Data Mining Analysis with Association Rules Method to Determine the Result of Fish Catch using FP-Growth Algorithm

Sukma, Dito; Fitrianah, Devi

doi:10.5120/ijca2018917748

Cited by 3 publications

(6 citation statements)

References 2 publications

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“…Minimum support 50% produces 12 frequent itemsets with itemset of 3. In Table 3, it can be concluded that the greater the minimum support, the fewer the number of items produced [2][7] and the stronger the association relationship between attributes in the rules formed [20]. Table 4, with a minimum support of 10%, it can see the itemsets formed and the items, for ex sample the itemset of four consists of classes or brands PYO, BON, Q BY and YSL.…”

Section: Discussionmentioning

confidence: 99%

“…Association rule mining is useful for discovering interesting relationships hidden in large data sets [8]. Association rules is data mining processes to find and determine all relationships between items in a database that meet minimum supports and minimum confidence [2]. Association rule is one method that aims to find patterns that often occur among many transactions, where each transaction consists of several items.…”

Section: Association Rulementioning

confidence: 99%

“…Using the FP-Tree structure, the FP-Growth algorithm can directly extract frequent itemset from the FP-Tree arrangement that has been formed [15]. The FP-Growth algorithm is divided into three main steps: generation of Conditional Pattern Base, FP-tree conditional generation and frequent itemset search [2].…”

Section: Fp-growthmentioning

confidence: 99%

“…Data mining technique known as Knowledge Discovery in Database (KDD) is a method used in the analysis of large database observations to find interesting patterns of relationships of a transaction [2]. The pattern is then identified to determine the relationship between items and to determine the items most often purchased simultaneously using the Association rule method [2]. Previous research, most of the studies adopted the Apriori algorithm in analyzing association rule.…”

Section: Introductionmentioning

confidence: 99%

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A Data Mining Approach For Product Promotion and Inventory Solution using FP-Growth Algorithm

Hartono¹,

Fitrianah²

2020

IJCA

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This study aims to find interesting patterns on the database transaction so that it can be used as a recommendation sales promotion and inventory product. Companies have difficulty finding interesting transaction patterns in large databases, so it will be difficult to determine the right product promotions and inventory. To resolve these problems are to use data mining techniques with association rule. In previous studies, most studies adopt Apriori algorithm to analyze the association rules. In this study, the data mining technique used is the association rules algorithm FP-Growth. In the FP-Growth algorithm did generate candidates as in Apriori algorithm and using a development concept Tree in frequent itemset search so that it requires faster than Apriori. Some of the analyzes produced in this study are higher minimum support values and minimum trust used will result in fewer items and association rules. Association rule in this study has a lift ratio value of more than 1.00, meaning that item K and L are actually bought together. The higher the lift ratio produced shows the stronger the association rules are formed. The results of this study are the minimum confidence of 97.63%, the maximum trust is 99.37% and the lift ratio is 1,00013798. These results can be used as recommendations for optimizing product promotions and inventory.

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Section: Discussionmentioning

confidence: 99%

Section: Association Rulementioning

confidence: 99%

Section: Fp-growthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Data Mining Approach For Product Promotion and Inventory Solution using FP-Growth Algorithm

Hartono¹,

Fitrianah²

2020

IJCA

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“…The results obtained from the analysis are the greater the minimum support and minimum confidence used, the fewer items and rules are formed. at the minimum support and minimum confidence level with a minimum supply of 50% and minimum confidence of 80%, we can be sure that when YellowFin tuna is obtained it will get Bigeye tuna too [19].…”

Section: Previous Studies On Frequent Pattern Growth (Fp-growth)mentioning

confidence: 99%

Analysis of Consumer Purchase Patterns on Handphone Accessories Sales Using FP-Growth Algorithm

Fitrianah¹,

Zain²

2021

Advances in Social Science, Education and Humanities Research

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This study aims to discover association patterns from customers' purchase, these patterns later on known as reference for the store to supply goods. Mapongs store is one of the online shops that is developing quite rapidly, however, Mapongs Store is still difficult to determine the supply of stock of goods to be sold, so this has an impact on decreasing sales figures. To solve this problem, the method used in this study is the Association rule using the FP Growth algorithm. Analysis of sales data obtained with the Association rules Method using the FP Growth algorithm, namely the greater the minimum support and minimum confidence used, the fewer itemset and rules will be formed. In this study, if the minimum support is 50% and the minimum confidence is 80%, it will produce 211 rules. So, the rules in this study can be used as information for sellers to find out what products buyers often buy at this Mapongs Store. All Rules produced in this study have a Lift ratio value of more than 1.00 so they can be used as a reference in determining the relationship between product sales to optimize the results of sales analysis for sellers

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Analisa Pola Peminjaman Buku Di Perpustakaan Untuk Mengatur Tata Letak Buku Menggunakan Algoritma Fp-Growth

Rasyidah

2021

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Perpustakaan merupakan suatu tempat yang menyediakan berbagai referensi dan informasi. Berbagai buku sampai majalah telah di sediakan. Dalam hal ini, tata letak buku di suatu perpustakaan merupakan hal yang penting untuk diperhatikan. Penelitian ini bertujuan untuk menemukan buku-buku apa saja yang mungkin akan dipinjam secara bersamaan oleh pengunjung. Dengan menggunakan algoritma FP-Growth pada metode aturan asosiasi data mining, pihak manajemen perpustakaan dapat mengambil keputusan untuk mengatur tata letak buku dengan melihat pola yang akan terbentuk. Data yang digunakan untuk penelitian ini adalah data transaksi perpustakaan untuk bulan Januari – Mei 2019 dengan jumlah 5026 data. Untuk penelitian kali ini, penulis menggunakan seluruh data untuk dilakukan testing. Tools yang digunakan untuk melakukan pengolahan data ini adalah RapidMiner dengan atribut yang mencakup tanggal transaksi, ID Number, Genre Buku. Berdasarkan analisis data ini, semua aturan yang dihasilkan memiliki nilai rasio diatas 1.00 sehingga data sudah akurat dan dapat digunakan untuk referensi pengaturan tata letak buku di Perpustakaan.

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Data Mining Analysis with Association Rules Method to Determine the Result of Fish Catch using FP-Growth Algorithm

Cited by 3 publications

References 2 publications

A Data Mining Approach For Product Promotion and Inventory Solution using FP-Growth Algorithm

A Data Mining Approach For Product Promotion and Inventory Solution using FP-Growth Algorithm

Analysis of Consumer Purchase Patterns on Handphone Accessories Sales Using FP-Growth Algorithm

Analisa Pola Peminjaman Buku Di Perpustakaan Untuk Mengatur Tata Letak Buku Menggunakan Algoritma Fp-Growth

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