scholarly journals ANALISIS PENERAPAN ASOSIASI UNTUK MENENTUKAN TRANSAKSI PENJUALAN PADA WHAT’S UP CAFÉ DENGAN METODE ALGORITMA APRIORI

2019 ◽  
Vol 1 (4) ◽  
pp. 181-186 ◽  
Author(s):  
Yulinda Wahyuningtias ◽  
Rusdiansyah Rusdiansyah
Keyword(s):  
Data Mining ◽  
Interior Design ◽  
Association Rules ◽  
A Priori ◽  
Data Sets ◽  
Small Cities ◽  
Minimum Support ◽  
Food And Beverage ◽  
The Many ◽  
People Today

Di Indonesia saat ini sedang terkenal dan sangat berkembangnya dalam bisnis. Terutama bisnis Restoran/cafe hal ini yang sangat di minati oleh kalangan anak muda hingga dewasa,yang bertempat di seluruh Indonesia di kota-kota besar maupun kota kecil. Hal ini dapat dilihat dari banyaknya restoran/cafe yang bermunculan dikarenakan restoran/cafe sudah menjadi lifestyle bagi kebanyakan orang di jaman sekarang, dengan menyediakan tempat berdesain interior yang menarik serta menawarkan suasana yang nyaman dan menyenangkan. Data Mining adalah proses ekstraksi informasi dari kumpulan data melalui penggunaan algoritma dan teknik yang melibatkan bidang ilmu statistik, mesin pembelajaran, dan sistem manajemen database. Algoritma apriori termasuk jenis aturan asosiasi pada data mining. Pada penelitian ini menggunakan data sekunder. Tanagra adalah perangkat lunak bebas untuk tujuan akademik dan penelitian. Sampel dari penelitian ini adalah bagian dari jumlah populasi menu makanan dan minuman pada What’s Up Café Meruya Periode bulan Desember 2018 sampai bulan Maret 2019 yang berjumlah 22 menu untuk menentukan sampel tersebut pada penelitian ini adalah menggunakan rumus slovin. Hasil dari penelitian ini terdapat 2 transaksi dengan 2 itemset dengan minimum support 40% yang memenuhi syarat ketentuan aturan asosiasi algoritma apriori. In Indonesia, it is currently well-known and highly developed in business. Especially the restaurant or cafe business that is very interested in young people to adults, located throughout Indonesia in big cities and small cities. This can be seen from the many restaurants or cafes that have sprung up because restaurants or cafes have become a lifestyle for most people today, by providing an attractive interior design place and offering a comfortable and pleasant atmosphere.Data Mining is the process of extracting information from data sets through the use of algorithms and techniques involving the fields of statistics, machine learning, and database management systems. A priori algorithms include types of association rules in data mining. Tanagra is free software for academic and research purposes. The sample is a small part of the population members taken. The sample of this study is part of the total population of food and beverage menu at the What's Merge Cafe Meruya Period from December 2018 to March 2019 which amounted to 22 menus to determine the sample in this study is to use the Slovin formula. The results of this study are 2 transactions with 2 item sets with a minimum support of 40% that meet the conditions of the a priori algorithm association rules.

scholarly journals Implementasi Data Mining Menggunakan Algoritma Apriori Pada Penjualan Suku Cadang Motor

2021 ◽  
Vol 14 (2) ◽  
pp. 125
Author(s):  
Ainul Mardiaha ◽  
Yulia Yulia
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
A Priori ◽  
Spare Parts ◽  
Frequent Itemset ◽  
Sales Data ◽  
Minimum Support ◽  
Relationship Of

This research was carried out to simplify or assist Candra Motor workshop owners in managing data and archives of motorcycle parts sales by applying a data mining a priori algorithm method. Data mining is an operation that uses a particular technique or method to look for different patterns or shapes in a selected data. Sales data for a year with the number of 15 items selected using the priori algorithm method. A priori algorithm is an algorithm for taking data with associative rules (association rule) to determine the associative relationship of an item combination. In a priori algorithm, it is determined frequent itemset-1, frequent itemset-2, and frequent itemset-3 so that the association rules can be obtained from previously selected data. To obtain the frequent itemset, each selected data must meet the minimum support and minimum confidence requirements. In this study using minimum support ? 7 or 0.583 and minimum confidence of 90%. So that some rules of association were obtained, where the calculation of the search for association rules manually and using WEKA software obtained the same results.By fulfilling the minimum support and minimum confidence requirements, the most sold spare parts are inner tube, Yamaha oil and MPX oil.

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 Algoritma Apriori Untuk Penentuan Assosiasi Penjualan Barang

2021 ◽  
Vol 9 (1) ◽  
pp. 7
Author(s):  
Calvin Ivan Wiryawan ◽  
Yustina Retno Wahyu Utami ◽  
Didik Nugroho
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
A Priori ◽  
Basic Needs ◽  
Marketing Strategies ◽  
Apriori Algorithm ◽  
Growing Up ◽  
Minimum Support ◽  
Data Mining Algorithms ◽  
Mining Algorithms

The increasing of selling basic needs make the company has to provide a lot of goods. The data will be growing up with increasing the transaction at Sari Bumi store. All this time, the selling basic needs at Sari Bumi Store unstructured well so that needed an application with produce important information that can decide marketing strategies. In this research, Apriori algorithm is used to determine association rules. This method was chosen because it is one of the classic data mining algorithms to look for patterns of relationships between one or more items in one dataset. A priori algorithms can help companies in developing marketing strategies. The result of this research is combination between 4 item set with a minimum support of 30% and minimum confidence of 60%.Keywords: sale, staple, apriori algorithm

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

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

Finding Persistent Strong Rules

2010 ◽  
pp. 85-107
Author(s):  
Anthony Scime ◽  
Karthik Rajasethupathy ◽  
Kulathur S. Rajasethupathy ◽  
Gregg R. Murray
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Strong Association ◽  
National Election ◽  
Data Sets ◽  
Rule Discovery ◽  
Discovery Process ◽  
Data Set ◽  
Rule Sets ◽  
Election Studies

Data mining is a collection of algorithms for finding interesting and unknown patterns or rules in data. However, different algorithms can result in different rules from the same data. The process presented here exploits these differences to find particularly robust, consistent, and noteworthy rules among much larger potential rule sets. More specifically, this research focuses on using association rules and classification mining to select the persistently strong association rules. Persistently strong association rules are association rules that are verifiable by classification mining the same data set. The process for finding persistent strong rules was executed against two data sets obtained from the American National Election Studies. Analysis of the first data set resulted in one persistent strong rule and one persistent rule, while analysis of the second data set resulted in 11 persistent strong rules and 10 persistent rules. The persistent strong rule discovery process suggests these rules are the most robust, consistent, and noteworthy among the much larger potential rule sets.

Visual Data Mining for Discovering Association Rules

2008 ◽  
pp. 2105-2120
Author(s):  
Kesaraporn Techapichetvanich ◽  
Amitava Datta
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Large Data ◽  
Data Sets ◽  
Visual Data Mining ◽  
Useful Knowledge ◽  
Large Databases ◽  
A New Technique ◽  
Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

Visual Data Mining for Discovering Association Rules

2011 ◽  
pp. 209-226
Author(s):  
Kesaraporn Techapichetvanich ◽  
Amitava Datta
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Large Data ◽  
Data Sets ◽  
Visual Data Mining ◽  
Useful Knowledge ◽  
Large Databases ◽  
A New Technique ◽  
Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

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%.

A Data Warehouse Cleansing Approach Based on Mathematical Association Rules

2012 ◽  
Vol 490-495 ◽  
pp. 1878-1882
Author(s):  
Yu Xiang Song
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Association Rules ◽  
Data Sets ◽  
Mathematical Association ◽  
Manual Intervention ◽  
Mining Association Rules ◽  
High Degree

The alliance rules stated above based on the principle of data mining association rules provide a solution for detecting errors in the data sets. The errors are detected automatically. The manual intervention in the proposed algorithm is highly negligible resulting in high degree of automation and accuracy. The duplicity in the names field of the data warehouse has been remarkably cleansed and worked out. Domain independency has been achieved using the concept of integer domain which even adds on to the memory saving capability of the algorithm.

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