Implementation Of Data Mining Association Methods With Apriori Algorithm For Determining The Key Players Of Football Club

2020 ◽  
Vol 7 (2) ◽  
Author(s):  
Ari Zakaria ◽  
Arief Wibowo
Keyword(s):  
Data Mining ◽  
Apriori Algorithm ◽  
Football Club ◽  
Key Players

scholarly journals Applying Improved Apriori Algorithm in Figuring out the Relation between Weather Factors and Rainfall

2020 ◽  
Vol 1 (1) ◽  
pp. 23-26
Author(s):  
Siti Zulaikha ◽  
Martaleli Bettiza ◽  
Nola Ritha
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Light Intensity ◽  
Apriori Algorithm ◽  
Weather Factors ◽  
Factors Affecting ◽  
Data Store ◽  
Repeated Pattern ◽  
Major Factors ◽  
Sun Light

Data on the rainfall is compelling to study as it becomes one of the major factors affecting the weather in a certain region and various aspects of life as well. Generally, predicting rainfall is performed by analyzing data in the past in certain methods. Rainfall is prone to follow repeated pattern in sequence of time. The utilization of big data mining is expected to result in any valuable information that used to be unrevealed in the big data store. Some methods used in data mining are Apriori Algorithm and Improved Apriori Algorithm. Improved Apriori itself is to represent the database in the form of matrix to describe its relation in the database. Data used in this research is the rainfall factor in 2016 in Tanjungpinang city. Based on the test of Improved Apriori Algorithm, it was found out that the relation of the rainfall and weather factors utilizing 2 item sets, that is, if the temperature is low (24,0 - 26,0), the humidity is high (85 - 100), then the rainfall is mild. If the temperature is low (24,0 - 26,0), the light intensity is low (0 – 3), then the rainfall is heavy, and 3 item sets if the temperature is low (24,0 - 26,0), the humidity is high (85 - 100), the sun light intensity is low (0-3), then the rainfall is medium.

scholarly journals Penerapan Algoritma Apriori pada Sistem Informasi Perpustakaan Sekolah SMK Negeri 1 Siantar

2012 ◽  
Vol 6 (1) ◽  
pp. 226
Author(s):  
Risti DwiSyari ◽  
M Safii ◽  
M Fauzan
Keyword(s):  
Data Mining ◽  
School Library ◽  
School Libraries ◽  
Vocational School ◽  
Apriori Algorithm ◽  
Data Mining Techniques ◽  
Great Leader ◽  
Library Staff ◽  
Strategic Layout

The SMK Negeri 1 Siantar School Library is one of the special libraries located at the SMK Negeri 1 Siantar School. Libraries provide various kinds of library materials such as books, lessons, lesson questions, and other vocational books. After the researcher made observations, the problem that often occurred was books that were borrowed and returned books that had a non-strategic layout, so that library visitors who did not know the placement found it difficult to find the books they wanted to borrow. This research uses data mining techniques, namely the Apriori Algorithm, the Apriori Method is a method for looking for patterns of relationships between one or more items in a dataset. The Apriori method can be used for data on borrowing books at the Siantar 1 State Vocational School School Library, where the composition of the library books (B1) X_Press UN 2019 B. Indonesia side by side with books (B4) School of Love is a Great Leader and Teacher, if the composition of the book is (B10) Moral Mulia side by side with book (B1) X_Press UN 2019 B. Indonesia, If the book arrangement (B7) X_Press Mathematics is side by side with the book (B5) Relationer, if the book arrangement (B7) X_Press Mathematics is side by side with the book (B9) Indonesian Wisdom Batak Toba, and if the arrangement of the book (B10) Morals Mulia is side by side with the book (B8) Hati Therapy, the data from these items each met the minimum confidance value of 0,5% or the same as the specified 50%. The result of this research is to help library staff arrange the book layout correctly. It is hoped that this research can provide input to the school

scholarly journals ANALISIS POLA PENJUALAN DENGAN MENGGUNAKAN ALGORITMA APRIORI PADA KOPERASI KARYAWAN YAYASAN ANAKKU

2019 ◽  
Vol 2 (1) ◽  
pp. 31-36
Author(s):  
Arfianto Darmawan ◽  
Titin Kristiana
Keyword(s):  
Data Mining ◽  
Strong Association ◽  
Apriori Algorithm ◽  
Business Unit ◽  
Savings And Loans ◽  
Transaction Data ◽  
Existing Problems ◽  
A Value ◽  
Transaction Database ◽  
Using Data

The Anakku Foundation Cooperative is a multi-business cooperative consisting of shop businesses, savings and loans, and student shuttle services. Every sale of stuff services will be inputted data directly to each business unit. The Anakku Foundation Cooperative still has problems, including store transactions that cannot yet answer what items are often sold, when stock items are still difficult to determine the items that are still available or almost running out. Data mining techniques have been mostly used to overcome existing problems, one of which is the application of the Apriori algorithm to obtain information about the associations between products from a transaction database. Transaction data on school equipment sales at Cooperative Employees of Anakku Foundation can be reprocessed using Data mining applications so as to produce strong association rules between itemset sales of school supplies so that they can provide recommendations for item alignment and simplify the arrangement or strong item placement related to interdependence. The results are found that the highest value of support and confidence is if buying MUSLIM L1.5P1, so it would buy AL-IZHAR II LOGO with a value of 14.5% support and 79.5% confidence

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.

Data Mining Analysis of Moving Object Based on Apriori Algorithm

2014 ◽  
Vol 608-609 ◽  
pp. 221-225
Author(s):  
Jin Li Yang ◽  
Yong Ting Xu
Keyword(s):  
Data Mining ◽  
Strong Association ◽  
Apriori Algorithm ◽  
Table Tennis ◽  
Systematic Analysis ◽  
Material Resources ◽  
Object Based ◽  
Before And After ◽  
Classification Management ◽  
Effective Analysis

At present, under the background of reform in university physical education, the sports community constantly makes innovations of ways and means of study to improve the ways of education, how to analyze the teaching methods, this is the key issue needed to be focused on. During table tennis teaching, comparative analysis is based on the traditional test to study the effectiveness of teaching and take further measures, this process needs to choose specific objects to have the training in specific time and compare the results before and after testing, the manpower and material resources required is large and it is time-consuming, the article uses the improved Apriori algorithm, uses data mining technology to have systematic analysis of representative sample of data of Table Tennis teaching, and through the formation of a series strong association rules data to have compared analysis, in this way it can have effective analysis of teaching data for table tennis teaching, and give technical support to the classification management, at the same time it provides a reasonable, effective and directional data and suggestions for table tennis teaching .

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