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 Application Of Pizza Sales Data Mining Using Apriori Method

SinkrOn ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 1 ◽  
Author(s):  
Rusdiansyah Rusdiansyah ◽  
Nining Suharyanti ◽  
Triningsih Triningsih ◽  
Muhammad Darussalam
Keyword(s):  
Data Mining ◽  
Data Collection ◽  
Association Rules ◽  
A Priori ◽  
Apriori Algorithm ◽  
Mining Method ◽  
Processed Food ◽  
Sales Data ◽  
Business Opportunity ◽  
Use Of Data

Pizza is a processed food originating from Italy and has been spread in various other countries including one of them in Indonesia. Pizza is a processed food that is currently sought after by various groups of people so as to make the pizza business opportunity very profitable, if it is run in a food business. Currently the pizza business has very favorable prospects when compared to other businesses. Moreover, the targeted target can be from all walks of life from children to adults. Pizza sales transactions that produce sales data every day, have not been able to maximize the use of sales data. Sales data is only stored as an archive, so it becomes a pile of data. Therefore the use of data mining is used to solve this problem. A priori algorithm is a data mining method by using minimum support parameters, minimum confidence and will analyze in the period of every month of sales transactions. This study produces data on the results of the process of association rules from the data collection of sales transactions. From the association rules it can be concluded that the pattern of pizza sales, where consumers more often buy Meatzza and Cheese Mania, as evidenced by the results of calculations using Apriori Algorithm and Rapidminer 5.3, with support of 30% and 60% confidence.

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.  

Analisis Transaksi Konsumen Bidang Data Mining Menggunakan Algoritma Apriori Untuk Rekomendasi Bundling Produk Pada 212 Mart Kota Lhokseumawe

JURNAL TIKA ◽  
2021 ◽  
Vol 6 (02) ◽  
pp. 92-98
Author(s):  
Mutasar Mutasar ◽  
Chaeroen Niesa
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Frequent Itemset ◽  
Minimum Support

212 Mart Lhokseumawe yang bernaung di bawah PT. Syirkah Mubarakah Lhokseumawe adalah sebuah usaha ritel yang baru saja dirintis. Dalam operasionalnya masih banyak kendala yang dihadapi sehingga omzet penjualan harian masih jauh dari yang diharapkan, namun peneliti ingin menawarkan sebuah solusi untuk meningkatkan penjualan produk dengan teknik Bundling Produk yang masih diterapkan secara konvensional. Tujuan dari penelitian ini adalah merancang dan membangun sebuah aplikasi data mining untuk memprediksi hasil penjualan barang yang diminati konsumen pada 212Mart Kota Lhokseumawe dengan menggunakan algoritma apriori berdasarkan data transaksi penjualan. Algoritma Apriori adalah salah suatu algoritma yang melakukan pencarian frequent itemset dengan menggunakan teknik association rule. Algoritma Apriori menggunakan pengetahuan frekuensi atribut yang telah diketahui sebelumnya untuk memproses informasi selanjutnya. Pada algoritma Apriori menentukan kandidat yang mungkin muncul dengan cara memperhatikan minimum support dan minimum confidence. Implementasi dari hasil pengolahan data transaksi penjualan diproses dengan algoritma apriori sehingga akan menghasilkan sebuah output penawaran Bundling Barang kepada konsumen dan menawarkan harga yang relative lebih ekonomis. Proses data mining ini melalui tahapan pengenalan pola perilaku dan transaksi konsumen pada 212 Mart Kota Lhokseumawe

scholarly journals Penerapan Association Rule Mining Berbasis Algoritma Frequent Pattern Growth untuk Rekomendasi Penjualan

2020 ◽  
Vol 7 (2) ◽  
pp. 135-148
Author(s):  
Didi Supriyadi
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Itemset ◽  
Frequent Pattern ◽  
Rule Mining ◽  
Minimum Support ◽  
Pattern Growth ◽  
Mining Association Rule

Tingkat persaingan dan kompleksitas permasalahan penjualan pada perusahaan retail, menuntut setiap perusahaan retail untuk mampu berkompetisi dengan perusahaan lain. Salah satu yang dapat dilakukan adalah melalui pengambilan keputusan terkait penjualan yang lebih tepat dan efektif. Besarnya data transaksinonal penjualan perusahaan retail dapat dilakukan ekstraksi informasi yang bermanfaat. Metode yang dapat digunakan untuk menggali informasi adalah melalui penerapan association rule mining. Association Rule Mining merupakan suatu metode data mining yang berfokus pada pola transaksi dengan cara mengekstraksi asosiasi atau hubungan suatu kejadian. Keranjang belanja yang terdapat pada perusahaan retail yang terkomputerisasi merupakan cara terbaik untuk memberikan dukungan rekomendasi keputusan secara ilmiah dengan cara menentukan hubungan antara barang yang dibeli secara bersamaan dalam setiap transaksi. Algoritma FP-growth digunakan untuk menentukan himpunan dataset yang paling sering muncul (frequent itemset) pada sekeompok data. Penelitian ini menghasilkan nilai minimum support 0,1% dan nilai minimum confidence 60% jumlah rule yang dihasilkan berjumlah 116457, nilai minimum confidence 70% jumlah rule yang dihasilkan berjumlah 84086, dan nilai minimum confidence 80% jumlah rule yang dihasilkan berjumlah 48623 dari data yang diolah sebanyak 22191. Hasil rule ini dapat digunakan untuk strategi pemasaran produk. Nilai minimum support 0,1% dimana semakin besar nilai minimum confidence maka menghasilkan rule yang semakin sedikit.

scholarly journals Rancang Bangun Aplikasi Data Mining pada Penjualan Distro Bloods Berbasis Web menggunakan Algoritma Apriori

2021 ◽  
Vol 4 (2) ◽  
pp. 26
Author(s):  
Muhammad Muttaqin Muchlis ◽  
Iskandar Fitri ◽  
Rini Nuraini
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Association Rule ◽  
Web Applications ◽  
A Priori ◽  
Sales Data ◽  
Development Life Cycle ◽  
Technological Developments ◽  
Rule Method ◽  
Purchasing Patterns

The design of this data mining application is a computerized system in the field of technology, this proves that technological developments in data processing are increasingly advanced, this can be the basis for the development of data processing systems for sales of bloods based web applications using a priori algorithms, problems in this bloods distribution cannot Minimizing the decline in sales at the Jakarta clothing event in 2019, it is necessary to evaluate the sales data, with market basket analysis or consumer shopping baskets to find out consumer shopping patterns as a reference for the sale strategy of event Jakarta clothing at the end of the year. This analysis uses a priori algorithm with the association rule method, while the SDLC (Software Development Life Cycle) method is used as the basis for developing expert systems. From the results of the study, it was found that sales data for 5 days and 7 items got the highest 100% confidence value from the itemset calculation 1,2,3 which passed the selection so that they became aware of consumer purchasing patterns and rearranged product layouts for promotion and improving the correct sales strategy.Keywords:Applications, Data Mining, Apriori Algorithms, Association Rule Method, SDLC.

scholarly journals IMPLEMENTASI METODE MARKET BASKET ANALYSIS (MBA) MENGGUNAKAN ALGORITMA APRIORI DALAM TRANSAKSI PENJUALAN (STUDI KASUS: KAFE RUANG TEMU)

2020 ◽  
Vol 10 (2) ◽  
pp. 138
Author(s):  
Muhammad SyahruRomadhon ◽  
Achmad Kodar
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Apriori Algorithm ◽  
Market Basket Analysis ◽  
Tourist Attractions ◽  
Market Basket ◽  
Transaction Data ◽  
Data Mining Techniques ◽  
Minimum Support

Jakarta is one of the culinary attractions, many tourist attractions every year become creative in business. One of them is a cafe. Cafe Ruang Temu has sales transaction data but is not used to see associations between one product and another. In this case there needs to be a system for finding menu combinations by processing sales transactions. One of the data mining techniques is association rule or Market Basket Analysis (MBA) with apriori algorithm. Apriori algorithm aims to produce association rules to form menu combinations. The sales dataset for January 2019 to July 2019 is determined by the minimum support and minimum confidence values that have been set.  

scholarly journals Simple Modification for an Apriori Algorithm With Combination Reduction and Iteration Limitation Technique

2020 ◽  
Vol 3 (2) ◽  
pp. 89
Author(s):  
Adie Wahyudi Oktavia Gama ◽  
Ni Made Widnyani
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
A Priori ◽  
Frequent Itemset ◽  
Apriori Algorithm ◽  
Simple Modification ◽  
Scanning Process ◽  
Iteration Methods ◽  
Short Time

Apriori algorithm is one of the methods with regard to association rules in data mining. This algorithm uses knowledge from an itemset previously formed with frequent occurrence frequencies to form the next itemset. An a priori algorithm generates a combination by iteration methods that are using repeated database scanning process, pairing one product with another product and then recording the number of occurrences of the combination with the minimum limit of support and confidence values. The a priori algorithm will slow down to an expanding database in the process of finding frequent itemset to form association rules. Modification techniques are needed to optimize the performance of a priori algorithms so as to get frequent itemset and to form association rules in a short time. Modifications in this study are obtained by using techniques combination reduction and iteration limitation. Testing is done by comparing the time and quality of the rules formed from the database scanning using a priori algorithms with and without modification. The results of the test show that the modified a priori algorithm tested with data samples of up to 500 transactions is proven to form rules faster with quality rules that are maintained.Keywords: Data Mining; Association Rules; Apriori Algorithms; Frequent Itemset; Apriori Modified;

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

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