MARKETING STRATEGY FOR THE DETERMINATION OF STAPLE CONSUMER PRODUCTS USING FP-GROWTH AND APRIORI ALGORITHM

The demand for staple products that vary among customers makes it necessary for the store to determine how the marketing strategy should be. Data mining are known as KDD (Knowledge Discovery in Database) is to digging up valuable knowledge from the data. Research purpose is to identify the right marketing strategy to sales the goods. The marketing strategy is took by analyze how much consumers demand for basic needs. The algorithms used in this research are FP (Frequent Pattern)-Growth and A-priori Algorithm. Finding combinations patterns between itemset using the Association Rule. FP-Growth algorithm is an algorithm that been used to determining a set of data in a data set that often appears on the frequency of the itemset. the KDD stages study are data cleansing, data integration, data selection, data transformation, data mining, pattern evaluation and knowledge presentation. the Testing used Rapidminer software with a minimum confidence value of 0.6 and a minimum support of 0.45. FP-Growth algorithm obtained 5 rule conclusions while Apriori Algorithm obtained 3 rule conclusions. The FP-Growth algorithm make a better decision rules than a priori algorithms in determining of marketing strategies, because it produces more decisions on how the goods sold.

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Penerapan Data Mining Apriori Pada Persediaan Obat (Studi Kasus Apotek Rafif Farma Medan)

ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA ◽

10.30829/algoritma.v3i2.6953 ◽

2019 ◽

Vol 3 (2) ◽

pp. 115

Author(s):

Mardiah Mardiah

Keyword(s):

Data Mining ◽

Phase Analysis ◽

Customer Service ◽

Association Rule ◽

High Frequency ◽

A Priori ◽

Inventory Systems ◽

Frequent Pattern ◽

Apriori Algorithm ◽

Analysis Algorithm

The importance of inventory systems at a pharmacy and the type of goods which are a top priority that must be in stock. It is useful to anticipate the void stuff. Due to the lack of inventory may affect customer service and asset to the pharmacy. Therefore, this study was conducted to help resolve those problems by designing a data mining application that serves to predict sales of the drug is needed most knowable a priori algorithm with the help of Tools Tanagra. One of the interesting association analysis phase analysis algorithm that generates a high frequency patterns (frequent pattern mining). Keywords: Data Mining, Apriori Algorithm, Association Rule

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Penerapan Data Mining Menggunakan Algoritma Apriori untuk Menentukan Pola Penyebab Gelandangan dan Pengemis

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020721376 ◽

2020 ◽

Vol 7 (2) ◽

pp. 229

Author(s):

Wirta Agustin ◽

Yulya Muharmi

Keyword(s):

Data Mining ◽

Association Rule ◽

Urban Areas ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Frequent Itemset ◽

Frequent Pattern ◽

Data Sets ◽

Apriori Algorithm ◽

Data Set

Gelandangan dan pengemis salah satu masalah yang ada di daerah perkotaan, karena dapat mengganggu ketertiban umum, keamanan, stabilitas dan pembangunan kota. Upaya yang dilakukan saat ini masih fokus pada cara penanganan gelandangan dan pengemis, belum untuk pencegahan. Salah satu cara yang bisa dilakukan adalah dengan menentukan pola usia gelandangan dan pengemis. Algoritma Apriori sebuah metode Association Rule dalam data mining untuk menentukan frequent itemset yang berfungsi membantu menemukan pola dalam sebuah data (frequent pattern mining). Perhitungan manual menggunakan algoritma apriori, menghasilkan pola kombinasi sebanyak 3 rules dengan nilai minimum support sebesar 30% dan nilai confidence tertinggi sebesar 100%. Pengujian penerapan Algoritma Apriori menggunakan aplikasi RapidMiner. RapidMiner salah satu software pengolahan data mining, diantaranya analisis teks, mengekstrak pola-pola dari data set dan mengkombinasikannya dengan metode statistika, kecerdasan buatan, dan database untuk mendapatkan informasi bermutu tinggi dari data yang diolah. Hasil pengujian menunjukkan perbandingan pola usia gelandangan dan pengemis yang berpotensi menjadi gelandangan dan pengemis. Berdasarkan hasil pengujian aplikasi RapidMiner dan hasil perhitungan manual Algoritma Apriori, dapat disimpulkan sesuai kriteria pengujian, bahiwa pola (rules) usia dan nilai confidence (c) hasil perhitungan manual Algoritma Apriori tidak mendekati nilai hasil pengujian menggunakan aplikasi RapidMiner, maka tingkat keakuratan pengujian rendah, yaitu 37.5 %. Abstract Homeless and beggars are one of the problems in urban areas as they possibly disrupt public order, security, stability and urban development. The efforts conducted are still focusing on managing the existing homeless and beggars instead of preventing the potential ones. One of the methods used for solving this problem is Algoritma Apriori which determines the age pattern of homeless and beggars. Apriori Algorithm is an Association Rule method in data mining to determine frequent item set that serves to help in finding patterns in a data (frequent pattern mining). The manual calculation through Apriori Algorithm obtains combination pattern of 3 rules with a minimum support value of 30% and the highest confidence value of 100%. These patterns were refences for the incharged department in precaution action of homeless and beggars arising numbers. Apriori Algorithm testing uses the RapidMiner application which is one of data mining processing software, including text analysis, extracting patterns from data sets and combining them with statistical methods, artificial intelligence, and databases to obtain high quality information from processed data. Based on the results of the said testing, it can be concluded that the level of accuracy test is low, i.e. 37.5%.

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IMPLEMENTASI ALGORITMA APRIORI TERHADAP DATA PENJUALAN PADA PERUSAHAAN RETAIL

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v15i1.113 ◽

2019 ◽

Vol 15 (1) ◽

pp. 85-90 ◽

Cited By ~ 3

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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Implementation of Apriori Algorithm Data Mining for Increase Sales

SinkrOn ◽

10.33395/sinkron.v5i1.10587 ◽

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.

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A Hybrid Method for Prediction and Assessment Efficiency of Decision Making Units

International Journal of Decision Support System Technology ◽

10.4018/jdsst.2013010104 ◽

2013 ◽

Vol 5 (1) ◽

pp. 66-83 ◽

Cited By ~ 1

Author(s):

Iman Rahimi ◽

Reza Behmanesh ◽

Rosnah Mohd. Yusuff

Keyword(s):

Data Mining ◽

Decision Making ◽

Decision Rules ◽

Large Data ◽

Poultry Meat ◽

Small Data ◽

Data Set ◽

Data Mining Techniques ◽

Decision Making Units

The objective of this article is an evaluation and assessment efficiency of the poultry meat farm as a case study with the new method. As it is clear poultry farm industry is one of the most important sub- sectors in comparison to other ones. The purpose of this study is the prediction and assessment efficiency of poultry farms as decision making units (DMUs). Although, several methods have been proposed for solving this problem, the authors strongly need a methodology to discriminate performance powerfully. Their methodology is comprised of data envelopment analysis and some data mining techniques same as artificial neural network (ANN), decision tree (DT), and cluster analysis (CA). As a case study, data for the analysis were collected from 22 poultry companies in Iran. Moreover, due to a small data set and because of the fact that the authors must use large data set for applying data mining techniques, they employed k-fold cross validation method to validate the authors’ model. After assessing efficiency for each DMU and clustering them, followed by applied model and after presenting decision rules, results in precise and accurate optimizing technique.

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Research into the Algorithm of Frequent Pattern Mining Based on across Linker

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.195-196.984 ◽

2012 ◽

Vol 195-196 ◽

pp. 984-986

Author(s):

Ming Ru Zhao ◽

Yuan Sun ◽

Jian Guo ◽

Ping Ping Dong

Keyword(s):

Data Mining ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Frequent Itemsets ◽

Frequent Pattern ◽

Apriori Algorithm ◽

Important Data ◽

Classical Algorithm ◽

Frequent Itemsets Mining ◽

Mining Frequent Itemsets

Frequent itemsets mining is an important data mining task and a focused theme in data mining research. Apriori algorithm is one of the most important algorithm of mining frequent itemsets. However, the Apriori algorithm scans the database too many times, so its efficiency is relatively low. The paper has therefore conducted a research on the mining frequent itemsets algorithm based on a across linker. Through comparing with the classical algorithm, the improved algorithm has obvious advantages.

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Association Rules Analysis on FP-Growth Method in Predicting Sales

10.31227/osf.io/8m57c ◽

2017 ◽

Author(s):

Andysah Putera Utama Siahaan ◽

Mesran Mesran ◽

Andre Hasudungan Lubis ◽

Ali Ikhwan ◽

Supiyandi

Keyword(s):

Data Mining ◽

Association Rules ◽

Frequent Itemset ◽

Frequent Pattern ◽

Data Set ◽

Pattern Processing ◽

Large Databases ◽

Growth Method ◽

Association Rules Analysis ◽

A Company

Sales transaction data on a company will continue to increase day by day. Large amounts of data can be problematic for a company if it is not managed properly. Data mining is a field of science that unifies techniques from machine learning, pattern processing, statistics, databases, and visualization to handle the problem of retrieving information from large databases. The relationship sought in data mining can be a relationship between two or more in one dimension. The algorithm included in association rules in data mining is the Frequent Pattern Growth (FP-Growth) algorithm is one of the alternatives that can be used to determine the most frequent itemset in a data set.

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Application Of Pizza Sales Data Mining Using Apriori Method

SinkrOn ◽

10.33395/sinkron.v4i2.10500 ◽

2020 ◽

Vol 4 (2) ◽

pp. 1 ◽

Cited By ~ 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.

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Implementasi Algoritma Apriori Pada Penyusunan Menu Makanan Rumah Makan Prasmanan

Jurnal Teknik Informatika dan Sistem Informasi ◽

10.28932/jutisi.v6i2.2742 ◽

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.

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PENERAPAN DATA MINING MENGGUNAKAN ALGORITMA C4.5 TEHADAP PENGARUH PENJUALAN KOPI PADA PT. JPW INDONESIA

Jurnal Sistem Informasi dan Informatika (Simika) ◽

10.47080/simika.v3i1.836 ◽

2020 ◽

Vol 3 (1) ◽

pp. 40-54

Author(s):

Ikong Ifongki

Keyword(s):

Data Mining ◽

Decision Tree ◽

Decision Rules ◽

Large Data ◽

Added Value ◽

Data Set ◽

Use Of Data ◽

Decision Tree Classification ◽

C4.5 Algorithm

Data mining is a series of processes to explore the added value of a data set in the form of knowledge that has not been known manually. The use of data mining techniques is expected to provide knowledge - knowledge that was previously hidden in the data warehouse, so that it becomes valuable information. C4.5 algorithm is a decision tree classification algorithm that is widely used because it has the main advantages of other algorithms. The advantages of the C4.5 algorithm can produce decision trees that are easily interpreted, have an acceptable level of accuracy, are efficient in handling discrete type attributes and can handle discrete and numeric type attributes. The output of the C4.5 algorithm is a decision tree like other classification techniques, a decision tree is a structure that can be used to divide a large data set into smaller sets of records by applying a series of decision rules, with each series of division members of the resulting set become similar to each other. In this case study what is discussed is the effect of coffee sales by processing 106 data from 1087 coffee sales data at PT. JPW Indonesia. Data samples taken will be calculated manually using Microsoft Excel and Rapidminer software. The results of the calculation of the C4.5 algorithm method show that the Quantity and Price attributes greatly affect coffee sales so that sales at PT. JPW Indonesia is still often unstable.

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