scholarly journals PENERAPAN DATA MINING MENGGUNAKAN METODE ASSICIATION RULE DENGAN ALGORITMA APRIORI UNTUK ANALISA POLA PENJUALAN BARANG

JURTEKSI ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 193-198
Author(s):  
Yori Apridonal M ◽  
Wirdah Choiriah ◽  
Akmal Akmal
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Frequent Itemset ◽  
Apriori Algorithm ◽  
Data Set ◽  
Processing Data ◽  
New Information ◽  
Alternative Algorithm ◽  
Rule Method ◽  
Events Data

Abstract: Fantasy Kids is a children's clothing distribution in the Bangkinang area, Kampar Regency, Riau. In its operations, distros sell their products to the general public, including the sale of children's shirts, children's shirts, jackets or children's sweaters which are usually sold in other distros. These distributions carry out product updates at certain events. Data Mining is the development or discovery of new information by looking for certain patterns or rules of a large amount of data expected to overcome these conditions. The method that will be used in the construction of this application is the Association Rule method with the Apriori Algorithm. Association Rule method is a procedure to find relationships between items in a specified data set. In determining a Association Rule, there is a measure of trust obtained from the results of processing data with certain calculations. Apriori Algorithm is an alternative Algorithm that can be used to determine the frequent itemset in a data set. Keywords : Data Mining, Algoritma, Apriori, Association Rule, Sales, Distro  Abstrak: Fantasy Kids merupakan sebuah distro baju anak-anak di kawasan Bangkinang, Kabupaten Kampar, Riau. Dalam operasionalnya, distro menjual produknya kepada masyarakat umum meliputi penjualan kaos anak, kemeja anak, bag, jaket atau sweater anak yang biasa dijual di distro-distro lainnya. Distro ini melakukan pembaruan produk pada event tertentu. Data Mining merupakan pegembangan atau penemuan informasi baru dengan mencari pola atau aturan tertentu dari sejumlah data dalam jumlah besar diharapkan dapat mengatasi kondisi tersebut. Metode yang akan digunakan dalam pembangunan aplikasi ini adalah metode Association Rule dengan Algoritma Apriori. Metode Association Rule adalah suatu prosedur untuk mencari hubungan antara item dalam suatu kumpulan data yang ditentukan. Dalam menentukan suatu Association Rule, terdapat suatu ukuran kepercayaan yang di dapatkan dari hasil pengolahan data dengan perhitungan tertentu. Algoritma Apriori merupakan salah satu alternatif Algoritma yang dapat digunakan untuk menentukan himpunan data yang paling sering muncul (frequent itemset) dalam suatu kumpulan data. Kata kunci: Data Mining, Algoritma, Apriori, Association Rule, Penjuaan, Distro

Penggunaan Association Rule Data Mining Untuk Menentukan Pola Lama Studi Mahasiswa F-MIPA UNSRAT

d'CARTESIAN ◽  
2014 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
M. Zainal Mahmudin ◽  
Altien Rindengan ◽  
Winsy Weku
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Large Data ◽  
Apriori Algorithm ◽  
Data Mining Algorithms ◽  
Adequate Information ◽  
Processing Data ◽  
Rule Method ◽  
Mining Algorithms ◽  
Confidence Value

Abstract The requirement of highest information sometimes is not balance with the provision of adequate information, so that the information must be re-excavated in large data. By using the technique of association rule we can obtain information from large data such as the college data. The purposes of this research is to determine the patterns of study from student in F-MIPA UNSRAT by using association rule method of data mining algorithms and to compare in the apriori method and a hash-based algorithms. The major’s student data of F-MIPA UNSRAT as a data were processed by association rule method of data mining with the apriori algorithm and a hash-based algorithm by using support and confidance at least 1 %. The results of processing data with apriori algorithms was same with the processing results of hash-based algorithms is as much as 49 combinations of 2-itemset. The pattern that formed between 7,5% of graduates from mathematics major that studied for more 5 years with confidence value is 38,5%. Keywords: Apriori algorithm, hash-based algorithm, association rule, data mining. Abstrak Kebutuhan informasi yang sangat tinggi terkadang tidak diimbangi dengan pemberian informasi yang memadai, sehingga informasi tersebut harus kembali digali dalam data yang besar. Dengan menggunakan teknik association rule kita dapat memperoleh informasi dari data yang besar seperti data yang ada di perguruan tinggi. Tujuan penelitian ini adalah menentukan pola lama studi mahasiswa F-MIPA UNSRAT dengan menggunakan metode association rule data mining serta membandingkan algoritma apriori dan algoritma hash-based. Data yang digunakan adalah data induk mahasiswa F-MIPA UNSRAT yang  diolah menggunakan teknik association rule data mining dengan algoritma apriori dan algoritma hash-based dengan minimum support 1% dan minimum confidance 1%. Hasil pengolahan data dengan algoritma apriori sama dengan hasil pengolahan data dengan algoritma hash-based yaitu sebanyak 49 kombinasi 2-itemset. Pola yang terbentuk antara lain 7,5% lulusan yang berasal dari jurusan matematika menempuh studi selama lebih dari     5 tahun dengan nilai confidence 38,5%. Kata kunci : Association rule data mining, algoritma apriori, algoritma hash-based

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

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

<p class="Judul2">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 <em>Association Rule</em> dalam data mining untuk menentukan frequent itemset yang berfungsi membantu menemukan pola dalam sebuah data (<em>frequent pattern mining</em>). Perhitungan manual menggunakan algoritma apriori, menghasilkan pola kombinasi sebanyak 3 rules dengan nilai minimum <em>support</em> sebesar 30% dan nilai <em>confidence</em> 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 %.</p><p class="Judul2"> </p><p class="Judul2"><strong><em>Abstract </em></strong></p><p class="Judul2"><strong> </strong></p><p><em>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%.</em></p>

scholarly journals Penerapan Metode Association Rule Untuk Menganalisa Pola Pemakaian Bahan Kimia Di Laboratorium Menggunakan Algoritma FP-Growth (Studi Kasus di Laboratorium Kimia PT. PLN (Persero) Sektor Pembangkitan Belawan Medan)

2019 ◽  
Vol 3 (2) ◽  
pp. 56
Author(s):  
Buyung Solihin Hasugian
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Frequent Itemset ◽  
Data Mining Technique ◽  
Data Set ◽  
Mining Technique ◽  
Several Variables ◽  
Alternative Algorithm ◽  
Chemical Usage ◽  
Tree Development

<p class="Default"><em>The pattern of using chemicals in the laboratory of PT. PLN (Persero) </em><em>Sektor Pembangkitan </em><em>Belawan Medan is not only to find out what chemicals are used but also to find out the amount of chemicals left so that laboratory officials can properly manage the use of these chemicals. One appropriate way to determine the pattern of use of these chemicals is to use data mining techniques. The Data Mining technique used in this case is the FP-Growth Algorithm. FP-Growth is an alternative algorithm that can be used to determine the most frequent set of data in a data set. The study was conducted using several variables, namely the date and chemicals used. The results of this study are in the form of a chemical usage pattern which is processed using software, namely implementing the FP-Growth algorithm using the concept of FP-Tree development in searching for Frequent Itemset.</em></p><p class="Default"><em> </em></p><pre><em>Keywords: Data Mining, Association Rules, Frequent Itemset, FP-Growth</em></pre>

scholarly journals Data Mining Menggunakan Algoritma Apriori untuk Rekomendasi Produk bagi Pelanggan

2019 ◽  
Vol 7 (3) ◽  
pp. 103-108
Author(s):  
Ariefana Ria Riszky ◽  
Mujiono Sadikin
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Marketing Strategy ◽  
Association Rule ◽  
Apriori Algorithm ◽  
Processing Data ◽  
Total Data ◽  
Product Recommendations ◽  
Rule Method ◽  
Confidence Value

The implementation of a marketing strategy requires a reference so that promotion can be on target, such as by looking for similarities between product items. This study examines the application of the association rule method and apriori algorithm to the purchase transaction dataset to assist in forming candidate combinations among product items for customer recommended product promotion. The purchase transaction dataset was collected in October and November 2018 with a total data of 1027. In the experiment, the minimum value of support is 85%, and the minimum confidence value is 90% by processing data using the Weka software 3.9 version. Apriori algorithm can form association rules as a reference in the promotion of company products and decision support in providing product recommendations to customers based on defined minimum support and confidence values.

scholarly journals Aplikasi Web Usage Mining Menggunakan Metode Association Rule Dengan Algoritma Fp-Growth Untuk Mengetahui Pola Browsing Pengunjung Website

2019 ◽  
Vol 1 ◽  
pp. 1060
Author(s):  
Yori Apridonal M ◽  
Febri Dristyan ◽  
Afdhal Syafnur
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Layout Design ◽  
Association Pattern ◽  
Processing Data ◽  
Alternative Algorithm ◽  
Rule Method ◽  
Using Data ◽  
Confidence Value ◽  
The Web

As a way to improve the promotion of institutions via the web, there is a need for a method to view browsing patterns of visitors on the site unilak.ac.id, thereby showing the user's interest in the links he visits. Data mining or knowledge discovery is a process of extracting valuable information by analyzing the existence of certain patterns or relationships. To find visitor patterns in the form of association rules is to use the association rule method. FP-Growth is an alternative algorithm that can be used to determine the most frequent set of data in a set of data. FP-Growth is applied to get a pattern of visitors, about what links are frequently visited and seen by visitors on the site unilak.ac.id. This pattern is used to help web administrators in developing the site unilak.ac.id by utilizing knowledge from the association pattern to regulate the layout / layout design of the categories available on the site unilak.ac.id. From the results of processing the dataset with FP-Growth algorithm and processing data processed using data mining software, namely Rapidminer 6.5. It was found that the minimum value of support was 1% and the minimum confidence value of 50% resulted in 124 rules of association.

scholarly journals Implementasi Algoritma Apriori Pada Penyusunan Menu Makanan Rumah Makan Prasmanan

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.  

scholarly journals Application of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom

2021 ◽  
Vol 1 (2) ◽  
pp. 54-66
Author(s):  
M. Hamdani Santoso
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
A Priori ◽  
Apriori Algorithm ◽  
Rule Discovery ◽  
Association Rule Discovery ◽  
Rule Method ◽  
Confidence Value ◽  
Method Show

Data mining can generally be defined as a technique for finding patterns (extraction) or interesting information in large amounts of data that have meaning for decision support. One of the well-known and commonly used association rule discovery data mining methods is the Apriori algorithm. The Association Rule and the Apriori Algorithm are two very prominent algorithms for finding a number of frequently occurring sets of items from transaction data stored in databases. The calculation is done to determine the minimum value of support and minimum confidence that will produce the association rule. The association rule is used to produce the percentage of purchasing activity for an itemset within a certain period of time using the RapidMiner software. The results of the test using the priori algorithm method show that the association rule, that customers often buy toothpaste and detergents that have met the minimum confidence value. By searching for patterns using this a priori algorithm, it is hoped that the resulting information can improve further sales strategies.

scholarly journals PENERAPAN DATA MINING MENGGUNAKAN ALGORITMA APRIORI UNTUK MENENTUKAN POLA GOLONGAN PENYANDANG MASALAH KESEJAHTERAAN SOSIAL

Sebatik ◽  
2022 ◽  
Vol 26 (1) ◽  
Author(s):  
Irwan Adji Darmawan ◽  
Muhammad Fakhri Randy ◽  
Imam Yunianto ◽  
Muhamad Malik Mutoffar ◽  
M Tio Putra Salis
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Itemset ◽  
Frequent Pattern ◽  
Data Set ◽  
Minimum Support

Penyandang Masalah Kesejahteraan Sosial (PMKS) menjadi satu dari sekian masalah yang terdapat di daerah perkotaan, sebab dapat mengganggu pembangunan kota, ketertiban umum, keamanan dan stabilitas. Sejauh ini langkah yang dilakukan sementara masih terfokus dengan cara penanganan PMKS, masih belum mengarah untuk mencegah. Menentukan pola golongan PMKS merupakan salah satu cara yang dapat dilakukan. Algoritma Apriori memiliki fungsi untuk membantu menemukan pola yang terdapat pada data (frequent pattern mining) untuk menentukan frequent itemset yang menggunakan metode Association Rule dalam data mining. Dalam penghitungan secara manual yang dilakukan maka didapat pola kombinasi antara lain 3 rules yang memiliki nilai minimum support 15% dengan confidence tertinggi 100% menggunakan Algoritma Apriori. Dalam menguji Algoritma Apriori digunakan aplikasi RapidMiner. RapidMiner merupakan satu dari beberapa software pengolah data mining, misalnya menganalisis teks, mengekstrak pola data set kemudian dikombinasikan menggunakan metode statistik, database, dan kecerdasan buatan agar didapat informasi yang tinggi berasal dari olahan data. Hasil yang didapat dari pengujian perbandingan pola antar golongan PMKS. Dari pengujian menggunakan aplikasi RapidMiner dan penghitungan secara manual Algoritma Apriori, maka disimpulkan dengan kriteria pengujian, bahwa pola (rules) golongan dengan nilai confidence (c) penghitungan manual Algoritma Apriori dapat dibilang tidak mendekati hasil pengujian aplikasi RapidMiner, maka dapat dikatakan tingkat keakuratan pengujian rencah, hanya 37,5%.

scholarly journals Penerapan Data Mining dengan Memanfaatkan Metode Association Rule untuk Promosi Produk

2018 ◽  
Vol 3 (1) ◽  
pp. 89
Author(s):  
Rintho Rante Rerung
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Association Rule ◽  
Frequent Itemset ◽  
The Other ◽  
Apriori Algorithm ◽  
Online Social Media ◽  
Knowing That ◽  
Other Hand ◽  
Mining Association Rule

Dalam suatu bisnis diperlukan upaya memaksimalkan keuntungan diantaranya dengan melakukan promosi. Banyak cara yang bisa dilakukan untuk mempromosikan produk seperti dengan cara online dengan memanfaatkan media sosial Facebook dan situs-situs yang menyediakan iklan. Namun demikian, untuk memperoleh hasil yang maksimal maka perlu dilakukan perhitungan seberapa besar kemungkinan pelanggan akan tertarik terhadap produk yang ditawarkan. Penelitian ini bertujuan untuk menerapkan data mining untuk promosi produk Distro Nasional. Dalam bidang keilmuan data mining, terdapat suatu metode yang dinamakan association rule. Metode ini bertujuan untuk menunjukkan nilai asosiatif antara jenis-jenis produk yang dibeli oleh pelanggan sehingga terlihatlah suatu pola berupa produk apa saja yang sering dibeli oleh palanggan tersebut. Dengan mengetahui jenis produk yang sering dibeli maka dapat dibuat sebagai sebuah dasar keputusan untuk menentukan produk apa saja yang cocok untuk dipromosikan kepada pelanggan tersebut. Algoritma Apriori juga akan dipergunakan untuk menentukan frequent itemset sehingga hasil akhir yang dicapai yaitu untuk menghitung persentase ketertarikan (confindence) pelanggan terhadap produk yang ditawarkan.Kata kunci: promosi, data mining, association rule, produk In a business, there is required efforts to maximize profits include by promotion. Many ways can be conducted in promoting a product such as by using Facebook as an online social media and sites which provide advertisements. On the other hand, in gaining a maximum result is required a calculation about how big customer probability to get interested in a product offered. This study aims to apply data mining for product promotion of Distro Nasional store. In a science of data mining there is a method called association rule. This method was intended to indicate associative values among product types were bought by customers. So that, it can be seen a pattern which types of product that often bought by customers. By knowing that information it can be made as a decission base to determine which appropriate products get promotted to that customer. Apriori algorithm will also be used to determine the frequent itemset so that the final result achieved is to calculate the percentage of customer interest (confindence) on the product offered.Keywords: promotion, data mining, association rule, product 

scholarly journals A Novelty of Data Mining for Promoting Education based on FP-Growth Algorithm

2018 ◽  
Author(s):  
Andysah Putera Utama Siahaan ◽  
Ali Ikhwan ◽  
Solly Aryza
Keyword(s):  
Data Mining ◽  
Research Result ◽  
Frequent Itemset ◽  
Apriori Algorithm ◽  
Data Set ◽  
Home Address ◽  
Promotional Strategies ◽  
The University ◽  
Tree Development

The development of education at this time the increasing number of campuses are growing. Therefore every university wants to gain a lot of students in promoting the university. Many ways can be done for the determination of promotional strategies one of them by using techniques that exist in the data mining. The method used in this research by using the FP-Growth Algorithm. The FP-Growth algorithm is one of the alternative algorithms that can be used to select the most common data stack (Frequent Item Set) in a data set. The FP-Growth algorithm is a development of the Apriori algorithm. As for some events in FP-Growth does not generate candidate because FP-Growth has the concept of Tree build in doing Itemset search. This research is done by studying some research which is often considered by great jokes especially marketing part in determining promotion what become its target. Variables used are Last Education, Home Address, Department, Choice Prodi. The Research Result is a software system for implementing The FP-Growth algorithm that uses the FP-Tree Development concept in finding Frequent Itemset.

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