scholarly journals Rekomendasi Paket Menu Angkringan Waru Tanjung Bias Dengan Algoritma Frequent Pattern Growth Berbasis Web

2021 ◽  
Vol 3 (2) ◽  
pp. 92-98
Author(s):  
Lalu Aldila Maulana Fajar ◽  
Ria Rismayati
Keyword(s):  
Data Mining ◽  
Millennial Generation ◽  
Frequent Pattern ◽  
Growth Data ◽  
Transaction Data ◽  
Data Mining Techniques ◽  
Pattern Growth ◽  
The Many ◽  
Package Recommendations ◽  
Data Mining Application

Culinary business using carts selling various kinds of heavy food, light and drinks, is favored by many people to just fill their stomachs, gather with friends and even family. Culinary businesses or culinary destinations like this are known as Angkringan which are increasingly mushrooming in the millennial generation. Angkringan Waru, located in Tanjung Bias, is a gathering destination for all people to enjoy a relaxed atmosphere on the beach. Angkringan Waru provides 85 types of menus for its customers, the many menus often confuse customers in choosing snacks while enjoying the beachside atmosphere. Starting from these problems, data mining techniques are used with the Frequent Pattern Growth (Fp-Growth) algorithm to recommend items in producing a menu package consisting of 1 snack item and 1 drink item. The dataset used is transaction data from Angkringan Waru as many as 870 transactions, the resulting output is a menu package recommendation rule and implemented in a web for Angkringan Waru. The Fp-Growth Data Mining Application by providing a minimum support value of 20% and Confident 50% with a lift ratio > 1 produces 57 rules or menu package recommendations that will be offered to Angkringan Waru customers. The results of the application in the form of 57 menu package recommendations are then used as recommendations for Angkringan Waru customers, where these menus are the favorite menus of customers at Angkringan Waru.

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 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.

Penerapan Metode Klasifikasi Decision Tree dan Algoritma C4.5 dalam Memprediksi Kriteria Nasabah Kredit Mega Auto Finance

2020 ◽  
Vol 7 (2) ◽  
pp. 200
Author(s):  
Puji Santoso ◽  
Rudy Setiawan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Microsoft Excel ◽  
Customer Data ◽  
Data Mining Techniques ◽  
C4.5 Algorithm ◽  
Marketing Costs ◽  
Excel Format ◽  
Data Mining Application

One of the tasks in the field of marketing finance is to analyze customer data to find out which customers have the potential to do credit again. The method used to analyze customer data is by classifying all customers who have completed their credit installments into marketing targets, so this method causes high operational marketing costs. Therefore this research was conducted to help solve the above problems by designing a data mining application that serves to predict the criteria of credit customers with the potential to lend (credit) to Mega Auto Finance. The Mega Auto finance Fund Section located in Kotim Regency is a place chosen by researchers as a case study, assuming the Mega Auto finance Fund Section has experienced the same problems as described above. Data mining techniques that are applied to the application built is a classification while the classification method used is the Decision Tree (decision tree). While the algorithm used as a decision tree forming algorithm is the C4.5 Algorithm. The data processed in this study is the installment data of Mega Auto finance loan customers in July 2018 in Microsoft Excel format. The results of this study are an application that can facilitate the Mega Auto finance Funds Section in obtaining credit marketing targets in the future

scholarly journals Advanced Data Mining of SSD Quality Based on FP-Growth Data Analysis

2021 ◽  
Vol 11 (4) ◽  
pp. 1715
Author(s):  
Jieh-Ren Chang ◽  
You-Shyang Chen ◽  
Chien-Ku Lin ◽  
Ming-Fu Cheng
Keyword(s):  
Data Mining ◽  
Customer Service ◽  
Flash Memory ◽  
Association Rule ◽  
Production Control ◽  
Frequent Pattern ◽  
Nand Flash ◽  
Growth Data ◽  
Nand Flash Memory ◽  
Service Strategy

Storage devices in the computer industry have gradually transformed from the hard disk drive (HDD) to the solid-state drive (SSD), of which the key component is error correction in not-and (NAND) flash memory. While NAND flash memory is under development, it is still limited by the “program and erase” cycle (PE cycle). Therefore, the improvement of quality and the formulation of customer service strategy are topics worthy of discussion at this stage. This study is based on computer company A as the research object and collects more than 8000 items of SSD error data of its customers, which are then calculated with data mining and frequent pattern growth (FP-Growth) of the association rule algorithm to identify the association rule of errors by setting the minimum support degree of 90 and the minimum trust degree of 10 as the threshold. According to the rules, three improvement strategies of production control are suggested: (1) use of the association rule to speed up the judgment of the SSD error condition by customer service personnel, (2) a quality strategy, and (3) a customer service strategy.

scholarly journals Penerapan Data Mining Untuk Analisa Pola Pembelian Produk Menggunakan Algoritma Frequent Pattern – Growth

Rekayasa ◽  
2022 ◽  
Vol 14 (3) ◽  
pp. 456-460
Author(s):  
Paisal Soleh ◽  
Abu Tholib ◽  
M. Noer Fadli Hidayat
Keyword(s):  
Data Mining ◽  
Frequent Pattern ◽  
Pattern Growth

ANALISIS KINERJA ALGORITMA FREQUENT PATTERN GROWTH (FP-GROWTH) PADA PENAMBANGAN POLA ASOSIASI DATA TRANSAKSI

2019 ◽  
Vol 1 (1) ◽  
pp. 9-12
Author(s):  
Putri Kurnia Handayani ◽  
Nanik Susanti
Keyword(s):  
Data Mining ◽  
Frequent Itemset ◽  
Frequent Pattern ◽  
Pattern Growth

Data transaksi penjualan yang setiap hari bertambah menyebabkan banjir data dalam database. Data transaksi tersebut hanya digunakan sebagai laporan penjualan yang dicetak setiap bulannya. Data mining merupakan kegiatan menambang/menggali data untuk mengenali pola atau aturan tertentu dari sejumlah dataset yang sangat besar dan mempunyai dimensi tinggi. Asosiasi adalah teknik data mining untuk menemukan aturan suatu kombinasi item. Pola asosiasi yang berhasil diketahui dapat membantu pihak manajemen untuk mendukung pengambilan keputusan berkaitan dengan strategi penjualan, promosi produk, reward bagi pelanggan dan kendali stok. Penggalian pola asosiasi menggunakan algoritma FP-Growth melalui 3 tahap, yaitu pembangkitan conditional pattern base, conditional pattern tree dan pencarian frequent itemset. Metode perancangan sistem menggunakan UML. Tujuan penelitian ini adalah untuk menghasilkan sebuah sistem yang dapat mengenali pola asosiasi produk pada database.

scholarly journals A DATA MINING APPLICATION: ANALYSIS OF PROBLEMS OCCURRING DURING A SOFTWARE PROJECT DEVELOPMENT PROCESS

2005 ◽  
Vol 15 (04) ◽  
pp. 647-663 ◽  
Author(s):  
RICHI NAYAK ◽  
TIAN QIU
Keyword(s):  
Data Mining ◽  
Software Engineering ◽  
Development Process ◽  
Software Project ◽  
Data Mining Techniques ◽  
Application Analysis ◽  
Potential Benefits ◽  
Future Prediction ◽  
Hidden Data ◽  
Data Mining Application

Data mining techniques provide people with new power to research and manipulate the existing large volume of data. A data mining process discovers interesting information from the hidden data that can either be used for future prediction and/or intelligently summarising the details of the data. There are many achievements of applying data mining techniques to various areas such as marketing, medical, and financial, although few of them can be currently seen in software engineering domain. In this paper, a proposed data mining application in software engineering domain is explained and experimented. The empirical results demonstrate the capability of data mining techniques in software engineering domain and the potential benefits in applying data mining to this area.

scholarly journals Pola Beli Konsumen Menggunakan Algoritma Fp-Growth Untuk Rekomendasi Promosi Pada Aneka Jaya Motor

2021 ◽  
Vol 6 (1) ◽  
pp. 48-55
Author(s):  
Junta Zeniarja
Keyword(s):  
Data Mining ◽  
Business Strategy ◽  
Recommendation System ◽  
Transaction Data ◽  
Data Mining Techniques ◽  
Minimum Support ◽  
Spark Plug ◽  
Using Data ◽  
Product Promotion

A piece of appropriate information can create and establish a business strategy in increasing sales through technology that can affect the trade-in buying and selling goods with the data information generated can be calculated in detail and accurately. At Aneka Jaya Motor Semarang, this was triggered by the demand for competition. One solution is a product promotion target. For determining which items are feasible for promotion, the application of a promotional decision recommendation system is made using data mining techniques associated with FP-Growth algorithms, its function is to find items that are often purchased simultaneously by consumers. Data used in the form of transaction data with the total amount used 501 data. The results obtained by appearing 1 rule is if consumers buy spark plug parts then buy oil parts with minimum support of 10% and minimum confidence of 35%. The lift ratio obtained is 1 so that valid rules are generated.

scholarly journals Analisa Pola Frekuensi Keranjang Belanja Dengan Dengan Perbandingan Algoritma Fp-Growth (Frequent Pattern Growth) dan Eclat pada minimarket

2020 ◽  
Vol 7 (2) ◽  
pp. 364-373
Author(s):  
Krisna Nata Wijaya
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Frequent Pattern ◽  
Minimum Support ◽  
Pattern Growth

Dalam kegiatan transaksi jual beli di minimarket ataupun toko pemilik harus mengerti apa yang diinginkan komsumen dalam memberikan kenyaman berbelanja, terutama kemudahan dalam pemilihan barang yang disesuaikan dengan tata letak atau penempatan barang. Dengan menerapkan association rule pada data transaksi akan memudahkan pemilik dalam mengelolah informasi penjualan dan mencari itemset. Oleh karena itu, penelitian ini Melakukan analisis pola data transaksi penjualan dengan menerapkan metode asosiasi pada data mining. Selanjutnya dengan melakukan perbandingan algoritma Fp-Growth dan Eclat dengan minimum support dan confidence sebesar 0.01% untuk menentukan jumlah aturan yang terbentuk sebagai bahan pengambil keputusan yang ditunjukan untuk frekuensi keranjang belanja.

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