Analisis Dan Implementasi Data Mining Menggunakan Algoritma Apriori Untuk Meningkatkan Penjualan Pada Kantin Universitas Tanri Abeng

Mulya, Muhamad Femy; Rismawati, Nofita; Alifi, Raudha Rizky

doi:10.30998/faktorexacta.v12i3.4541

Cited by 6 publications

(8 citation statements)

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“…Data mining is a method of finding interesting patterns and knowledge from large amounts of data [10]. Data mining is widely used to describe and predict data [10].…”

Section: Data Miningmentioning

confidence: 99%

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Association Rules Mining for Designing Product Bundling Strategy (Study Case at a Cafe in Jakarta)

Jannah,

Iskandar,

Zulvia

2023

jli

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Cafe X is a modern and prominent coffee shop in town that sells specialty beverage products with a total of 73 variations. Sales transactions that occurred in March 2021 at that Cafe reached an average of 300 times per day. All this time, the transaction data is only used to calculate inventory replenishment and to observe sales profits whereas it can actually be utilized to reveal information related to customer behavior in buying products by finding the association or pattern between product sales. The purpose of this study is to obtain association models between items that can be used as a base to arrange recommendations for selling beverage products at Cafe X using a data mining method, namely the association rules with the Apriori algorithm. The transaction data specified for data processing are payment transactions of a total of 3,100 data records. From the analysis results, it is known that there are 8 product association rules that meet the minimum support of 0.01 and the minimum confidence of 0.5, which means that these rules can be used as an alternative basis for determining business strategy at Cafe X. Based on the association information between products, the customer relationship management (CRM) strategy that can be applied at Cafe X is the promotion strategy of product bundling. From a number of recommended bundled products, it is found that there are opportunities to increase sales for four products, they are MD001, TH002, LB001, and TU001.

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“…Data mining is a method of finding interesting patterns and knowledge from large amounts of data [10]. Data mining is widely used to describe and predict data [10].…”

Section: Data Miningmentioning

confidence: 99%

“…Data mining is a method of finding interesting patterns and knowledge from large amounts of data [10]. Data mining is widely used to describe and predict data [10]. An example of the application of data mining in the field of sales is the market analysis [11].…”

Section: Data Miningmentioning

confidence: 99%

Association Rules Mining for Designing Product Bundling Strategy (Study Case at a Cafe in Jakarta)

Jannah,

Iskandar,

Zulvia

2023

jli

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“…Salah satu cara yang dapat dilakukan yaitu dengan teknik data mining atau yang merupakan suatu proses untuk menemukan informasi dari beberapa kumpulan data [4] yang bertujuan untuk membuat keputusan bisnis yang lebih efisisen dan mengambil keputusan secara tepat [5]. Teknik data mining dengan metode clustering merupakan metode pengelompokan record, pengamatan, dan membentuk kelas objekobjek yang memiliki kemiripan, dimana metode clustering digunakan oleh penulis dalam penelitian ini [6]. Penelitian ini menggunakan data transaksi berupa excel yang diberikan langsung oleh pihak perusahaan, dimana pada data transaksi tersebut terdapat field data yang dapat digunakan untuk melakukan pemetaan pelanggan sehingga diperlukan metode algoritma yang tepat dalam pengelompokan data.…”

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“…Hasil akir yang diperoleh yaitu dengan nilai CLV tertinggi berada di cluster 2, dimana nilai LRFM yang dihasilkan yaitu length yang tinggi, frequency yang tinggi, recency yang rendah dan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6. No.1 (2022) …”

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Pemetaan Pelanggan dengan LRFM dan Two Stage Clustering untuk Memenuhi Strategi Pengelolaan

Viandari

Suarjaya²,

Piarsa³

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Maibus is a company of transportation services located in Bali. Transaction data that is owned has not been managed properly. This results in data accumulation and only as a turnover calculation, so LRFM and clustering methods are needed to assist the calculation and processing data in fulfilling customer management strategies. The research was conducted by collecting and understanding data, preprocessing, applying LRFM (Length,Recency,Frequency,Monetary), normalizing LRFM, evaluating the number of clusters with Davies Bouldin Index (DBI), clustering with K-Means, and analyzing cluster results. The data used is transaction data from January 2017 to December 2018 with a total of 14.292 data. The clustering method with the K-means algorithm helps in mapping customers based on transaction data. DBI was used to determine the optimal number of clusters and LRFM used to test the determination of variables in determining customer behavior and loyalty. The results of testing 7.193 invoice using 5 clusters with DBI value is 0.135. The result of customers in cluster 0,2,4 are new customer groups with the proposed strategy is enforced strategy, while the customer in cluster 1 and 3 are lost customers with the proposed strategy is let-go strategy that refers to the customer value and customer loyalty matrix.

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“…Kumpulan data transaksi dalam jumlah besar sebetulnya dapat dimanfaatkan untuk keperluan prediksi dalam menentukan strategi penjualan selanjutnya dengan metode Data Mining [5]. Metode ini dapat digunakan untuk menghasilkan pola-pola yang valid, berpotensi memiliki manfaat serta dapat dipahami dengan mudah dari kumpulan data dalam jumlah besar [6].…”

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Analisa Cluster Data Transaksi Penjualan Minimarket Selama Pandemi Covid-19 dengan Algoritma K-means

2021

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Covid-19 has had a negative impact on the economic sector in Indonesia. This can be seen from the losses experienced by industry players in the form of a decrease in income turnover. Sales strategy needs to be done so that losses can be minimized. Sales transaction analysis can be done to find product groups with the most sales data so that stock management can be fulfilled and increase sales transactions. Berkah Abadi Minimarket is an industry that has been affected by this pandemic. Data analysis has not been carried out to find out which product has the most sales data, so it is necessary to analyze it with the k-means algorithm. This algorithm can group data based on similar characteristics. The application of the algorithm on 278480 transaction data, obtained three sales data clusters, namely cluster 2 or the highest sales of 57 products, cluster 1 or moderate sales of 57 products and the rest are cluster 0 with low sales. The result of the accuracy of the clustering model generated by the confusion matrix is 87%. Based on these results, the owners of the Berkah Abadi Minimarket can be helped in making decisions on stock management while the Covid-19 pandemic is still ongoing.

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Analisis Dan Implementasi Data Mining Menggunakan Algoritma Apriori Untuk Meningkatkan Penjualan Pada Kantin Universitas Tanri Abeng

Cited by 6 publications

References 0 publications

Association Rules Mining for Designing Product Bundling Strategy (Study Case at a Cafe in Jakarta)

Association Rules Mining for Designing Product Bundling Strategy (Study Case at a Cafe in Jakarta)

Pemetaan Pelanggan dengan LRFM dan Two Stage Clustering untuk Memenuhi Strategi Pengelolaan

Analisa Cluster Data Transaksi Penjualan Minimarket Selama Pandemi Covid-19 dengan Algoritma K-means

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