Sales Analysis Using Apriori Algorithm in Data Mining Application on Food and Beverage (F&B) Transactions
Abstract
The current business landscape has compelled many companies to compete in boosting their company's revenue, particularly in the F&B sector. Existing sales transaction data has not been fully maximized in determining the business strategy of companies. Therefore, the implementation of data mining is necessary to analyze and explore available data to discover new information that is more beneficial for the company. In this study, we analyze sales transaction data using the a priori algorithm method because this algorithm efficiently handles the data mining process on a large scale with a substantial amount of data. The results of this study indicate that the formed association rules can determine patterns of product purchases that are frequently bought together. The established association rules successfully combine sales transaction data into two-item combinations, namely green tea latte and french fries, with a support value of 16% and a confidence level of 83%. These rules can be used as a reference in determining the company's business strategy.
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References
A. Tri and S. Budi, “Analisis Loyalitas Berbasis Kepuasan Pelanggan Toko UKM Om Jeans Klaten,” Upajiwa Dewantara, vol. 2, no. 1, pp. 39–57, 2018.
I. Zulfa, R. Rayuwati, and K. Koko, “Implementasi data mining untuk menentukan strategi penjualan buku bekas dengan pola pembelian konsumen menggunakan metode Apriori ( studi kasus : Kota Medan ),” J. SAINS DAN Teknol., vol. 16, no. 1, pp. 69–82, 2020.
O. Solihin, “Implementasi Big Data Pada Sosial Media Sebagai Strategi,” vol. 5, 2021.
M. Ayub, U. K. Maranatha, and A. N. Networks, “Proses Data Mining dalam Sistem Pembelajaran Berbantuan Komputer Proses Data Mining dalam Sistem Pembelajaran Berbantuan Komputer,” no. January 2012, 2018.
S. Defit, “Penggunaan Algoritma Apriori dalam Menganalisa Prilaku Mahasiswa dalam Memilih Mata Kuliah (Studi Kasus: FKIP UPI ‘YPTK’),” urnal Media Process., vol. 8, pp. 31–42, 2013.
W. B. Zulfikar, A. Wahana, W. Uriawan, and N. Lukman, “Implementation of association rules with apriori algorithm for increasing the quality of promotion,” in 2016 4th International Conference on Cyber and IT Service Management, 2016, pp. 1–5, doi: 10.1109/CITSM.2016.7577586.
S. A. Miranda and D. Kurniawan, “Implementasi Association Rule Dalam Menganalisis Data Penjualan Sheshop dengan Menggunakan Algoritma Apriori,” Metik J. Vol., vol. 6, no. 1, 2022, doi: 10.47002/metik.v6i1.342.
S. P. Tualeka et al., “Implementasi Data Mining Untuk Memprediksi Penjualan Houseware Menggunakan Algoritma Apriori Implementation of Data Mining for Predicting Sales and Stock Placement at CV Pasti Jaya Houseware Using Apriori,” pp. 115–123, 2021, doi: 10.47002/seminastika.v3i1.258.
U. Ependi and A. Putra, “Solusi Prediksi Persediaan Barang dengan Menggunakan Algoritma Apriori ( Studi Kasus : Regional Part Depo Auto 2000 Palembang ),” vol. 5, no. 2, pp. 139–145, 2019.
J. L. Putra et al., “Implementasi Algoritma Apriori Terhadap Data Penjualan,” vol. 15, no. 1, pp. 85–90, 2019.
E. Buulolo, “Implementasi Algoritma Apriori Pada Sistem Persediaan Obat ( Studi Kasus : Apotik Rumah Sakit Estomihi Medan) Implementasi Algoritma Apriori Pada Sistem Persediaan Obat (Studi Kasus : Apotik Rumah Sakit Estomihi Medan ),” Pelita Inf. Budi Darma, vol. IV, no. January, 2017.
M. Afdal and M. Rosadi, “Penerapan Association Rule Mining Untuk Analisis,” vol. 5, no. 1, pp. 99–108, 2019.
K. Tampubolon, H. Saragih, B. Reza, K. Epicentrum, A. Asosiasi, and A. Apriori, “Implementasi Data Mining Algoritma Apriori Pada Sistem Persediaan Alat-Alat Kesehatan,” pp. 93–106, 2013.
A. Kurniawati, “Pemetaan Pola Hubungan Program Studi Dengan Algoritma Apriori – Studi Kasus SPMU UNNES,” vol. 1, no. 1, pp. 51–58, 2014.
D. P. Mulya, “Analisa dan Implementasi Association Rule Dengan Algoritma Fp-Growth Dalam Seleksi Pembelian Tanah Liat (Studi Kasus Di Pt. Anveve Ismi Berjaya),” J. Teknol. Dan Sist. Inf. Bisnis, vol. 1, no. 1, pp. 47–57, 2019.
D. Prabowo and F. Ramdani, “Penerapan Algoritma Apriori Untuk Rekomendasi Buku Pada Amikom Resource Center Sistem Informasi Universitas AMIKOM Yogyakarta Abstraksi Keywords :,” vol. 3, no. 1, pp. 8–12, 2020.
E. N. Sari, “Analisa Algoritma Apriori Untuk Menentukan Merek Pakaian Yang Paling Diminati Pada Mode Fashion Group Medan,” Pelita Inform. Budi Darma, vol. 4, no. 1, pp. 35–39, 2013.
A. Rahmadsyah and R. Rosnelly, “Analisa Association Rule Pada Algoritma Apriori Untuk Minat Pembelian Alat Kesehatan,” vol. 5, pp. 280–286, 2021, doi: 10.30865/mib.v5i1.2658.
N. Ritha, E. Suswaini, and W. Pebriadi, “Penerapan Association Rule Menggunakan Algoritma Apriori Pada Poliklinik Penyakit Dalam ( Studi Kasus : Rumah Sakit Umum Daerah Bintan ),” vol. 7, no. November, pp. 222–230, 2021, doi: 10.34128/jsi.v7i2.329.
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