Assosiation Rules for Product Sales Data Analysis Using The Apriori Algorithm

Authors

  • Jarseno Pamungkas STMIK Nusa Mandiri, Indonesia
  • Yopi Handrianto Universitas Bina Sarana Informatika

DOI:

10.33395/sinkron.v5i1.10599

Keywords:

Apriori Algorithm, Product Sales, Analysis, Tanagra

Abstract

To increase sales transactions, the company must be able to compete with other competitors so that it requires an appropriate strategy in carrying out the sales process carried out. In addition to the marketing strategy, the company must be able to analyze the products sold based on the number of sales that have occurred so that the company can see which products are more dominant in consumer demand so that the company can determine a more effective sales strategy. PT. Surya Indah City is a company engaged in the sale of various clothing and accessories. In an effort to increase sales of its products, an analysis is needed to be able to increase company revenue by utilizing sales transaction data it has. To analyze the relationship between clothing products and accessories which are more predominantly sold and other available clothing and accessories products, a data mining algorithm is used, namely the a priori algorithm. With the help of the tanagra application to carry out the calculation process, the dominant product that consumers are interested in can be determined. By using two variables that meet support and minimum confidence, it can be concluded that the most sold products are from the type of clothing, namely clothes and pants. It was concluded that the results of the known final association rules, if you buy a shirt, you will buy pants with 50% support and 75% confidence. If you buy pants, you will buy clothes with 50% support and 85% confidence.

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References

Anas, A. (2016). Analisa Algoritma Apriori Untuk Mendapatkan Pola Peminjaman Buku Perpustakaan Smpn 3 Batanghari. Jurnal Ilmiah Media SISFO, 10(2), 628–641. http://ejournal.stikom-db.ac.id/index.php/mediasisfo/article/view/233/220

Badrul, M. (2015). Prediksi Hasil Pemilu Legislatif Dengan Menggunakan Algoritma K-Nearest Neighbor. Jurnal Pilar Nusa Mandiri, XI(2), 152–160. http://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/424/374

Badrul, M. (2016). Algoritma Asosiasi Dengan Algoritma Apriori Untuk Analisa Data Penjualan. None, 12(2), 121–129. https://media.neliti.com/media/publications/227549-algoritma-asosiasi-dengan-algoritma-apri-f4245cc8.pdf

Fauziah, S., & Ratnawati. (2018). Penerapan Metode FIFO Pada Sistem Informasi Persediaan Barang. Jurnal Teknik Komputer, 4(1), 98–108.

Gunadi, G., & Sensuse, D. I. (2012). Penerapan Metode Data Mining Market Basket Analysis Terhadap Data Penjualan Produk Buku Dengan Menggunakan Algoritma Apriori Dan Frequent Pattern Growth ( Fp-Growth ) : Telematika, 4(1), 118–132.

Handrianto, Y., & Farhan, M. (2019). C.45 Algorithm for Classification of Causes of Landslides. SinkrOn, 4(1), 120. https://doi.org/10.33395/sinkron.v4i1.10154

Irfiani, E. (2019). Application of Apriori Algorithms to Determine Associations in Outdoor Sports Equipment Stores. SinkrOn, 3(2), 218. https://doi.org/10.33395/sinkron.v3i2.10089

Lestari, B. S. (2014). Fashion sebagai Komunikasi Identitas Sosial di Kalangan Mahasiswa. Ragam Jurnal Pengembangan Humaniora, 14(3), 225–238. https://jurnal.polines.ac.id/index.php/ragam/article/view/514/

Listriani, D., Setyaningrum, A. H., & Eka, F. (2016). Penerapan Metode Asosiasi Menggunakan Algoritma Apriori Pada Aplikasi Analisa Pola Belanja Konsumen (Studi Kasus Toko Buku Gramedia Bintaro). Jurnal Teknik Informatika, 9(2), 120–127. https://doi.org/10.15408/jti.v9i2.5602

Panjaitan, L. F., Handrianto, Y., & Nurhadi, A. (2020). Apriori Algorithm On Car Rental Analysis With The Most Popular Brands. SinkrOn, 4(2), 47. https://doi.org/10.33395/sinkron.v4i2.10506

Putra, J. L., Raharjo, M., Sandi, T. A. A., & Prasetyo, R. (2019). Implementasi Algoritma Apriori Terhadap Data Penjualan. Jurnal Pilar Nusa Mandiri, 15(1), 85–90. http://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/113/95

Rusdiansyah, Suharyanti, N., Triningsih, & Murniyati. (2020). Application Of Pizza Sales Data Mining Using Apriori Method. SinkrOn, 4(2), 1. https://doi.org/10.33395/sinkron.v4i2.10500

Santoso, H., Hariyadi, I. P., & Prayitno. (2016). Data Mining Analisa Pola Pembelian Produk Dengan Menggunakan Metode Algoritma Apriori. Teknik Informatika, 1, 19–24. http://ejournal.stikom-db.ac.id/index.php/mediasisfo/article/view/233/220

Sugiyono. (2015). Metode penelitian pendekatan kuantitatif, kualitatif dan R&DNo Title. In Metode penelitian pendekatan kuantitatif, kualitatif dan R&D (pp. 80–84). Alfabeta.

Wulandari, R. T. (2017). Data Mining Teori dan Aplikasi Rapidminer (1st ed.). Gava Media.

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Publication History:

Submitted Aug 30, 2020
Published Oct 7, 2020
Last Modified Oct 7, 2020

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How to Cite

Pamungkas, J., & Handrianto, Y. (2020). Assosiation Rules for Product Sales Data Analysis Using The Apriori Algorithm. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 5(1), 84-91. https://doi.org/10.33395/sinkron.v5i1.10599