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 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..

Rancang Bangun Inventarisasi Barang menggunakan Algoritma Apriori Sebagai Data Mining

2021 ◽  
Vol 5 (4) ◽  
pp. 354
Author(s):  
Aditya Prasetya ◽  
Septi Andriana ◽  
Ratih Titi Komalasari
Keyword(s):  
Data Mining ◽  
Programming Language ◽  
A Priori ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Important Thing ◽  
Minimum Support ◽  
Manual Method ◽  
The Times ◽  
Modern Era

Inventory activities become an important thing for business progress, along with the times, inventory activities become easier due to the large number of facilities and infrastructure to support activities, including the Ap Jaya Store which also competes in the modern era, but currently, inventory activities in stores Ap Jaya still uses the manual method, namely by recording inventory activities using a book then recapitulating one by one so that it takes a lot of time, along with these problems an inventory application is needed that can be used to support these activities, this inventory application is made using the a priori algorithm method as data mining and using the programming language PHP and MySQL as a database besides that the a priori algorithm can also be used for item recommendation systems, on testing with 20 transaction data with a minimum support value = 20% and a minimum confidence = 70% also from the results of the transaction. Tests carried out using the apriori algorithm and using applications that are made get the same results according to the requirements for support and confidence values.Keywords:Inventory, Data Mining, Apriori Algorithm

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.

scholarly journals Perancangan Program Aplikasi Market Basket Analysis untuk Mendukung Persediaan Barang dengan Metode Fuzzy C-Covering

2012 ◽  
Vol 3 (1) ◽  
pp. 241
Author(s):  
Zahedi Zahedi ◽  
Charies Chandra
Keyword(s):  
Data Mining ◽  
Fuzzy Sets ◽  
Market Basket Analysis ◽  
Huge Number ◽  
Market Basket ◽  
Transaction Data ◽  
Sales Data ◽  
Data Market ◽  
A Company ◽  
Data Mining Application

Any difficulty in analyzing sales transaction data is often faced by a company due to the huge number of sales transactions and the limited tools to process the data. It results in losses for the company since it is difficult to estimate the goods supply for subsequent sales. In this paper a data mining application is designed to analyze sales data. Market Basket Analysis is a data mining application that aims to determine most purchased or used products at once by the consumer. The process of Market Basket Analysis is to analyze consumer buying habits by finding associations among products purchased by different customers. The method used in Market Basket Analysis is a method of Fuzzy c-Covering, which is one method to classify the elements of a universal set into partitions of fuzzy sets. This study found that the value of support and confidence is part of the Market Basket Analysis, computed using the Fuzzy c-Covering. The higher the limit, the more selected the analytical results obtained are.

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.

A data mining algorithm for fuzzy transaction data

2013 ◽  
Vol 48 (6) ◽  
pp. 2963-2971 ◽  
Author(s):  
Chin-Yuan Chen ◽  
Gin-Shuh Liang ◽  
Yuhling Su ◽  
Mao-Sheng Liao
Keyword(s):  
Data Mining ◽  
Data Mining Algorithm ◽  
Transaction Data ◽  
Mining Algorithm

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

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