scholarly journals Penerapan Asosiasi Algoritma Apriori Pada Data Penjualan Alat-Alat Listrik Dan Tekhnik

METIK JURNAL ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 71-76
Author(s):  
Regina Pihu Atadjawa ◽  
Tuti Haryanti ◽  
Laela Kurniawati
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Microsoft Excel ◽  
Transaction Data ◽  
Sales Data ◽  
Business People ◽  
Electronic Product ◽  
Sales And Marketing ◽  
Product Sales

The incompatibility of information in reporting productsthat are sold, and data storage is very large, business people, especially in the sales business are required to find an appropriate strategy that can increase sales and marketing of products sold, one of which is by using electronic product sales data. Therefore, anapplication is needed that is able to sort and select data, so that information can be obtained that is useful for users, namely data mining. Associate patterns can be used to place products that are often purchased together into an area that is close together so as to facilitate the customer in finding the desired product and designing the appearance of products in the catalog. The method used is theApriori Algorithm method, with the help ofTanagra 1.4.50 tools and processing transaction data using Microsoft Excel 2007.

scholarly journals The Use of Apriori Algorithm in the Formation of Association Rule at Lotteria Cibubur

SinkrOn ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 76
Author(s):  
Ovi Liansyah ◽  
Henny Destiana
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Association Rules ◽  
Association Rule ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Data Mining Techniques ◽  
Sales Data ◽  
Sales Strategy ◽  
Data Mining Application

Lotteria as one of the franchises that produce sales data every day, has not been able to maximize the utilization of that data. The sale data storage is still not optimal. By utilizing sales transaction data that have been stored in the database, the management can find out the menus purchased simultaneously, using the association rule. Namely, data mining techniques to find the association rules of a combination of items. The process of searching for associations uses the help of apriori algorithms to produce patterns of the combination of items and rules as important knowledge and information from sales transaction data. By using the minimum support parameters, the minimum and the month period of the sales transaction to find the association rules, the data mining application generates association rules between items in April 2019, where consumers who buy hot / ice coffee will then buy float together with support of 16% and 100% confidence. Knowing which menu products or items are the most sold, thus lotteria Cibubur can develop a sales strategy to sell other types of menu products by examining the advantages of the most sold menu with other menus and can increase the stock of menu ingredients.

Pemanfaatan Metode K-Means Dalam Penentuan Persediaan Barang

2018 ◽  
Vol 6 (1) ◽  
pp. 41-48
Author(s):  
Santoso Setiawan
Keyword(s):  
Data Mining ◽  
Stock Management ◽  
Transaction Data ◽  
Business People ◽  
Cluster Data ◽  
Data Clusters ◽  
Inventory Strategy ◽  
Transactional Data

Abstract   Inaccurate stock management will lead to high and uneconomical storage costs, as there may be a void or surplus of certain products. This will certainly be very dangerous for all business people. The K-Means method is one of the techniques that can be used to assist in designing an effective inventory strategy by utilizing the sales transaction data that is already available in the company. The K-Means algorithm will group the products sold into several large transactional data clusters, so it is expected to help entrepreneurs in designing stock inventory strategies.   Keywords: inventory, k-means, product transaction data, rapidminer, data mining   Abstrak   Manajemen stok yang tidak akurat akan menyebabkan biaya penyimpanan yang tinggi dan tidak ekonomis, karena kemungkinan terjadinya kekosongan atau kelebihan produk tertentu. Hal ini sangat berbahaya bagi para pelaku bisnis. Metode K-Means adalah salah satu teknik yang dapat digunakan untuk membantu dalam merancang strategi persediaan yang efektif dengan memanfaatkan data transaksi penjualan yang telah tersedia di perusahaan. Algoritma K-Means akan mengelompokkan produk yang dijual ke beberapa cluster data transaksi yang umumnya besar, sehingga diharapkan dapat membantu pengusaha dalam merancang strategi persediaan stok.   Kata kunci: data transaksi produk, k-means, persediaan, rapidminer, data mining.

scholarly journals Implementation of Apriori Algorithm Data Mining for Increase Sales

SinkrOn ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 17
Author(s):  
Reza Alfianzah ◽  
Rani Irma Handayani ◽  
Murniyati Murniyati
Keyword(s):  
Data Mining ◽  
Business Strategy ◽  
A Priori ◽  
Data Mining Algorithm ◽  
Sales Data ◽  
The Right ◽  
Or Organization ◽  
Product Sales ◽  
The Relationship ◽  
Confidence Value

Any company or organization that wants to survive needs to determine the right business strategy. The product sales data carried out by Lakoe Dessert Pondok Kacang will eventually result in a pile of data, so it is unfortunate if it is not re-analyzed. The products offered vary with a wide variety of products as many as 45 products, to find out the products with the most sales and the relationship between one product and another, one of the algorithms is needed in the data mining algorithm, namely the a priori algorithm to find out, and with the help of the Rapidminer 5 application, with a support value 2,4% and a confidence value 50%, products that customers often buy or are interested in can be found. This study used sales data for March 2020, which amounted to 209 transaction data. From the research, it was found that the item with the name Pudding Strawberry and Pudding Vanilla was the product most purchased by consumers. With knowledge of the most sold products and the patterns of purchasing goods by consumers, Lakoe Dessert Pondok Kacang can develop marketing strategies to market other products by analyzing the profits from selling the most sold products and anticipating running out or empty of stock or materials at a later date.

IMPLEMENTASI METODE DATA MINING APRIORI PADA APLIKASI PENJUALAN PT. TIGA RAKSA SATRIA

2020 ◽  
Vol 17 (1) ◽  
pp. 329-338
Author(s):  
Siti Qomariah ◽  
Hanifah Ekawati ◽  
Sepriyadi Belareq
Keyword(s):  
Data Mining ◽  
A Priori ◽  
Apriori Algorithm ◽  
Analysis Method ◽  
Strategic Information ◽  
Sales Data ◽  
Data Application ◽  
Product Sales

been done computerized, but the sales data has not been processed optimally. there is no application that analyzes sales data for category, planning and service to consumers. Analyzing sales data is an important part of the company, an analysis of sales results has an impact on the profits to be gained by the company. Datamining is the science of digging up valuable information and knowledge in databases. One algorithm in data mining is a priori algorithm. Datamining is widely implemented in various fields such as business, commerce, and others. This research aims to make an application with the Application of Data Mining Basketball Analysis Method with Apriori Algorithm to process the sales data in a more structured, detailed and know the problems in product sales. This application generates rules that help draw conclusions needed for drawing conclusions of strategic information for companies regarding sales data. Application made with the application of a priori methods helps in the analysis of sales data that is owned. 

scholarly journals Implementation of the K-Means Clustering Method in Data Grouping Sales In Asia Africa Dentures Dental

2019 ◽  
Vol 2 (2) ◽  
pp. 286-291
Author(s):  
Sonibe Halawa ◽  
Rita Hamdani
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Visual Basic ◽  
Added Value ◽  
Clustering Method ◽  
Sales Data ◽  
Knowledge Analysis ◽  
Data Grouping

Data mining can be applied to explore the added value of a set of data in the form of knowledge that had been unknown to them manually. There are several techniques used dala mining eyes, one satuteknik data mining is clustering. Clustering can be used for grouping to something. As can group sales data that is most desirable, and others. Examples of companies engaged in the sale is a dental african Asia. Asia Africa Dental is one area of business engaged in the sale of false teeth. Asia Africa Dental these every day to meet the needs of consumers. But Asia Africa Dental lacking in reviewing products sold. What products are needed consumer and data storage is less effective. Thus the need for a system that can support the company in taking decisions quickly and precisely. So in this study, the authors used the application of K-Means Clustering method. To facilitate the author in analyzing the K-Means Clustering The author using the application Weka (Waikato Environment for Knowledge Analysis) .. The result of the calculation Weka (Waikato Environment for Knowledge Analysis) is inserted into the Visual Basic .Net.

scholarly journals IMPLEMENTASI DATA MINING MENGGUNAKAN METODE ALGORITMA APRIORI PADA TOKO DHURROH ELEKTRONIK

2021 ◽  
Vol 13 (2) ◽  
pp. 67
Author(s):  
Syafrianto Syafrianto ◽  
Durotun Ayniyah
Keyword(s):  
Data Mining ◽  
Apriori Algorithm ◽  
Business World ◽  
Transaction Data ◽  
Sales Data ◽  
Group Data ◽  
Confidence Value

In the business world, every store must of course be able to compete and think about how the store can continue to grow and be able to expand its business scale. In order to increase sales of products sold, business actors must have various strategies. One way is by utilizing all sales transaction data that has occurred in the store itself. Dhurroh Elektronik store is a store that sells various kinds of goods such as cellphone accessories. Management of sales data in this store is still done manually, namely by recording sales data in the sales book or sometimes when serving purchases just remembering it. The obstacle faced is that it is difficult to find out where the goods are not in accordance with the behavior of consumers' habits in buying goods at the same time. Based on the above problems, it is necessary to have a calculation to group data items based on their tendencies that appear together in a transaction with Data Mining calculations using the Apriori Algorithm method. The results of the calculation of the items that are most in-demand are if you buy a headset, you will buy a lamp with a 100% confidence value and 19% support, if you buy a radio you will buy a lamp with a 71% confidence value and 16% support, if you buy a data cable, you will buy a flashlight. with a 71% Confidence value and 16% Support, If you buy a battery, you will buy a Flashlight with a 71% Confidence value and 16% Support. Keywords: Data Mining, Apriori Algorithm..

scholarly journals PENERAPAN DATA MINING MENGGUNAKAN ALGORITMA K-MEANS UNTUK MENGETAHUI MINAT CUSTOMER DI TOKO HIJAB

2019 ◽  
Vol 15 (2) ◽  
pp. 241-246
Author(s):  
Yulianti Yulianti ◽  
Dwi Yuni Utami ◽  
Noer Hikmah ◽  
Fuad Nur Hasan
Keyword(s):  
Data Mining ◽  
Transaction Data ◽  
Business People ◽  
Sales Strategy ◽  
Two Parameters

Hijab is not a foreign thing for the population in Indonesia, because most of the population of Indonesia is Muslim. Today, many business people, especially hijab sellers, provide a variety of brands and models in the hijab they sell. Therefore sellers are required to be able to think intelligently in making a sales strategy that will certainly be useful to know clearly which products are most in demand by customers, and also to increase sales in their stores. Then there needs to be an alternative that can realize the recording of sales transaction data more quickly and structured. In this study the authors applied the k-means algorithm to determine customer interest in the products they sell. In the calculation that has been done by using two parameters, namely the transaction and the number of sales and passing three iterations with the results of iterations one gets a ratio of 0.374324132, the iteration two gets the ratio 0.543018325, and the iteration three gets the same ratio value as second iteration. So it can be concluded that the hijab that is most desirable by the customers is the hijab with the brand Rabbani, Elzatta, and Zoya, the low-interest hijab branded by Dian Pelangi, Kami Idea, and Meccanism. And the hijab with those who are not high and also not low is the hijab under the brand Ria Miranda, Jenahara, Shasmira, and Shafira.

scholarly journals Penerapan Data Mining untuk Klasifikasi Produk Merk Bata Menggunakan Algoritma K-Means

2021 ◽  
Vol 2 (1) ◽  
pp. 161-166
Author(s):  
Hernita Samosir ◽  
Muhammad Amin ◽  
Indra Ramadona Harahap
Keyword(s):  
Data Mining ◽  
Programming Language ◽  
Transaction Data ◽  
Sales Data ◽  
Product Classification ◽  
Store Management

Abstract: Tanjungbalai Bata Store is a store that is engaged in the business of selling products and every day processes purchase data, sales data and transaction data. Transaction data is the result of sales that can be obtained so that store management knows the strategies that will be carried out to increase sales results. As for consumers who make transactions at stores for a separate reason, especially because of the completeness and many models that can be obtained from the Tanjungbalai brick shop, another reason is that the Tanjungbalai Brick Shop can provide a sense of comfort and peace in addition and the cleanliness seen from the store . There are many types of products sold at the Tanjungbalai Brick Shop. However, Tanjungbalai Brick Shop cannot classify products that are selling well and those that are not selling well. So that the difficulties experienced are the frequent shortage of stock of products that sell well because sales are high and the accumulation of products that are not selling well in the warehouse because the sellers are low. Based on the problems above, data mining is needed to classify which products are in demand and which are not. Data mining and k-means methods can help in this research combined with the PHP programming language and MySQL database. Keywords:Data Mining; Product Classification; K-Means Algorithm.  Abstrak:Toko Bata Tanjungbalai adalah toko yang bergerak di bidang bisnis penjulalan produk dan setiap harinya melalukan proses data pembelian, data penjualan maupun data transaksi. Data transaksi merupakan hasil penjualan yang di dapat agar manajemen toko mengetahui strategi yang akan di lakukan untuk meningkatkan hasil penjualan. Adapun konsumen yang melakukan transaksi di toko memiliki alas an tersendiri ataupun di karenakan kelengkapan dan banyak model yang bisa di dapatkan dari toko bata tanjungbalai, alasan yang lain adalah Toko Bata Tanjungbalai dapat memberikan rasa nyamandan tentram di tambah lagi keramahan dan kebersihan yang di lihat dari toko tersebut. Ada banyak jenis produk yang terjual di Toko Bata Tanjungbalai, namun toko bata Tanjungbalai tidaklah mampu dalam membagikan kelompok produk tersebut masuk kategori laris dan tidak laris. Sehingga kesulitan yang dialami yaitu seringnya kekurangan stok produk yang laku karena penjualannya tinggi dan menumpuknya produk yang tidak laris di gudang karena penjualnnya rendah. Berdasarkan permasalahan di atas maka dibutuhkan data mining untuk mengelompokkan produk mana saja yang laris dan tidak. Data mining dan metode k-meansdapat membantu dalam penelitian ini dipadukan dengan pemrograman PHP dan MySQL. Kata Kunci :Data Mining; Klasifikasi Produk; Algoritma K-Means.

scholarly journals PENERAPAN ASSOCIATION RULE DATA MINING UNTUK REKOMENDASI PRODUK KOSMETIK PADA PT. FABIANDO SEJAHTERA MENGGUNAKAN ALGORITMA APRIORI

ALGOR ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Febri Antho ◽  
Dram Renaldi ◽  
Edy ◽  
Yakub
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Association Rule ◽  
Policy Decision ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Cosmetic Product ◽  
Product Recommendations ◽  
Using Data ◽  
Product Sales

In some companies that have sales transaction data and this data will increase from day to day so that it will accumulate and become garbage if it is not managed and utilized properly. Sales transaction data is one thing that can be used to increase product sales. Not only to increase product sales but also to provide product recommendations for each sale. As in the product stock setting section, it can provide recommendations for the number of products so that problems such as over stock will not occur which will cause the amount in a product to expire. In this study, an association rule data mining will be implemented for cosmetic product recommendations using the Apriori algorithm. Testing the results of using data mining and the Apriori algorithm is carried out to find out that the results of the study can find association rules from existing datasets to recommend cosmetic products. The association rule method is used in the search for product attachment patterns for sales strategies in policy decision making. So that it can be seen that the cosmetics that are often purchased by consumers, based on the rules generated from the data contained in the database. Tests were carried out using the Rapidminer 9.5 application. The results obtained from this test are that there are 16 rules (rules) that will be used for decision making in cosmetic product recommendations.

scholarly journals PERANCANGAN APLIKASI OLAP (ONLINE ANALYTICAL PROCESS) PENJUALAN PADA PT. HM SEMPOERNA TBK DPC LUBUKLINGGAU

2018 ◽  
Vol 3 (2) ◽  
pp. 109-121
Author(s):  
Budi Santoso
Keyword(s):  
Decision Makers ◽  
Socially Responsible ◽  
Processing Application ◽  
Transaction Data ◽  
Sales Data ◽  
Analytical Process ◽  
Future Developments ◽  
Long Time ◽  
Product Sales

AbstrakPT. HM Sempoerna merupakan salah satu produsen rokok terkemuka di Indonesia yang memproduksi sejumlah merk rokok kretek yang dikenal luas di Indonesia maupun di luar negeri. Tujuan utama perusahaan ini adalah menjadi perusahaan yang bertanggung jawab secara sosial di tingkat lokal maupun global dan yang menjadi misi perusahaan ini adalah  menawarkan pengalaman merokok terbaik kepada perokok dewasa di Indonesia, salah satu  cabang perusahaan ini ada di kota Lubuklinggau, dikota Lubuklinggau perusahaan ini maju sangat pesat dengan banyaknya data transaksi penjualan pada setiap bulannya. Untuk menganalisis data penjualan tersebut, pihak manajemen perusahaan harus membuka file-file lama kemudian mengeditnya kembali untuk disajikan dalam presentasi, hal tersebut akan membutuhkan waktu yang cukup lama agar data yang tersajikan bisa akurat karena harus menyeleksi datanya dengan teliti. Setelah disajikan, para pengambil keputusan belum tentu puas karena hanya dapat melihat penyajian tersebut hanya dari satu sisi saja. Mereka menginginkan suatu penyajian yang ringkas dan dapat menggambarkan kondisi penjualan terkini, sehingga dapat mengambil keputusan untuk perkembangan yang akan datang. Memperhatikan kondisi yang dialami perusahaan terutama yang terjadi pada PT. HM Sempoerna cabang Lubuklinggau. Dimana perusahaan tersebut kesulitan dalam menganalisis data penjualan produk, maka perlu adanya suatu sistem yang bisa menangani permasalahan tersebut, dari tersebut diatas maka peneliti berniat untuk membangun sebuah aplikasi OLAP (Online Analitycal Processing) untuk analisis data penjualan pada PT. HM Soempurna DPC Lubuklinggau             Kata kunci: OLAP, Penjualan  AbstractPT. HM Sempoerna is one of the leading cigarette manufacturers in Indonesia producing a number of brands of clove cigarettes widely known in Indonesia and abroad. The company's main objective is to be socially responsible company both locally and globally and the company's mission is to offer the best smoking experience to adult smokers in Indonesia, one of the company's branches in Lubuklinggau city, Lubuklinggau city, rapidly with the number of sales transaction data on each month. To analyze the sales data, the company management must open the old files and then edit them back to be presented in the presentation, it will take a long time for the data presented can be accurate because it must select the data carefully. Once presented, the decision makers are not necessarily satisfied because they can only see the presentation from one side only. They want a concise presentation and can describe the current sales conditions, so they can make decisions for future developments. Taking into account the conditions experienced by the company especially that occurred at PT. HM Sempoerna branch Lubuklinggau. Where the company difficulties in analyzing product sales data, it is necessary for a system that can handle these problems, from the above then the researcher intends to build an OLAP (Online Analitycal Processing) application for data sales analysis at PT. HM Perfect DPC Lubuklinggau. Keywords : OLAP,  Sales

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