Penerapan Algoritma Apriori untuk Optimasi Strategi Penjualan Berdasarkan Analisis Pola Pembelian di Torsa Cafe
-
Anzas Ibezato Zalukhu
*
Universitas Pembangunan Panca Budi,
Indonesia
- Dewi Sartika Universitas Pembangunan Panca Budi Medan , Angola
- Sri Wahyuni Universitas Pembangunan Panca Budi Medan , Indonesia
Abstract
This study aims to analyze consumer purchasing patterns at Torsa Café using data mining methods with the Apriori algorithm to discover association rules between products that are frequently purchased together. In facing the increasingly competitive business environment in the food and beverage industry, understanding consumer purchasing behavior becomes key to enhancing marketing and operational strategies. This research uses sales transaction data from October 2024, consisting of 31 transactions with a total of 129 items. The analysis process begins with data collection and normalization of transaction data, followed by the application of the Apriori algorithm to calculate the support and confidence values of items in the transactions. The analysis results show several items with high support levels, such as "Sanger Espresso", "Avocado Cappuccino Torsa", and "Kopi Susu Torsa", with support values above 30%. Additionally, product combinations frequently purchased together, such as Kopi Tancap with Redvelvet, Macchiato, Frappucino, and Kopi Susu Torsa, can serve as the basis for promotions or more efficient stock management. These findings provide valuable insights for Torsa Café management to determine product placement strategies, raw material stock management, and design more targeted promotions based on the identified purchasing patterns. Therefore, the results of this study are expected to improve operational efficiency and enhance Torsa Café’s competitiveness in the increasingly competitive market.
References
F. Rozi Lubis, M. Khoiruddin Harahap, and A. Mahmud Husein, “Analisis Prediktif untuk Keputusan Bisnis : Peramalan Penjualan,” J. Data Sci. Indones., pp. 32–40, 2019.
K. Martowinangun, D. J. Sri Lestari, and K. Karyadi, “Pengaruh Strategi Promosi Terhadap Peningkatan Penjualan Di Cv. Jaya Perkasa Motor Rancaekek Kabupaten Bandung,” J. Co Manag., vol. 2, no. 1, pp. 139–152, 2019, doi: 10.32670/comanagement.v2i1.162.
Andisman and L. Hanifa, “PERUSAHAAN GO PUBLIK,” J. Ilm. Mhs. Fak. Ekon. Umbut., vol. 5, no. 2747–2779, pp. 189–202, 2023, [Online]. Available: https://jurnal-umbuton.ac.id/index.php/entries/article/download/5059/2458/18407
F. D. Putra, “Pengaruh Volume Penjualan Dan Biaya Produksi Kalung Terhadap Laba Pada Hidayah Shop Kuta-Badung,” J. Pendidik. Ekon. Undiksha, vol. 9, no. 2, p. 462, 2019, doi: 10.23887/jjpe.v9i2.20127.
D. A. C. Rachman, R. Goejantoro, and F. D. T. Amijaya, “Implementasi Text Mining Pengelompokkan Dokumen Skripsi Menggunakan Metode K-Means Clustering,” Eksponensial, vol. 11, no. 2, p. 167, 2021, doi: 10.30872/eksponensial.v11i2.660.
O. Pratama and J. Haerul Jaman, “Penerapan Data Mining Menggunakan Algoritma Apriori Untuk Mengetahui Kebiasaan Konsumen Dan Prediksi Stok Produk (Studi Kasus Toko Elektronik Wk),” 2023.
I. Nozomi, “Penerapan Data Mining Untuk Peringatan Dini Banjir Menggunakan Metode Klastering K-Means (Studi Kasus Kota Padang),” J. Sains Inform. Terap., vol. 2, no. 2, pp. 39–44, Jun. 2023, doi: 10.62357/JSIT.V2I2.165.
K. Erwansyah, “Implementasi Data Mining Untuk Menganalisa Hubungan Data Penjualan Produk Bahan Kimia Terhadap Persedian Stok Barang Menggunakan Algoritma FP ( Frequent Pattern ) Growth Pada PT . Grand Multi Chemicals,” J. Teknol. Sist. Inf. dan Sist. Komput. TGD (J-SISKO TECH), vol. 2, no. 2, pp. 30–40, 2019.
H. Kusumo, E. Sediyono, and M. Marwata, “Analisis Algoritma Apriori untuk Mendukung Strategi Promosi Perguruan Tinggi,” Walisongo J. Inf. Technol., vol. 1, no. 1, p. 49, 2019, doi: 10.21580/wjit.2019.1.1.4000.
C. N. Dengen, K. Kusrini, and E. T. Luthfi, “Penentuan Association Rule Pada Kelulusan Mahasiswa Menggunakan Algoritma Apriori,” J. Rekayasa Teknol. Inf., vol. 3, no. 1, p. 20, 2019, doi: 10.30872/jurti.v3i1.2256.
M. F. Mulya, N. Rismawati, and R. R. Alifi, “Analisis Dan Implementasi Data Mining Menggunakan Algoritma Apriori Untuk Meningkatkan Penjualan Pada Kantin Universitas Tanri Abeng,” Fakt. Exacta, vol. 12, no. 3, p. 210, 2019, doi: 10.30998/faktorexacta.v12i3.4541.
S. C. Bilqisth and K. Mustofa, “Determination of Temporal Association Rules Pattern Using Apriori Algorithm,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 14, no. 2, p. 159, 2020, doi: 10.22146/ijccs.51747.
X. Yuan, “An improved Apriori algorithm for mining association rules,” AIP Conf. Proc., vol. 1820, no. September, 2017, doi: 10.1063/1.4977361.
Hernawati, “Analisis Market Basket Dengan Algoritma Apriori,” Ikraith-Informatika, vol. 2, no. 18, pp. 13–17, 2018.
M. I. Madani, A. Padmo, A. Masa, and H. J. Setyadi, “PERBANDINGAN METODE APRIORI DAN FREQUENT PATTERN,” vol. 12, no. 2, 2024.
E. Devia, “Aplikasi Sistem Penjualan Menggunakan Teknik Data Mining Dengan Market Basket Analysis Dan Algoritma Apriori (Studi Kasus Pada : Jetlag Coffee),” J. Tek. Inform. dan Teknol. Inf., vol. 1, no. 2, pp. 28–40, 2021, doi: 10.55606/jutiti.v1i2.1085.
P. M. S. Tarigan, J. T. Hardinata, H. Qurniawan, M. Safii, and R. Winanjaya, “Implementasi Data Mining Menggunakan Algoritma Apriori Dalam Menentukan Persediaan Barang,” J. Janitra Inform. dan Sist. Inf., vol. 2, no. 1, pp. 9–19, Apr. 2022, doi: 10.25008/janitra.v2i1.142.
T. Prasetya, J. E. Yanti, A. I. Purnamasari, A. R. Dikananda, and O. Nurdiawan, “Analisis Data Transaksi Terhadap Pola Pembelian Konsumen Menggunakan Metode Algoritma Apriori,” INFORMATICS Educ. Prof. J. Informatics, vol. 6, no. 1, p. 43, 2022, doi: 10.51211/itbi.v6i1.1688.
S. Sunarti, F. Handayanna, and E. Irfiani, “Analisa Pola Penjualan Makanan Dengan Penerapan Algoritma Apriori,” Techno.Com, vol. 20, no. 4, pp. 478–488, 2021, doi: 10.33633/tc.v20i4.4715.
Copyright (c) 2024 Anzas Ibezato Zalukhu; Dewi Sartika; Sri Wahyuni
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
.png)
.png)
