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 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 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.

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>

Analisa Algoritma Apriori Untuk Peminjaman Buku Pada Perpustakaan SMA 1 Silima Pungga-Pungga Parongil

2019 ◽  
Vol 4 (1) ◽  
pp. 154-160
Author(s):  
Oktaviani Manurung ◽  
Penda Sudarto Hasugian
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
High Frequency ◽  
A Priori ◽  
Apriori Algorithm ◽  
Minimum Support ◽  
Minimum Requirements ◽  
To Come

ABSTRACT The library has the role of helping students to love reading books. The availability of books in various fields motivates students to come to visit the library, students can read or borrow library books. For this reason, library officers apply the rules for visiting the library. The Apriori algorithm is a part of data mining, namely the search for high frequency patterns such as activities that often appear simultaneously. The pattern that will be analyzed is the pattern of borrowing any books that are often borrowed so that librarians know the information of books that are often borrowed. With the application of a priori algorithms, book data is processed to produce a book borrowing pattern. After all the high frequency patterns were found, then association rules were found that met the minimum requirements for associative confidence A → B minimum confidence = 25%. Rules for sequential final association based on minimum support and minimum confidence, if borrowing an IPA, then borrowing MTK Support = 15%, Confidence = 42.8%. Keywords:Patterns of borrow of books, Library, Apriori Algorithms

Polyclinic Visitor Pattern Discovery Using Apriori Algorithm

2020 ◽  
Vol 9 (2) ◽  
pp. 229
Author(s):  
Teguh Satya Dharma ◽  
I Ketut Gede Suhartana
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Pattern Analysis ◽  
Pattern Discovery ◽  
Apriori Algorithm ◽  
People Today

Living in an information era, the presence of data is super important. With the data exponentially grows from decades to decades, it reflect the old saying that people today are so rich with data yet so poor on information. To dissect the information that contained within the large amount of data, a method is introduced called “Data Mining.” Data mining is a process of retriving a unique, unseen, and valuable information/insight from the data. Data mining comes with a lot of methods branches, one of those is pattern analysis, or also known as “Association Rules”. With the help of Association Rules, people can discover the relational pattern within the data, so people can make the best decision on the period of time. In this study, the writer is implementing Apriori Algorithm (one of the Association Rules algorithm) to see the pattern of a polyclinic visitor.

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 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

A Novel and Improved Apriori Algorithm

2014 ◽  
Vol 721 ◽  
pp. 543-546 ◽  
Author(s):  
Dong Juan Gu ◽  
Lei Xia
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Inner Product ◽  
Apriori Algorithm ◽  
Product Operation ◽  
Classical Algorithm ◽  
Transaction Database ◽  
The Given ◽  
To Receive ◽  
Improved Algorithm

Apriori algorithm is the classical algorithm in data mining association rules. Because the Apriori algorithm needs scan database for many times, it runs too slowly. In order to improve the running efficiency, this paper improves the Apriori algorithm based on the Apriori analysis. The improved idea is that it transforms the transaction database into corresponding 0-1 matrix. Whose each vector and subsequent vector does inner product operation to receive support. And comparing with the given minsupport, the rows and columns will be deleted if vector are less than the minsupport, so as to reduce the size of the rating matrix, improve the running speeding. Because the improved algorithm only needs to scan the database once when running, therefore the running speeding is more quickly. The experiment also shows that this improved algorithm is efficient and feasible.

Research of Association Rules Algorithm Based on Matrix under Cloud Computing

2014 ◽  
Vol 568-570 ◽  
pp. 798-801
Author(s):  
Ye Qing Xiong ◽  
Shu Dong Zhang
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Association Rules ◽  
Apriori Algorithm ◽  
Computing Technology ◽  
Binary Matrix ◽  
Transaction Data ◽  
Big Data Mining ◽  
Parallel Mining

It occurs time and space performance bottlenecks when traditional association rules algorithms are used to big data mining. This paper proposes a parallel algorithm based on matrix under cloud computing to improve Apriori algorithm. The algorithm uses binary matrix to store transaction data, uses matrix "and" operation to replace the connection between itemsets and combines cloud computing technology to implement the parallel mining for frequent itemsets. Under different conditions, the simulation shows it improves the efficiency, solves the performance bottleneck problem and can be widely used in big data mining with strong scalability and stability.

The Application of Apriori Algorithm in Analysis on Admitted Students of Colleges and Universities

2013 ◽  
Vol 321-324 ◽  
pp. 2578-2582
Author(s):  
Qian Zhang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Colleges And Universities ◽  
Apriori Algorithm ◽  
Data Mining Techniques ◽  
Minimum Support ◽  
Sample Data ◽  
Mining Association Rules

This paper examined the application of Apriori algorithm in extracting association rules in data mining by sample data on student enrollments. It studied the data mining techniques for extraction of association rules, analyzed the correlation between specialties and characteristics of admitted students, and evaluated the algorithm for mining association rules, in which the minimum support was 30% and the minimum confidence was 40%.

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