Analysis of Information Technology (IT) Goods Sales Patterns Using the FP-Growth Algorithm

Wandri, Rizky; Hanafiah, Anggi

doi:10.25299/itjrd.2022.8155

Cited by 4 publications

(3 citation statements)

References 7 publications

(8 reference statements)

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“…Kegiatan jual dan beli ialah sebuah kegiatan pada bidang pemasaran yang terpenting dalam mencapai sebuah tujuan dari badan usaha. Penjualan merupakan ilmu dan seni dalam mempengaruhi suatu pribadi yang dikerjakan oleh penjual untuk bersedia membeli produk yang ditawarkan [1] [2]. Data transaksi penjualan yaitu hal yang dapat digunakan untuk sebuah pengambilan keputusan bisnis.…”

Section: Pendahuluanunclassified

Penerapan Metode FP-Growth Dalam Optimalisasi Bisnis Retail

Rhayatun Aviqah,

Muhammad,

Mandala

2024

CoSciTech

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Data transaksi penjualan merupakan sebuah hal yang dapat dimanfaatkan kembali untuk pengambilan keputusan bisnis. Namun pada kasus ini data transaksi tersebut tidak dimanfaatkan kembali untuk keperluan bisnis, dan hanya dijadikan sebagai arsip laporan penjualan. Algoritma FP-Growth yakni tingkatan dari algoritma asosiasi apriori yang menggunakan alternatif frekuensi itemset berdasarkan pada angka yang paling sering muncul pada tiap transaksi(frequent itemset) dalam sebuah kelompok data. Karakteristik dari algoritma FP-Growth yaitu struktur pada data yang dipakai berupa tree dengan nama FP-Tree. Dengan penggunaan FP-Tree, algoritma FP-Growth dapat mengekstrak frequent Itemset dari FP-Tree. Metode FP-Growth terbagi menjadi 3 tahapan-tahapan utama yaitu tahap pembangkitan conditional pattern base, tahap pembangkitan conditional FP-Tree, dan tahap pencarian frequent itemset. Dengan penerapan metode FP-Growth pada penelitian ini bisa digunakan untuk melihat pola penjualan produk. Hasil yang didapatkan berupa 5 interesting rules dengan memasukkan nilai min support 10% dan min confidance 50 % yaitu jika membeli popok maka membeli baju, jika membeli dot baby maka membeli baju, jika membeli topi maka membeli baju, jika membeli celana akan membeli baju, dan jika membeli singlet akan membeli baju. Diharapkan penelitian ini dapat membantu pemilik Retail Aura Moms Baby & Kids dalam memanfaatkan hasil transaksi penjualan sehingga hasil dapat dimanfaatkan dengan tepat. Hal ini dibuktikan dengan dibuatnya sistem E-Business yang dapat mengelola transaksi penjualan untuk menentukan pola pembelian produk pada Retail Aura Moms Baby & Kids.

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Section: Pendahuluanunclassified

Penerapan Metode FP-Growth Dalam Optimalisasi Bisnis Retail

Rhayatun Aviqah,

Muhammad,

Mandala

2024

CoSciTech

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“…The FP-Growth algorithm attracts attention because it can analyze frequent patterns with just two database scans without generating a pool of candidates [5]. The FP-Growth algorithm is an alternative algorithm that is quite effective for finding the most frequently occurring data set (frequent itemset) in large data sets [17]. The FP-Growth algorithm is a development of the Apriori algorithm [18].…”

Section: Fp Growthmentioning

confidence: 99%

Implementation of the FP-Growth Algorithm in Sales Transactions for Menu Package Recommendations at Warung Oemah Tani

Triana

Khoerida

Widiawati

et al. 2022

IOTA

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Along with the rapid development of the culinary industry, business competition is also getting tougher. Warung Oemah Tani serves a variety of menus and drinks, but to provide satisfying service to customers, business people must try to develop new products. Under these circumstances, the menu recommendations for Warung Oemah Tani need to be analyzed so that the recommendations made are right on target. This study aims to analyze the sales of Warung Oemah Tani using the FP Growth algorithm. This algorithm identifies the data set with the highest frequency of concurrent sales (frequent itemset). The results of the association rules show that the highest support value is 0.520 and the highest confidence value is 0.929, with a minimum support of 30% and a minimum confidence of 80%. Obtained 14 rule associations that meet the minimum support and minimum confidence.

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“…The third was carried out by Rizky Wandri Anggi Hanafiah with the title "Analysis of Information Technology (IT) Goods Sales Patterns Using the FP-Growth Algorithm" [18]. In this study, the aim is to analyze marketing transaction patterns using the FP-growth algorithm with RapidMiner tools to look for correlation relationships to take policies, but the dataset used is only 70 transactions, and the lack of datasets used is very small.…”

mentioning

confidence: 99%

Determination of the best rule-based analysis results from the comparison of the Fp-Growth, Apriori, and TPQ-Apriori Algorithms for recommendation systems

Syahrir

Mardedi

2023

MATRIX

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The popular association rule algorithms are Apriori and fp-growth; both of these algorithms are very familiar among data mining researchers; however, there are some weaknesses found in the association rule algorithm, including long dataset scans in the process of finding the frequency of the item set, using large memory, and the resulting rules being sometimes less than optimal. In this study, the authors made a comparison of the fp-growth, Apriori, and TPQ-Apriori algorithms to analyze the rule results of the three algorithms. TPQ- Apriori is an algorithm developed from the Apriori algorithm. For experiments, the Apriori and fp-growth algorithms use RapidMiner and Weka tools, while the TPQ-apriori algorithm uses self-built application programs. The dataset used is the sales data for the Kopegtel NTB department store, which has been uploaded on the Kaggle site. As for the results of testing the base rules from the overall results of testing the rules with the good Kopegtel dataset for 100%, 50%, and 25% of the total volume of the dataset, a conclusion can be drawn that the larger the dataset to be processed, the results will be more optimal when using the fp-growth algorithm RapidMiner, but not optimal if the dataset to be processed is small. It is different from using the Apriori and Weka FP-growth algorithms, where the resulting rules are less than optimal if the dataset used is large and optimal if the dataset is small. Several rules do not appear in the fp-growth and Apriori Weka algorithms because the two algorithms do not have a tolerance value in Weka's tools for the support of the rules that will be displayed. Meanwhile, the TPQ- Apriori algorithm that has been developed is capable of producing optimal rules for both large datasets and small datasets.

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Analysis of Information Technology (IT) Goods Sales Patterns Using the FP-Growth Algorithm

Cited by 4 publications

References 7 publications

Penerapan Metode FP-Growth Dalam Optimalisasi Bisnis Retail

Penerapan Metode FP-Growth Dalam Optimalisasi Bisnis Retail

Implementation of the FP-Growth Algorithm in Sales Transactions for Menu Package Recommendations at Warung Oemah Tani

Determination of the best rule-based analysis results from the comparison of the Fp-Growth, Apriori, and TPQ-Apriori Algorithms for recommendation systems

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