scholarly journals Data Mining Menggunakan Algoritma Apriori untuk Rekomendasi Produk bagi Pelanggan

2019 ◽  
Vol 7 (3) ◽  
pp. 103-108
Author(s):  
Ariefana Ria Riszky ◽  
Mujiono Sadikin
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Marketing Strategy ◽  
Association Rule ◽  
Apriori Algorithm ◽  
Processing Data ◽  
Total Data ◽  
Product Recommendations ◽  
Rule Method ◽  
Confidence Value

The implementation of a marketing strategy requires a reference so that promotion can be on target, such as by looking for similarities between product items. This study examines the application of the association rule method and apriori algorithm to the purchase transaction dataset to assist in forming candidate combinations among product items for customer recommended product promotion. The purchase transaction dataset was collected in October and November 2018 with a total data of 1027. In the experiment, the minimum value of support is 85%, and the minimum confidence value is 90% by processing data using the Weka software 3.9 version. Apriori algorithm can form association rules as a reference in the promotion of company products and decision support in providing product recommendations to customers based on defined minimum support and confidence values.

Penggunaan Association Rule Data Mining Untuk Menentukan Pola Lama Studi Mahasiswa F-MIPA UNSRAT

d'CARTESIAN ◽  
2014 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
M. Zainal Mahmudin ◽  
Altien Rindengan ◽  
Winsy Weku
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Large Data ◽  
Apriori Algorithm ◽  
Data Mining Algorithms ◽  
Adequate Information ◽  
Processing Data ◽  
Rule Method ◽  
Mining Algorithms ◽  
Confidence Value

Abstract The requirement of highest information sometimes is not balance with the provision of adequate information, so that the information must be re-excavated in large data. By using the technique of association rule we can obtain information from large data such as the college data. The purposes of this research is to determine the patterns of study from student in F-MIPA UNSRAT by using association rule method of data mining algorithms and to compare in the apriori method and a hash-based algorithms. The major’s student data of F-MIPA UNSRAT as a data were processed by association rule method of data mining with the apriori algorithm and a hash-based algorithm by using support and confidance at least 1 %. The results of processing data with apriori algorithms was same with the processing results of hash-based algorithms is as much as 49 combinations of 2-itemset. The pattern that formed between 7,5% of graduates from mathematics major that studied for more 5 years with confidence value is 38,5%. Keywords: Apriori algorithm, hash-based algorithm, association rule, data mining. Abstrak Kebutuhan informasi yang sangat tinggi terkadang tidak diimbangi dengan pemberian informasi yang memadai, sehingga informasi tersebut harus kembali digali dalam data yang besar. Dengan menggunakan teknik association rule kita dapat memperoleh informasi dari data yang besar seperti data yang ada di perguruan tinggi. Tujuan penelitian ini adalah menentukan pola lama studi mahasiswa F-MIPA UNSRAT dengan menggunakan metode association rule data mining serta membandingkan algoritma apriori dan algoritma hash-based. Data yang digunakan adalah data induk mahasiswa F-MIPA UNSRAT yang  diolah menggunakan teknik association rule data mining dengan algoritma apriori dan algoritma hash-based dengan minimum support 1% dan minimum confidance 1%. Hasil pengolahan data dengan algoritma apriori sama dengan hasil pengolahan data dengan algoritma hash-based yaitu sebanyak 49 kombinasi 2-itemset. Pola yang terbentuk antara lain 7,5% lulusan yang berasal dari jurusan matematika menempuh studi selama lebih dari     5 tahun dengan nilai confidence 38,5%. Kata kunci : Association rule data mining, algoritma apriori, algoritma hash-based

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 MENGGUNAKAN METODE ASSICIATION RULE DENGAN ALGORITMA APRIORI UNTUK ANALISA POLA PENJUALAN BARANG

JURTEKSI ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 193-198
Author(s):  
Yori Apridonal M ◽  
Wirdah Choiriah ◽  
Akmal Akmal
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Frequent Itemset ◽  
Apriori Algorithm ◽  
Data Set ◽  
Processing Data ◽  
New Information ◽  
Alternative Algorithm ◽  
Rule Method ◽  
Events Data

Abstract: Fantasy Kids is a children's clothing distribution in the Bangkinang area, Kampar Regency, Riau. In its operations, distros sell their products to the general public, including the sale of children's shirts, children's shirts, jackets or children's sweaters which are usually sold in other distros. These distributions carry out product updates at certain events. Data Mining is the development or discovery of new information by looking for certain patterns or rules of a large amount of data expected to overcome these conditions. The method that will be used in the construction of this application is the Association Rule method with the Apriori Algorithm. Association Rule method is a procedure to find relationships between items in a specified data set. In determining a Association Rule, there is a measure of trust obtained from the results of processing data with certain calculations. Apriori Algorithm is an alternative Algorithm that can be used to determine the frequent itemset in a data set. Keywords : Data Mining, Algoritma, Apriori, Association Rule, Sales, Distro  Abstrak: Fantasy Kids merupakan sebuah distro baju anak-anak di kawasan Bangkinang, Kabupaten Kampar, Riau. Dalam operasionalnya, distro menjual produknya kepada masyarakat umum meliputi penjualan kaos anak, kemeja anak, bag, jaket atau sweater anak yang biasa dijual di distro-distro lainnya. Distro ini melakukan pembaruan produk pada event tertentu. Data Mining merupakan pegembangan atau penemuan informasi baru dengan mencari pola atau aturan tertentu dari sejumlah data dalam jumlah besar diharapkan dapat mengatasi kondisi tersebut. Metode yang akan digunakan dalam pembangunan aplikasi ini adalah metode Association Rule dengan Algoritma Apriori. Metode Association Rule adalah suatu prosedur untuk mencari hubungan antara item dalam suatu kumpulan data yang ditentukan. Dalam menentukan suatu Association Rule, terdapat suatu ukuran kepercayaan yang di dapatkan dari hasil pengolahan data dengan perhitungan tertentu. Algoritma Apriori merupakan salah satu alternatif Algoritma yang dapat digunakan untuk menentukan himpunan data yang paling sering muncul (frequent itemset) dalam suatu kumpulan data. Kata kunci: Data Mining, Algoritma, Apriori, Association Rule, Penjuaan, Distro

scholarly journals Aplikasi Web Usage Mining Menggunakan Metode Association Rule Dengan Algoritma Fp-Growth Untuk Mengetahui Pola Browsing Pengunjung Website

2019 ◽  
Vol 1 ◽  
pp. 1060
Author(s):  
Yori Apridonal M ◽  
Febri Dristyan ◽  
Afdhal Syafnur
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Layout Design ◽  
Association Pattern ◽  
Processing Data ◽  
Alternative Algorithm ◽  
Rule Method ◽  
Using Data ◽  
Confidence Value ◽  
The Web

As a way to improve the promotion of institutions via the web, there is a need for a method to view browsing patterns of visitors on the site unilak.ac.id, thereby showing the user's interest in the links he visits. Data mining or knowledge discovery is a process of extracting valuable information by analyzing the existence of certain patterns or relationships. To find visitor patterns in the form of association rules is to use the association rule method. FP-Growth is an alternative algorithm that can be used to determine the most frequent set of data in a set of data. FP-Growth is applied to get a pattern of visitors, about what links are frequently visited and seen by visitors on the site unilak.ac.id. This pattern is used to help web administrators in developing the site unilak.ac.id by utilizing knowledge from the association pattern to regulate the layout / layout design of the categories available on the site unilak.ac.id. From the results of processing the dataset with FP-Growth algorithm and processing data processed using data mining software, namely Rapidminer 6.5. It was found that the minimum value of support was 1% and the minimum confidence value of 50% resulted in 124 rules of association.

XML-Enabled Association Analysis

2011 ◽  
pp. 2117-2122
Author(s):  
Ling Feng
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Marketing Strategy ◽  
Association Rules ◽  
Association Rule ◽  
Rule Mining ◽  
Xml Data ◽  
Market Basket ◽  
Transactional Databases ◽  
Mining Association Rules

The discovery of association rules from large amounts of structured or semi-structured data is an important data mining problem [Agrawal et al. 1993, Agrawal and Srikant 1994, Miyahara et al. 2001, Termier et al. 2002, Braga et al. 2002, Cong et al. 2002, Braga et al. 2003, Xiao et al. 2003, Maruyama and Uehara 2000, Wang and Liu 2000]. It has crucial applications in decision support and marketing strategy. The most prototypical application of association rules is market basket analysis using transaction databases from supermarkets. These databases contain sales transaction records, each of which details items bought by a customer in the transaction. Mining association rules is the process of discovering knowledge such as “80% of customers who bought diapers also bought beer, and 35% of customers bought both diapers and beer”, which can be expressed as “diaper ? beer” (35%, 80%), where 80% is the confidence level of the rule, and 35% is the support level of the rule indicating how frequently the customers bought both diapers and beer. In general, an association rule takes the form X ? Y (s, c), where X and Y are sets of items, and s and c are support and confidence, respectively. In the XML Era, mining association rules is confronted with more challenges than in the traditional well-structured world due to the inherent flexibilities of XML in both structure and semantics [Feng and Dillon 2005]. First, XML data has a more complex hierarchical structure than a database record. Second, elements in XML data have contextual positions, which thus carry the order notion. Third, XML data appears to be much bigger than traditional data. To address these challenges, the classic association rule mining framework originating with transactional databases needs to be re-examined.

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 Analisa Pola Penjualan Produk Sepeda Motor Yamaha Menggunakan Metode Algoritma Apriori

2021 ◽  
Vol 5 (3) ◽  
pp. 1107
Author(s):  
Siti Nurlela ◽  
Lilyani Asri Utami
Keyword(s):  
Data Mining ◽  
Marketing Strategy ◽  
Automotive Industry ◽  
Association Rule ◽  
The Other ◽  
Apriori Algorithm ◽  
Sales Data ◽  
Competitive Prices ◽  
Rule Analysis ◽  
Strategy Pattern

The development of automotive industry in Indonesia can be classifiedas very rapid and annually increasing, causing highly competitive circumstances because many companies provide various types of motorcycle brands with quality and competitive prices. The company must create a marketing strategy pattern that can increase the level of sales efficiency of Yamaha motorcycle products. To overcome this problem, a strategy that can help increasing sales of motorcycle products is needed, in which by utilizing sales data owned by the company. Data mining can be used to process company sales data by looking for association rules with apriori algorithm on motorcycle product variables. From the results of the association rule analysis on sales data, with a minimum support of 30% and a minimum confidence of 75% can produce 3 rules with 3 products that are most in demand by consumers, namely the NEW MIOM3 CW, NEWAEROX155VVA and N-MAX, by knowing the most selling products, the company can add the most selling product supply and develop a marketing strategy to market the products with other products by examining the comparative advantage of the most sold products over the other products.

scholarly journals THE OPTIMIZE OF ASSOCIATION RULE METHOD FOR THE BEST BOOK PLACEMENT PATTERNS IN LIBRARY: A MONTHLY TRIAL

TEKNOKOM ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 53-59
Author(s):  
T. Husain ◽  
Nuzulul Hidayati
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Research Method ◽  
Information Service ◽  
User Needs ◽  
Research Approach ◽  
Sources Of Information ◽  
Rule Method ◽  
Confidence Value

Data mining is the process of finding interesting patterns and knowledge from large amounts of data. Sources of information service, especially in the library, include books, reference books, serials, scientific gray literature (newsletters, reports, proceedings, dissertations, theses, and others). The importance of this research being carried out in the library in this study aims to implement data mining with the association rule method to solve problems, especially in the placement of shelves based on the category of the printed version of the book collection. This research method uses a qualitative research approach. Data was collected using documentation techniques and deep analysis of existing weaknesses to identify user needs whose information was obtained through observation and interviews with key informants (admin, user, etc.). For example, the determination of the best book placement patterns can be done by looking at the results of the tendency of visitors to borrow books based on a combination of 2 item sets with 60 percent of confidence value every month or week and must be evaluated or take a calculate again.

scholarly journals Combination, Pola, Apriori ANALISIS POLA BELANJA KONSUMEN MENGGUNAKAN ALGORITMA APRIORI PADA RAFFA PHOTOCOPY

2019 ◽  
Vol 2 (2) ◽  
pp. 63-73
Author(s):  
Nurul Azwanti
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Minimum Support ◽  
Association Pattern ◽  
Using Data ◽  
Confidence Value

Raffa Photocopy is a shop that started its business in 2016. This business not only provides photocopy services, but also provides office stationery and school supplies. Every day there are sales transactions where the recording of goods sold has a relationship between one another, because in recording sometimes consumers do not just buy one item, but two items even more as when buying a book, it is likely that consumers also buy a pen. This recording is only stored as an archive by Raffa Photocopy, even though the number of sales transactions that occur every day can lead to a pile of data. One effort to increase sales at Raffa Photocopy can be done by processing transaction data that overlaps by using data mining association techniques. This association rule technique uses the Apriori algorithm which deals with the study of 'what is with what' or discovers the association pattern of items that are often bought. The results of this study in the form of rules include the first, if you buy an eraser, it is likely that consumers also buy notebooks simultaneously. Second, if you buy Tipex, then consumers also buy a double folio. The results of the Apriori algorithm process are based on a minimum support value of 35% and a minimum confidence value of 80%.

scholarly journals Data Mining Technology in Book Copyright Information Management Decision System

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Song Lifang
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information Management ◽  
Association Rule ◽  
Information Management System ◽  
Management Decision ◽  
Apriori Algorithm ◽  
Mining Technology ◽  
Copyright Information ◽  
Rule Method

Today is an era of data “big bang”; Internet information technology is widely used in various fields of society. As an indispensable spiritual food in people’s daily life, books are increasing in number and scale. In order to better manage book information, people have introduced data mining technology. Based on this, this article takes the research and application of data mining technology in book copyright information management decision-making system as the theme, explores the role of data mining technology in book copyright information management, and aims to provide reference for our country’s book copyright information management and decision-making. This article first introduces the common algorithms of data mining technology and then elaborates on the advantages and effectiveness of the association rule method in data mining. Aiming at some defects of the original Apriori algorithm of the association rule method, an improved Apriori algorithm is proposed. After taking the library book information management system and database of a university in our province as the experimental research object, the performance gap between the two algorithms is compared through experiments, and it is concluded that when the number of transaction set item records is less than 1400, the Apriori algorithm performs better, and when the number of records in the transaction set is greater than 1400, the improved Apriori algorithm is obviously more advantageous. The research results show that the introduction and application of data mining technology make the information management of books more efficient and convenient, and it is more convenient for the management and decision-making of book copyright information.

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