scholarly journals Penerapan Metode Association Rule Mining Pada Data Transaksi Penjualan Produk Kartu Perdana Kuota Internet Menggunakan Algoritma Apriori

2019 ◽  
Vol 10 (1) ◽  
pp. 173-188
Author(s):  
Uci Baetulloh ◽  
Acep Irham Gufroni ◽  
Rianto
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Minimum Support

Data transaksi penjualan produk kartu perdana kuota internet dapat dijadikan sebagai bahan acuan untuk mengetahui seberapa besar tingkat penjualan produk yang telah dipasarkan oleh beberapa operator telekomunikasi seluler. Data tersebut tidak hanya dijadikan sebagai data arsip penyimpanan laporan penjualan perusahaan saja, tetapi dapat dianalisa dan dimanfaatkan menjadi sebuah informasi untuk membantu dalam melakukan pengembangan strategi pemasaran produk. Tujuan dari penelitian ini yaitu untuk menemukan aturan asosiasi kombinasi antar item produk operator telekomunikasi seluler mana saja yang paling laku terjual di wilayah penjualan Priangan Timur meliputi cluster Ciamis, Garut dan Tasikmalaya. Perhitungan Algoritma Apriori pada aturan asosiasi ini dihitung melalui tiga tahap iterasi pembentukan kandidat k-itemset. Hasil analisa aturan asosiasi yang terbentuk dari perhitungan algoritma apriori dengan menentukan nilai minimum support 35% dan nilai minimum confidence 80%, menghasilkan 9 aturan asosiasi final terbaik pada cluster Ciamis, 21 aturan asosiasi final untuk cluster Tasikmalaya dan 7 aturan asosiasi final untuk cluster Garut. Ketiga wilayah penjualan tersebut produk yang paling sering laku terjual dipasaran outlet adalah produk dari operator kartu kuota internet XL dengan Telkomsel dan produk Indosat dengan Telkomsel. Dengan demikian hasil yang diperoleh dapat digunakan untuk membantu pengambil keputusan dalam meningkatkan penjualan produk yang lebih baik

scholarly journals Minimum Threshold Determination Method based on Dataset Characteristics in Association Rule Mining

2021 ◽  
Author(s):  
Erna Hikmawati ◽  
Nur Ulfa Maulidevi ◽  
Kridanto Surendro
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Threshold Value ◽  
Extraction Process ◽  
Frequent Itemset ◽  
Rule Mining ◽  
Minimum Threshold ◽  
Minimum Support ◽  
Associative Behavior

Abstract The process of extracting data to obtain useful information is known as data mining. Furthermore, one of the promising and widely used techniques for this extraction process is association rule mining. This technique is used to identify interesting relationships between sets of items in a dataset and predict associative behavior for new data. The first step in association rule mining is the determination of the frequent item set that will be involved in the rule formation process. In this step, a threshold is used to eliminate items excluded in the frequent itemset which is also known as the minimum support. Furthermore, the threshold provides an important role in determining the number of rules generated. However, setting the wrong threshold leads to the failure of the association rule mining to obtain rules. Currently, the minimum support value is determined by the user. This leads to a challenge that becomes worse for a user that is ignorant of the dataset characteristics. In this study, a method was proposed to determine the minimum support value based on the characteristics of the dataset. Furthermore, this required certain criteria to be used as thresholds which led to more adaptive rules according to the needs of the user. The results of this study showed that 6 from 8 datasets, obtained a rule with lift ratio > 1 using the minimum threshold value that was determined through this method.

Comparative Analysis of Association Rule Mining Algorithms in Market Basket Analysis Using Transactional Data

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
AA Izang ◽  
SO Kuyoro ◽  
OD Alao ◽  
RU Okoro ◽  
OA Adesegun
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Market Basket Analysis ◽  
Rule Mining ◽  
Business Decisions ◽  
Market Basket ◽  
Minimum Support ◽  
Support Threshold ◽  
Transactional Data

Association rule mining (ARM) is an aspect of data mining that has revolutionized the area of predictive modelling paving way for data mining technique to become the recommended method for business owners to evaluate organizational performance. Market basket analysis (MBA), a useful modeling technique in data mining, is often used to analyze customer buying pattern. Choosing the right ARM algorithm to use in MBA is somewhat difficult, as most algorithms performance is determined by characteristics such as amount of data used, application domain, time variation, and customer’s preferences. Hence this study examines four ARM algorithm used in MBA systems for improved business Decisions. One million, one hundered and twele thousand (1,112,000) transactional data were extracted from Babcock University Superstore. The dataset was induced with Frequent Pattern Growth, Apiori, Association Outliers and Supervised Association Rule ARM algorithms. The outputs were compared using minimum support threshold, confidence level and execution time as metrics. The result showed that The FP Growth has minimum support threshold of 0.011 and confidence level of 0.013, Apriori 0.019 and 0.022, Association outliers 0.026 and 0.294 while Supervised Association Rule has 0.032 and 0.212 respectively. The FP Growth and Apirori ARM algorithms performed better than Association Outliers and Supervised Association Rule when the minimum support and confidence threshold were both set to 0.1. The study concluded by recommending a hybrid ARM algorithm to be used for building MBA Applications. The outcome of this study when adopted by business ventures will lead to improved business decisions thereby helping to achieve customer retention. Keywords: Association rule mining, Business ventures, Data mining, Market basket analysis, Transactional data.

scholarly journals Mining predicate rules without minimum support threshold

2021 ◽  
Vol 48 (4) ◽  
Author(s):  
Hafiz I. Ahmad ◽  
◽  
Alex T. H. Sim ◽  
Roliana Ibrahim ◽  
Mohammad Abrar ◽  
...  
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
State Of The Art ◽  
Predicate Logic ◽  
Frequent Itemsets ◽  
Rule Mining ◽  
Minimum Support ◽  
Support Threshold ◽  
Definition Of

Association rule mining (ARM) is used for discovering frequent itemsets for interesting relationships of associative and correlative behaviors within the data. This gives new insights of great value, both commercial and academic. The traditional ARM techniques discover interesting association rules based on a predefined minimum support threshold. However, there is no known standard of an exact definition of minimum support and providing an inappropriate minimum support value may result in missing important rules. In addition, most of the rules discovered by these traditional ARM techniques refer to already known knowledge. To address these limitations of the minimum support threshold in ARM techniques, this study proposes an algorithm to mine interesting association rules without minimum support using predicate logic and a property of a proposed interestingness measure (g measure). The algorithm scans the database and uses g measure’s property to search for interesting combinations. The selected combinations are mapped to pseudo-implications and inference rules of logic are used on the pseudo-implications to produce and validate the predicate rules. Experimental results of the proposed technique show better performance against state-of-the-art classification techniques, and reliable predicate rules are discovered based on the reliability differences of the presence and absence of the rule’s consequence.

Souvenir Sales Analysis using Apriori Algorithm (Case Study: Ubud-Market Transaction in March 2020)

2021 ◽  
Vol 10 (1) ◽  
pp. 73
Author(s):  
Muhammad Firyanul Rizky ◽  
I Gusti Agung Gede Arya Kadyanan
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Marketing Strategies ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Minimum Support ◽  
Track Record ◽  
Market Transaction ◽  
Transaction Database

Ubud market is one of the largest art markets in Bali, there are many local Balinese souvenir traders and craftspeople, most of them are livelihoods depend on buying and selling local souvenirs, Since the Covid-19 pandemic entered in April 2020, Ubud market traders have started to close their business and hoping economic recoveryin future. The author tries to do a track record of souvenir sales transactions in Ubud market to find the last sales pattern before the traders closes their business to give a solution for marketing strategies in future. The sales transaction data will just become meaningless trash if it’s useless.. To get use information about the products that are most sold out at Ubud Market from the transaction database, the author uses the Apriori algorithm. This study was determined final rules on 2 itemset combination, If buying Manik-Manik Craft, Also buy Barong Shirt with the highest confidence 70% and Minimum Support 28%, and for 3 itemset a combination, If buying Celuk Silver, and Barong Shirt, Also buy Manik-Manik Craft with the highest confidence 37.5% and Minimum Support 12%, based on that there are 3 best-selling souvenir products, namely Barong Shirt, Manik-Manik Craft and Silver-Celuk in March 2020. Keywords: Apriori Algorithm, Data Mining, Sales Analysis, Association Rule Mining, Ubud Market.

Search Of Favorite Books As A Visitor Recommendation of The Fmipa Library Using CT-Pro Algorithm

2018 ◽  
Vol 1 (01) ◽  
pp. 09-13
Author(s):  
Sufiatul Maryana ◽  
Lita Karlitasari
Keyword(s):  
Knowledge Discovery ◽  
Association Rule ◽  
Association Rule Mining ◽  
Recommendation System ◽  
Main Process ◽  
Frequent Patterns ◽  
Rule Mining ◽  
Minimum Support ◽  
Minimum Value ◽  
Confidence Value

The library of Faculty of Mathematics and Natural Science (FMIPA) has a collection of books and other print media, total of 2,678 books with 7237 visitors and 2148 borrowers. The available book search system was very helpful for visitors to find the required books. Especially if the system has features recommended of books. In the provision of book recommendations used one of the data mining techniques, namely association rule mining techniques or excavation of association rules. In the development of this recommendation system, KDD (Knowledge Discovery from Database) model was used. The data used was the transaction history of borrowing book with the category of "chemistry", for the last 5 (five) months, that is September 2014 - February 2015. The excavation technique of this association rule has 2 (two) main process, they are: frequent patterns and rules. To find frequent patterns, a CT-PRO algorithm was used. The minimum value of support used was 1 and 2. Once the pattern is found, the confidence value of each pattern was calculated. The minimum value of confidence used ranges from 10% to 100%. The recommendation rule was based on calculating the value of this confidence. The comparison of minimum support values indicates that the greater value of minimum support then the less borrowing pattern was generated, and vice versa. The comparison of minimum confidence value shows that the greater of minimum confidence value then the less recommended rule given.. Keywords: Library, Recommended System, Knowledge Discovery from Database (KDD), Association Rule Mining, CT-PRO Algorithm

scholarly journals Penerapan Association Rule Mining Berbasis Algoritma Frequent Pattern Growth untuk Rekomendasi Penjualan

2020 ◽  
Vol 7 (2) ◽  
pp. 135-148
Author(s):  
Didi Supriyadi
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Itemset ◽  
Frequent Pattern ◽  
Rule Mining ◽  
Minimum Support ◽  
Pattern Growth ◽  
Mining Association Rule

Tingkat persaingan dan kompleksitas permasalahan penjualan pada perusahaan retail, menuntut setiap perusahaan retail untuk mampu berkompetisi dengan perusahaan lain. Salah satu yang dapat dilakukan adalah melalui pengambilan keputusan terkait penjualan yang lebih tepat dan efektif. Besarnya data transaksinonal penjualan perusahaan retail dapat dilakukan ekstraksi informasi yang bermanfaat. Metode yang dapat digunakan untuk menggali informasi adalah melalui penerapan association rule mining. Association Rule Mining merupakan suatu metode data mining yang berfokus pada pola transaksi dengan cara mengekstraksi asosiasi atau hubungan suatu kejadian. Keranjang belanja yang terdapat pada perusahaan retail yang terkomputerisasi merupakan cara terbaik untuk memberikan dukungan rekomendasi keputusan secara ilmiah dengan cara menentukan hubungan antara barang yang dibeli secara bersamaan dalam setiap transaksi. Algoritma FP-growth digunakan untuk menentukan himpunan dataset yang paling sering muncul (frequent itemset) pada sekeompok data. Penelitian ini menghasilkan nilai minimum support 0,1% dan nilai minimum confidence 60% jumlah rule yang dihasilkan berjumlah 116457, nilai minimum confidence 70% jumlah rule yang dihasilkan berjumlah 84086, dan nilai minimum confidence 80% jumlah rule yang dihasilkan berjumlah 48623 dari data yang diolah sebanyak 22191. Hasil rule ini dapat digunakan untuk strategi pemasaran produk. Nilai minimum support 0,1% dimana semakin besar nilai minimum confidence maka menghasilkan rule yang semakin sedikit.

scholarly journals Minimum threshold determination method based on dataset characteristics in association rule mining

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Erna Hikmawati ◽  
Nur Ulfa Maulidevi ◽  
Kridanto Surendro
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Threshold Value ◽  
Frequent Itemset ◽  
Rule Mining ◽  
Minimum Threshold ◽  
Minimum Support ◽  
Associative Behavior ◽  
Average Utility ◽  
Adaptive Support

AbstractAssociation rule mining is a technique that is widely used in data mining. This technique is used to identify interesting relationships between sets of items in a dataset and predict associative behavior for new data. Before the rule is formed, it must be determined in advance which items will be involved or called the frequent itemset. In this step, a threshold is used to eliminate items excluded in the frequent itemset which is also known as the minimum support. Furthermore, the threshold provides an important role in determining the number of rules generated. However, setting the wrong threshold leads to the failure of the association rule mining to obtain rules. Currently, user determines the minimum support value randomly. This leads to a challenge that becomes worse for a user that is ignorant of the dataset characteristics. It causes a lot of memory and time consumption. This is because the rule formation process is repeated until it finds the desired number of rules. The value of minimum support in the adaptive support model is determined based on the average and total number of items in each transaction, as well as their support values. Furthermore, the proposed method also uses certain criteria as thresholds, therefore, the resulting rules are in accordance with user needs. The minimum support value in the proposed method is obtained from the average utility value divided by the total existing transactions. Experiments were carried out on 8 specific datasets to determine the association rules using different dataset characteristics. The trial of the proposed adaptive support method uses 2 basic algorithms in the association rule, namely Apriori and Fpgrowth. The test is carried out repeatedly to determine the highest and lowest minimum support values. The result showed that 6 out of 8 datasets produced minimum and maximum support values for the apriori and fpgrowth algorithms. This means that the value of the proposed adaptive support has the ability to generate a rule when viewed from the quality as adaptive support produces at a lift ratio value of > 1. The dataset characteristics obtained from the experimental results can be used as a factor to determine the minimum threshold value.

scholarly journals Analisis Tingkat Pembelian Konsumen dengan Algoritma Apriori

2020 ◽  
Vol 2 (02) ◽  
Author(s):  
Ismasari Ismasari ◽  
Maulida Ramadhan ◽  
Wahyu Hadikristanto
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Market Basket Analysis ◽  
Rule Mining ◽  
Market Basket ◽  
Minimum Support ◽  
Mining Association Rule

Saat ini data mining telah diimplementasikan ke berbagai bidang salah satu diantaranya adalah pada bidang bisnis atau perdagangan yang dapat membantu para pebisnis dalam kebijakan pengambilan keputusan terhadap apa yang berhubungan dengan persediaan barang. Misalnya pentingnya sistem persediaan barang di suatu Toko dan jenis barang apa yang menjadi prioritas utama yang harus di stok untuk mengantisipasi kekosongan barang. Karena minimnya stok barang dapat berpengaruh pada pelayanan konsumen dan pendapatan Toko. Metode yang sering digunakan untuk menganalisa pola pembelian pelanggan adalah metode asosiasi atau association rule mining. Association rule mining adalah suatu metode untuk mencari pola hubungan antar satu atau lebih itemset yang ada dalam suatu dataset. Algoritma yang paling popular dalam mencari pola hubungan item set adalah algoritma apriori atau sering disebut dengan market basket analysis. Proses yang dilakukan dalam penelitian ini menggunakan tools Rapid Miner untuk mengolah data dengan algoritma apriori, dari pengujian yang dilakukan dengan parameter yang telah ditentukan yaitu minimum support 70% dan minimum confidence 80% menghasilkan 4 aturan asosiasi dengan nilai confidance 100% yaitu kombinasi item aqua 600ml-fulloblasto caramel cruncy chocolat - yupi 500 semua rasa - beng beng 25g. Dengan pencarian pola menggunakan algoritma apriori ini diharapkan informasi yang dihasilkan dapat meningkatakan strategi penjualan selanjutnya    

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