori Algorithm which is one of the data mining algorithms in the formation of rule mining associations. With frequent items in a priori algorithm by producing a small frequent, without doing candidate generation and minimizing the completion stages starting at k-1 items or the first stage in the a priori algorithm then used with the FP-Growth method where this method is very significant with the a priori algorithm , efficient in terms of time, the completion stage is faster, produces less frequent items and is more detailed in describing frequent item results because frequent results with a value <1 are still shown, not deleted. This research produced the most sold products for layer cakes are Original Cheese, Cheese Brownies, Full Talas. Based on the rules of the final association, it is known that if you buy the Bogor Sangkuriang Lapis Original Cheese cake layer, you will buy the Lapis Bogor Sangkuriang Brownies Cheese and Full Talas Cheese with a support value of 30% and a confidence value of 70%. Based on these results the company can make the decision to develop a strategy that is done next

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.  

Recognizing Job Apathy Patterns of Iraqi Higher Education Employees Using Data Mining Techniques

2019 ◽  
Vol 54 (4) ◽  
Author(s):  
Mustafa S. Abd ◽  
Suhad Faisal Behadili
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Psychiatric Patients ◽  
Human Life ◽  
A Priori ◽  
Psychological Research ◽  
Attribute Selection ◽  
Specific Class ◽  
Data Mining Techniques ◽  
Using Data

Psychological research centers help indirectly contact professionals from the fields of human life, job environment, family life, and psychological infrastructure for psychiatric patients. This research aims to detect job apathy patterns from the behavior of employee groups in the University of Baghdad and the Iraqi Ministry of Higher Education and Scientific Research. This investigation presents an approach using data mining techniques to acquire new knowledge and differs from statistical studies in terms of supporting the researchers’ evolving needs. These techniques manipulate redundant or irrelevant attributes to discover interesting patterns. The principal issue identifies several important and affective questions taken from a questionnaire, and the psychiatric researchers recommend these questions. Useless questions are pruned using the attribute selection method. Moreover, pieces of information gained through these questions are measured according to a specific class and ranked accordingly. Association and a priori algorithms are used to detect the most influential and interrelated questions in the questionnaire. Consequently, the decisive parameters that may lead to job apathy are determined.

DATA MINING FOR THE MANAGEMENT OF SOFTWARE DEVELOPMENT PROCESS

2004 ◽  
Vol 14 (06) ◽  
pp. 665-695 ◽  
Author(s):  
J. L. ÁLVAREZ-MACÍAS ◽  
J. MATA-VÁZQUEZ ◽  
J. C. RIQUELME-SANTOS
Keyword(s):  
Data Mining ◽  
Software Development ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Development Process ◽  
A Priori ◽  
Post Mortem ◽  
Software Development Process ◽  
A Priori Analysis ◽  
Mining Tools

In this paper we present a new method for the application of data mining tools on the management phase of software development process. Specifically, we describe two tools, the first one based on supervised learning, and the second one on unsupervised learning. The goal of this method is to induce a set of management rules that make easy the development process to the managers. Depending on how and to what is this method applied, it will permit an a priori analysis, a monitoring of the project or a post-mortem analysis.

scholarly journals Implementasi Data Mining Untuk Menentukan Pola Penyebab Kecelakaan Lalu Lintas Di Wilayah Kota Palembang Menggunakan Algoritma FP-Growth

2020 ◽  
Vol 1 (2) ◽  
pp. 97-109
Author(s):  
Fathan Pangestu ◽  
Andri Andri
Keyword(s):  
Data Mining ◽  
Traffic Accidents ◽  
Weather Conditions ◽  
Minimum Support ◽  
City Area ◽  
Road Geometry ◽  
Sunny Weather ◽  
Motorized Vehicles ◽  
The City ◽  
Confidence Value

Palembang City is one of the big cities in Indonesia. Along with the increasing population and the increasing number of motorized vehicles, it will certainly have an impact on the increasing number of traffic accidents in the city of Palembang. In this study, the writer will determine the pattern of traffic accidents by using the fp-growth algorithm and using various variables. The variables that will be used consist of weather, time of incident, road geometry, profession, level of injury. This research is expected to be a reference for the police to be able to take anticipatory measures in order to reduce the number of traffic accidents in the Palembang City area. The fp-growth algorithm can be applied properly to determine the pattern of the causes of traffic accidents in the city of Palembang by using 2 minimum support of 40% and 50% and 2 minimum confidence of 70% and 90%. Based on the resulting rules, there are rules with the highest confidence value of 98% with these rules: When an accident occurs with a Side-Side accident type, the accident occurs in sunny weather conditions

scholarly journals IMPLEMENTASI ALGORITMA APRIORI TERHADAP DATA PENJUALAN PADA PERUSAHAAN RETAIL

2019 ◽  
Vol 15 (1) ◽  
pp. 85-90 ◽  
Author(s):  
Jordy Lasmana Putra ◽  
Mugi Raharjo ◽  
Tommi Alfian Armawan Sandi ◽  
Ridwan Ridwan ◽  
Rizal Prasetyo
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
A Priori ◽  
Apriori Algorithm ◽  
Business World ◽  
Transaction Data ◽  
Retail Company ◽  
Special Strategy

The development of the business world is increasingly rapid, so it needs a special strategy to increase the turnover of the company, in this case the retail company. In increasing the company's turnover can be done using the Data Mining process, one of which is using apriori algorithm. With a priori algorithm can be found association rules which can later be used as patterns of purchasing goods by consumers, this study uses a repository of 209 records consisting of 23 transactions and 164 attributes. From the results of this study, the goods with the name CREAM CUPID HEART COAT HANGER are the products most often purchased by consumers. By knowing the pattern of purchasing goods by consumers, the company management can increase the company's turnover by referring to the results of processing sales transaction data using a priori algorithm

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