scholarly journals Pemanfaatan Teknologi dalam Pengelompokkan Produk pada Minimarket

2021 ◽  
pp. 44-49
Author(s):  
Eka Praja Wiyata Mandala ◽  
Dewi Eka Putri
Keyword(s):  
Software Testing ◽  
Retail Industry ◽  
Test Results ◽  
Transaction Data ◽  
Cluster 2 ◽  
Study Manual

The retail industry is currently growing rapidly, especially in Indonesia. One form of the retail industry is modern retail which includes supermarkets, minimarkets and others. This study focuses on the grouping of products sold at minimarkets. This research is caused by seeing the phenomenon of the large number of transactions that occur in one day, the result is the number of products sold. This makes it difficult for minimarket managers to determine the next product procurement. Therefore, This study is conducted to group the products sold so that the products that need to be procured are seen next. This study propose a software to perform the grouping using the K-means algorithm. For the data sample, this study obtained sales transaction data for 3 months from the Sastra Mart minimarket. In this study, manual calculations were carried out on 10 samples of beverage data taken randomly from sales transactions which would be divided into 3 clusters. The results of manual calculations, there are 3 drink data entered into the “Sangat Laris” cluster, 2 drink data entered the “Laris” cluster and 5 drink data entered the “Kurang Laris” cluster. The software produced from the research gives the same results as manual calculations in classifying products. This study has also carried out software testing to test all its functionalities, from the test results, everything runs normally and as expected.

scholarly journals SISTEM APLIKASI BERBASIS OPTIMASI METODE ELBOW UNTUK PENENTUAN CLUSTERING PELANGGAN

JOUTICA ◽  
2018 ◽  
Vol 3 (1) ◽  
pp. 117 ◽  
Author(s):  
Elly Muningsih ◽  
Sri Kiswati
Keyword(s):  
Data Center ◽  
Clustering Algorithm ◽  
Visual Basic ◽  
Customer Management ◽  
Number Of Clusters ◽  
Transaction Data ◽  
Development Method ◽  
Popular Method ◽  
Or Groups ◽  
Cluster 2

Customer is a very important asset for the company. Having customers who are loyal to the company is an absolute and important for the progress of the company. This study aims to help companies, especially in the online shop to create a better customer management by identifying and grouping customers into several clusters or groups to know the characteristics of their loyalty to the company. The method used in this research is K-Means method which is one of the best and most popular method in clustering algorithm. To overcome the weakness of the K-Means method in determining the number of clusters, we use the Elbow method where this method gets the comparison of the number of clusters added by calculating the SSE (Sum of Square Error) of each cluster value. This research starts from collecting the necessary data and will be processed. From total transaction data 478 then done cleaning of data and result 73 data. Then the data processed with RapidMiner software from Cluster 2 up to 10 to search the data center of each cluster. From the calculated SSE value found that the best number of clusters is 3. The end result of the research is a Visual Basic based application program that is expected to provide ease in grouping or clustering customers. Software development method using Waterfall method.

scholarly journals Goods Stock Management using the K-Means Algorithm Method

2020 ◽  
Vol 10 (1) ◽  
pp. 22-45
Author(s):  
Dhio Saputra
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Test Results ◽  
Stock Management ◽  
Clustering Method ◽  
Sales Data ◽  
Using Data ◽  
Cluster 2

The grouping of Mazaya products at PT. Bougenville Anugrah can still do manuals in calculating purchases, sales and product inventories. Requires time and data. For this reason, a research is needed to optimize the inventory of Mazaya goods by computerization. The method used in this research is K-Means Clustering on sales data of Mazaya products. The data processed is the purchase, sales and remaining inventory of Mazaya products in March to July 2019 totaling 40 pieces. Data is grouped into 3 clusters, namely cluster 0 for non-selling criteria, cluster 1 for best-selling criteria and cluster 2 for very best-selling criteria. The test results obtained are cluster 0 with 13 data, cluster 1 with 25 data and cluster 2 with 2 data. So to optimize inventory is to multiply goods in cluster 2, so as to save costs for management of Mazayaproducts that are not available. K-Means clustering method can be used for data processing using data mining in grouping data according to criteria.

scholarly journals PENERAPAN ALGORITMA APRIORI DALAM MENENTUKAN POLA PEMBELIAN KONSUMEN DI KAFE HIDDEN TOAST AND FLOAT

2019 ◽  
Vol 4 (2) ◽  
pp. 83-88
Author(s):  
Ridwan Rismanto ◽  
Lucki Darmawan ◽  
Arief Prasetyo
Keyword(s):  
A Priori ◽  
Real Data ◽  
Test Results ◽  
Transaction Data ◽  
Online Purchases ◽  
Sample Data ◽  
Purchasing Patterns ◽  
The City ◽  
Computerized System ◽  
Confidence Value

Progress in Information Technology encourages culinary businesses to innovate, one of them is a computerized system, online-based sales and several interesting features that can increase consumer interest and increase sales to be the most frequently used innovation today. The cafe "Hidden Toast and Float" is a cafe in the City of Kediri. To increase sales from the cafe, a system is needed to facilitate the owner in recording sales and increasing the number of sales by providing automatic menu recommendations to customers. Based on the problem, in this thesis a website-based sales system and sales system will be created that is accompanied by the application of a priori algorithm to determine the purchasing patterns of customers and automatic menu recommendations from the system for customers. The test results of this thesis are two website-based systems with admin systems used to process existing data on the database and customer websites that are used for online purchases, as well as the application of a priori algorithms with the results of testing sample data and real data that produce menu combination recommendations. most often purchased based on all transaction data, namely Dark Choco Jam and Cappucino with a support value of 15% and a confidence value of 45%.

scholarly journals Pengujian Black Box pada Aplikasi Perpustakaan Menggunakan Teknik Equivalence Partitions

2020 ◽  
Vol 3 (3) ◽  
pp. 150
Author(s):  
Bayu Aji Priyaungga ◽  
Dwi Bayu Aji ◽  
Mukron Syahroni ◽  
Nurul Tri Sukma Aji ◽  
Aries Saifudin
Keyword(s):  
Software Testing ◽  
Source Code ◽  
Black Box ◽  
Test Results ◽  
Black Box Testing ◽  
System Errors ◽  
Transaction Activities

The library application is used to help manage and document transaction activities in the library which include borrowing and repaying. Library applications must be free of errors, because if they contain errors can cause harm to providers, managers, or members of the library. To provide a guarantee that the library application is free from errors, testing needs to be done. Software testing is an activity aimed at finding and finding errors and bugs in an application, which aims to minimize the losses that occur due to system errors. In this study, the proposed library application testing is Black Box. Black Box Testing is a test based on the appearance (interface) and functions of the software itself and not from the source code of the program. Black Box Testing has several methods, one of which is Equivalence Partitions, the method we use for testing the software. Equivalence Partitions are methods that discuss valid or not entered into the software, and observe the accuracy of the input. So that the side of the error is known. The test results have proven that the library application that has been developed is error free and meets all the requirements set.

scholarly journals Implementasi Algoritma Apriori Terhadap Data Penjualan Oli Mobil Berbasis Sistem Pencarian Aturan Asosiasi

2019 ◽  
Vol 3 (2) ◽  
pp. 316
Author(s):  
Jorza Rulianto ◽  
Wida Prima Mustika
Keyword(s):  
Data Mining ◽  
Pattern Recognition ◽  
Software Testing ◽  
A Priori ◽  
Marketing Strategies ◽  
Motor Oil ◽  
Transaction Data ◽  
Data Mining Techniques ◽  
Pattern Recognition Techniques ◽  
Mathematical Techniques

Data mining techniques are used to design effective sales or marketing strategies by utilizing sales transaction data that is already available in the company. The problem in the company is that there are many data transactions that occur unknown, causing an accumulation of data unknown sales most in each month & year, unknown brands of car oil are often sold or demanded by customers. So this association search uses a priori algorithm as a place to store data using pattern recognition techniques such as static and mathematical techniques from a set of relationships (associations) between items obtained, it is expected that can help developers in designing marketing strategies for goods in the company. Software testing results that have been made have found the most sold oil brand products if you buy Shell Hx7, it will buy Toyota Motor Oil with 50% support and 66.7% confidence. If you buy Toyota Motor Oil, you will buy Shell Hx 7 with 50% support and 85.7% confidence.

scholarly journals Penggunaan Matlab dan Python dalam Klasterisasi Data

2020 ◽  
Vol 20 (1) ◽  
pp. 103-118
Author(s):  
Herlawati Herlawati ◽  
Rahmadya Trias Handayanto
Keyword(s):  
Programming Languages ◽  
Data Clustering ◽  
The Internet ◽  
Test Results ◽  
Great Demand ◽  
Cluster Distribution ◽  
Center Point ◽  
Past Data ◽  
Cluster 2

Abstract   Organizations need to dig through the data clustering process, both past data and data from the internet. Sometimes the data has to be re-clustered to match the actual conditions. Therefore, it is necessary to prepare clustering support equipment. In this study the K-Means method was chosen for comparing two technical computational languages, i.e. Matlab and Python which are currently in great demand by researchers and can be used by organizations for a clustering process. This study showed both Matlab and Python have enough libraries (libraries) and toolboxes to help users in data clastering as well as graphics presentation. The test results show that the two programming languages are capable of carrying out the clustering process with two clusters; cluster 1 with a center point at coordinates (1.24, 1.34) and cluster 2 with a center point at coordinates (3.1, 3.07) and are presented by a cluster distribution plot.   Keywords: Clusterization, K-Means, Matlab, Python.   Abstrak   Organisasi perlu menggali data lewat proses klasterisasi data, baik data lampau maupun data dari internet. Terkadang data harus dilakukan klasterisasi ulang untuk mencocokan dengan kondisi yang sebenarnya. Oleh karena itu perlu dipersiapkan peralatan pendukung klasterisasi. Dalam penelitian ini metode K-Means dipilih untuk membandingkan dua bahasa komputasi teknis yaitu Matlab dan Python yang sekarang ini banyak diminati para peneliti yang dan dapat digunakan oleh organisasi yang membutuhkan proses klasterisasi. Hasil dari penelitian ini menunjukan baik Matlab maupun Python memiliki cukup pustaka (library) dan toolbox dalam membantu pengguna mengklasterisasi data, mempresentasikan grafik. Hasil pengujian menunjukan kedua Bahasa pemrograman mampu menjalankan proses klasterisasi berupa klaster 1 yang memiliki titik pusat yang berada pada koordinat (1.24, 1.34) dan klaster 2 dengan titik pusat yang berada pada koordinat (3.1, 3.07) disertai dengan plot sebaran klasternya.   Kata kunci: Klasterisasi, K-Means, Matlab, Python.

scholarly journals Outlier Detection Credit Card Transactions Using Local Outlier Factor Algorithm (LOF)

2019 ◽  
Vol 13 (4) ◽  
pp. 409
Author(s):  
Silvano Sugidamayatno ◽  
Danang Lelono
Keyword(s):  
Credit Card ◽  
Service Providers ◽  
Test Results ◽  
Analysis Method ◽  
Outlier Analysis ◽  
Transaction Data ◽  
Local Outlier Factor ◽  
Average Value ◽  
Average Accuracy ◽  
Local Outlier

Threats or fraud for credit card owners and banks as service providers have been harmed by the actions of perpetrators of credit card thieves. All transaction data are stored in the bank's database, but are limited in information and cannot be used as a knowledge. Knowledge built with credit card transaction data can be used as an early warning by the bank. The outlier analysis method is used to build the knowledge with a local outlier factor algorithm that has high accuracy, recall, and precision results and can be used in multivariate data. Testing uses a matrix sample and confusion method with attributes date, categories, numbers, and countries. The test results using 1803 transaction data from five customers, indicating that the average value accuracy of LOF algorithms (96%), higher than the average accuracy values of the INFLO and AFV algorithms (84% and 77%).

scholarly journals IMPLEMENTASI DATA MINING PADA PENJUALAN PRODUK DI CV CAHAYA SETYA MENGGUNAKAN ALGORITMA FP-GROWTH

Petir ◽  
2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Wahyu Nur Setyo ◽  
Sukma Wardhana
Keyword(s):  
Data Mining ◽  
Large Volume ◽  
Association Rule ◽  
Computer Systems ◽  
Frequent Pattern ◽  
Retail Industry ◽  
Transaction Data ◽  
Pattern Growth ◽  
Transaction Database ◽  
Mining Association Rule

At this time the growth of data occurs rapidly and rapidly along with the use of computer systems in various transactions. But this increasingly large volume of data has no meaning if it is not processed into a knowledge where this is done by data mining. Association rule or what is known as market based analysis is one type of data mining implementation. This study aims to find patterns of transaction data in the CV Cahaya Setya retail industry by using an Frequent Pattern Growth algorithm or also known as FP-Growth algorithm. FP-Growth aims to find all the set items that can be retrieved (often found) from the transaction database as efficiently as possible. The results of this study show that the pattern on the database of consumer transactions at CV Cahaya Setya retail industry is can be found using the FP-Growth algorithm then implementing it in the application.

scholarly journals Analysis of Information Technology (IT) Goods Sales Patterns Using the FP-Growth Algorithm

2022 ◽  
pp. 130-141
Author(s):  
Rizky Wandri ◽  
Anggi Hanafiah
Keyword(s):  
Information Technology ◽  
Test Results ◽  
Transaction Data ◽  
Sales Data ◽  
Effective Strategies ◽  
Minimum Value ◽  
New Knowledge ◽  
Testing Data ◽  
Effective Analysis

Determination of sales patterns is very important in marketing. Sales pattern serves to conduct an effective analysis in improving marketing. Sales analysis aims to explore new knowledge that can help design effective strategies by utilizing sales transaction data. This study processes sales data for 12 transaction days containing 47 items using the Fp-Growth algorithm. The results of this study are items with a minimum value of support > 0.10 and confidence 0.60 and will be compared with testing data using RapidMiner to test whether the results are valid so that the test results can help in designing sales strategies.

scholarly journals Implementasi Algoritma FG-Growth untuk Sistem Rekomendasi Penjualan Produk

2021 ◽  
Vol 5 (1) ◽  
pp. 41
Author(s):  
Siti Yuliyanti
Keyword(s):  
Association Rule ◽  
Frequent Itemset ◽  
Test Results ◽  
Rule Mining ◽  
Transaction Data ◽  
Data Object ◽  
Sample Data ◽  
Product Sales ◽  
Processing Techniques ◽  
Confidence Value

The variety of stationery marketed, makes business competition increasingly fierce in order to provide the best service to customers. Abundant sales transaction data, triggering piles of data so that it requires data mining processing techniques, namely association rule mining using the FP-Growth algorithm. Algorithm that generates frequent itemset used in the process of determining the rules that can produce an option by taking a product sales transaction data object. The test results show a rule that has the best confidence value and lift ratio of 100%, as well as 80% support with the rules that every purchase of a ballpoint product can be sure to buy a notebook from the dataset used as a sample data in the system trial (50 names). goods and 7 transaction data) with minimum support (5% = 0.05) and minimum confidence (30% = 0.3).

Sign in / Sign up

Export Citation Format

Share Document