Analysis Of Product Recommendations Using Eclat Algorithm Based On PT XYZ Sales History

Abstract
PT XYZ is a company that provides livestock production facilities. Sales transactions are recorded as company files, sales reports, and income statements. More than 1,500 invoices are printed every month. However, in terms of product promotion, the company have not used the analysis results from the history of sales transactions. This study aims to provide product recommendations using the ECLAT algorithm. The ECLAT (Equivalence Class Transformation) algorithm uses the concept of depth-first search to find itemsets that often appear in transactions. The research steps are interviews for data acquisition, data pre-processing, data transformation, and data mining process with the ECLAT algorithm to find frequent itemsets and use the frequent itemset results as the basis for making association rules patterns. The results of the analysis show that the system can provide recommendations for association rules effectively from 14,617 transaction history. The highest minimum support that can be used to find a combination of k-itemset is 1%. The results of the annual association rules from the transaction history in 2018-2020 show the difference in results with the highest variance occurring in 2020, namely 5 association rules. Each association rule that appears has a strong confidence value that is above 50%
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References
PT XYZ, “Wawancara PT XYZ,” Blitar, 2021.
T. Subianto, “Studi Tentang Perilaku Konsumen Beserta Implikasinya Terhadap Keputusan Pembelian,” J. Ekon. Mod., vol. 3, no. 2, hal. 165–182, 2007.
N. F. H. Lestari, “Pola Perilaku Membeli Produk Kebutuhan Rumah Tangga (Pangan dan Non Pangan) di Pasar Modern Pada Konsumen Wanita Dewasa Tengah di Kota Karawang,” Universitas Brawijaya, 2014.
D. Suyanto, Data Mining untuk Klasifikasi dan Klasterisasi Data. Bandung: Informatika Bandung, 2017.
Sulastri, E. Zuliarso, dan Y. Anis, “Implementasi Algoritma Apriori dan Algoritma Eclat pada Ahass Akmal Jaya Purwodadi,” J. Din., vol. 22, hal. 49–56, 2017.
K. N. Wijaya, “Analisis Pola Frekuensi Keranjang Belanja dengan Perbandingan Algoritma Fp-Growth (Frequent Pattern Growth) dan Eclat pada Minimarket,” J. Tek. Inform. dan Sist. Inf., vol. 7, no. Agustus 2020, hal. 364–373, 2020.
Sudaryono, Manajemen Pemasaran: Teori dan Implementasi. Yogyakarta: CV. Andi Offset, 2016.
S. D. Pratiwi dan L. Suriani, “Strategi Pemasaran Produk Rangka Atap Baja Ringan Pada PT. Hari Rezeki Kita Semua Pekanbaru,” J. Valuta, vol. 3, no. 2, hal. 241–275, 2017, [Daring]. Tersedia pada: https://journal.uir.ac.id/index.php/valuta/article/view/2078.
M. Rasyaf, Beternak Ayam Kampung, Cet. 1. Jakarta: Penebar Swadaya, 2011.
G. Hidayat, “Manajemen Sarana Produksi Ternak pada Program Keahlian Budidaya Ternak SMK Negeri 1 Pandak Bantul Yogyakarta,” Univesitas Negeri Yogyakarta, 2011.
M. J. Zaki, S. Parthasarathy, dan W. Li, “A Localized Algorithm for Parallel Association Mining A Localized Algorithm for Parallel Association Mining,” no. June 2015, 1997, doi: 10.1145/258492.258524.
U. Fayyad, G. Piatetsky-Shapiro, dan P. Smyth, “From Data Mining to Knowledge Discovery in Databases,” AI Magazine, hal. 17(3), 37, 1996.
J. Han, H. Cheng, D. Xin, X. Yan, dan S. Barbara, “Frequent pattern mining : Current status and future directions Frequent pattern mining : current status and future,” Data Min. Knowl. Discov., vol. 1, no. 5, hal. 55–86, 2007, doi: 10.1007/s10618-006-0059-1.
A. Pratiwi, “Penerapan Algoritma Declat Dalam Identifikasi Adverse Event Pada Obat Antihipertensi Berdasarkan Kelompok Umur dan Jenis Kelamin,” Universitas Islam Negeri Sultan Syarif Kasim Riau, 2017.
F. Kayaalp, “Performance Comparison of Association Rule Algorithms with SPMF on Automotive Industry Data,” Düzce Univ. J. Sci. Technol., no. July, hal. 1985–2000, 2019, [Daring]. Tersedia pada: https://www.researchgate.net/publication/334825343_Performance_Comparison_of_Association_Rule_Algorithms_with_SPMF_on_Automotive_Industry_Data.
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