Implementation of RFM Analysis to Enhance Sales Patterns of Food and Beverages at Bonjour Café and Resto Using the Apriori Algorithm
DOI:
https://doi.org/10.35870/ijsecs.v4i3.3073Keywords:
RFM Analysis, Apriori Algorithm, Segmentation, Customer, Frequent ItemsetAbstract
The rapid growth of the culinary business has made business competition in this field increasingly tight, so a strategy is needed to increase food and beverage sales patterns. Bonjur Cafe Resto serves many food and beverage menus, but business actors need to try to produce product innovations in order to provide satisfactory service to customers. In this condition, a data processing technique is needed to determine customer segmentation and menu recommendations at Bonjur Cafe Resto. The analysis method used is RFM Analysis by analyzing customer behavior, analyzing purchase transaction data consisting of Recency Frequency Monetary (RFM) attributes and data mining techniques with the Apriori algorithm, where this algorithm is used to determine the most frequently appearing data set (frequent itemset). The results of this study are grouped into five categories of customers based on their purchasing behavior and association rules are formed with predetermined parameters, support 28% and confidence 70%. This can later be a recommendation for a menu combination from the data that has been collected and applied using the apriori algorithm so that it is expected to be used for service evaluation and be able to increase customer satisfaction so that Bonjur Cafe Resto can develop better
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References
Sinaga, D. M., Sirait, W. H., & Windarto, A. P. (2021). Analisis algoritma Apriori dalam menentukan pola pemesanan konsumen pada Ucokopi. Journal of Informatics Management and Information Technology, 1(2), 68-73. https://doi.org/10.47065/jimat.v1i2.105.
Haidar, I. (2021). Implementasi algoritma Apriori untuk mencari pola transaksi penjualan (Studi Kasus: Carroll Kitchen). https://dspace.uii.ac.id/handle/123456789/36068.
Merliani, N. N., Khoerida, N. I., Widiawati, N. T., Triana, L. A., & Subarkah, P. (2022). Penerapan algoritma Apriori pada transaksi penjualan untuk rekomendasi menu makanan dan minuman. J. Nas. Teknol. dan Sist. Inf, 8(1), 9-16. https://doi.org/10.25077/TEKNOSI.v8i1.2022.9-16.
Nursikuwagus, A., & Hartono, T. (2016). Implementasi algoritma Apriori untuk analisis penjualan berbasis web. Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, 7(2), 701-706. https://doi.org/10.24176/simet.v7i2.784.
Susilowati, D., Hairani, H., Lestari, I. P., Marzuki, K., & Mardedi, L. Z. A. (2022). Segmentasi lokasi promosi penerimaan mahasiswa baru menggunakan metode RFM dan K-Means clustering. MATRIK: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, 21(2), 275-282. https://doi.org/10.30812/matrik.v21i2.1542.
Widiyanto, A. T., & Witanti, A. (2021). Segmentasi pelanggan berdasarkan analisis RFM menggunakan algoritma K-Means sebagai dasar strategi pemasaran (Studi Kasus PT Coversuper Indonesia Global). KONSTELASI: Konvergensi Teknologi dan Sistem Informasi, 1(1), 204-215. https://doi.org/10.24002/konstelasi.v1i1.4293.
Larasati, I., Yusril, A. N., & Al Zukri, P. (2021). Systematic literature review analisis metode Agile dalam pengembangan aplikasi mobile. Sistemasi: Jurnal Sistem Informasi, 10(2), 369-380. https://doi.org/10.32520/stmsi.v10i2.1237.
Pailan, Y. O., Chrisnanto, Y. H., & Hadianna, A. I. (2020). Segmentasi loyalitas pelanggan berbasis RFM (Recency, Frequency, Monetary) menggunakan K-Means pada PD. Persada Ikan. Prosiding SISFOTEK, 4(1), 167-171.
Taqwim, W. A., Setiawan, N. Y., & Bachtiar, F. A. (2019). Analisis segmentasi pelanggan dengan model RFM pada PT. Arthamas Citra Mandiri menggunakan metode fuzzy C-means clustering. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 3(2), 1986-1993.
Badaruddin, M., & Rayendra, R. (2022). Penerapan algoritma Apriori pada analisa data penjualan e-commerce. Jurnal Media Informatika Budidarma, 6(2), 1032-1037. https://doi.org/10.30865/mib.v6i2.3976.
Aditya, A., Marisa, F., & Purnomo, D. (2016). Penerapan algoritma Apriori terhadap data penjualan di Toko Gudang BM. Journal of Information Technology and Computer Science, 1(1), 224619. https://doi.org/10.31328/jointecs.v1i1.408.
Fauzi, R. M., & Mulyana, D. I. (2021). Implementasi data mining menggunakan metode least square untuk memprediksi penjualan lampu LED pada PT. Sumber Dinamika Solusitama. Jurnal Sosial Teknologi, 1(8), 907-919. https://doi.org/10.59188/jurnalsostech.v1i8.182.
Aprianti, W., Hafizd, K. A., & Rizani, M. R. (2017). Implementasi association rules dengan algoritma Apriori pada dataset kemiskinan. Limits: Journal of Mathematics and Its Applications, 14(2), 145-155. https://doi.org/10.12962/limits.v14i2.2933.
Yuliska, Y., & Syaliman, K. U. (2020). Literatur review terhadap metode, aplikasi dan dataset peringkasan dokumen teks otomatis untuk teks berbahasa Indonesia. IT Journal Research and Development, 5(1), 19-31. https://doi.org/10.25299/itjrd.2020.vol5(1).4688.
Novianti, D. (2019). Implementasi algoritma Naïve Bayes pada dataset hepatitis menggunakan Rapid Miner. Paradig.-J. Komput. dan Inform, 21(1), 49-54. https://doi.org/10.31294/p.v21i1.4979.
Sari, Y. R., Sudewa, A., Lestari, D. A., & Jaya, T. I. (2020). Penerapan algoritma K-Means untuk clustering data kemiskinan Provinsi Banten menggunakan RapidMiner. CESS (Journal Comput. Eng. Syst. Sci., 5(2), 192. https://doi.org/10.24114/cess.v5i2.18519.
Open Peer Review on Qeios (2020). Pseudocode. Definitions. National Cancer Institute. Pseudocode. NCI Thesaurus. Code C47900. https://doi.org/10.32388/TF77DY.
Darmawan, T., Birawa, A. S., Eryanto, E., & Mauritsius, T. (2020). Credit classification using CRISP-DM method on Bank ABC customers. International Journal of Emerging Trends in Engineering Research, 8(6). https://doi.org/10.30534/ijeter/2020/28862020.
Ahmad, I., Samsugi, S., & Irawan, Y. (2022). Implementasi data mining sebagai pengolahan data. J. Teknoinfo, 16(1), 46.
Siregar, Z. A. (2022). Analisis pola penjualan produk makanan dan minuman menggunakan algoritma Apriori. Journal of Informatics Management and Information Technology, 2(2), 65-72.
Dongga, J., Koru, N., & Lante, G. (2023). Implementasi data mining menggunakan algoritma Apriori dalam menentukan persediaan barang (Studi Kasus: Toko Swapen Jaya Manokwari). G-Tech: Jurnal Teknologi Terapan, 7(1), 119-126.
Muhammad, F. T. N., Vulandari, R. T., & Harsadi, P. (2022). Implementasi algoritma Apriori pada penentuan kombinasi menu UMKM XYZ Sukoharjo. Jurnal TIKomSiN, 10(2).
Rahayu, P. W., Bernadus, I. N., & Datya, A. I. (2024). Penerapan data mining dalam mengetahui pola transaksi pembelian obat menggunakan algoritma Apriori di Apotek Kharisma Farma Tiga. J-Icon: Jurnal Komputer dan Informatika, 12(1), 44-55.
Purnia, D. S., & Warnilah, A. I. (2017). Implementasi data mining pada penjualan kacamata menggunakan algoritma Apriori. IJCIT (Indonesian Journal on Computer and Information Technology, 2(2). https://doi.org/10.31294/ijcit.v2i2.2776.
Tana, M. P., Marisa, F., Wijaya, I. D., Informatika, J. T., & Widyagama, F. T. U. (2018). Penerapan metode data mining market basket analysis terhadap data penjualan produk pada toko Oase menggunakan algoritma Apriori. JIMP-J. Inform. Merdeka Pasuruan, 3(2), 17-22.
Takdirillah, R. (2020). Penerapan data mining menggunakan algoritma Apriori terhadap data transaksi penjualan bisnis ritel. Edumatic: Jurnal Pendidikan Informatika, 4(1), 37-46. https://doi.org/10.29408/edumatic.v4i1.2081.
Nurajizah, S. (2019). Analisa transaksi penjualan obat menggunakan algoritma Apriori. Jurnal Inovtek Polbeng Seri Informatika, 4(1), 35-44. https://doi.org/10.35314/isi.v4i1.938.
Kristania, Y. M., & Listanto, S. (2022). Implementasi data mining terhadap data penjualan dengan algoritma Apriori pada PT. Duta Kencana Swaguna. Jurnal Teknoinfo, 16(2), 364-372. https://doi.org/10.33365/jti.v16i2.1973.
Djamaludin, I., & Nursikuwagus, A. (2017). Analisis pola pembelian konsumen pada transaksi penjualan menggunakan algoritma Apriori. Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, 8(2), 671-678. https://doi.org/10.24176/simet.v8i2.1566.
Miranda, S. A., Fahrullah, F., & Kurniawan, D. (2022). Implementasi association rule dalam menganalisis data penjualan Sheshop dengan menggunakan algoritma Apriori. Metik Jurnal, 6(1), 30-36. https://doi.org/10.47002/metik.v6i1.342.
Khanza, M., Toyib, R. T., & Onsardi, O. (2021). Implementasi algoritma Apriori untuk meningkatkan penjualan handphone di Toko Mardha Cell. Journal Scientific and Applied Informatics, 4(2), 221-235. https://doi.org/10.36085/jsai.v4i2.1674.
Abidin, Z., Amartya, A. K., & Nurdin, A. (2022). Penerapan algoritma Apriori pada penjualan suku cadang kendaraan roda dua (studi kasus: Toko Prima Motor Sidomulyo). Jurnal Teknoinfo, 16(2), 225-232. https://doi.org/10.33365/jti.v16i2.1459.
Wibowo, A., & Handoko, A. R. (2020). Segmentasi pelanggan ritel produk farmasi obat menggunakan metode data mining klasterisasi dengan analisis recency frequency monetary (RFM) termodifikasi. J. Teknol. Inf. dan Ilmu Komput, 7(3).
Saputra, R., & Sibarani, A. J. (2020). Implementasi data mining menggunakan algoritma Apriori untuk meningkatkan pola penjualan obat. JATISI (Jurnal Teknik Informatika dan Sistem Informasi, 7(2), 262-276. https://doi.org/10.35957/jatisi.v7i2.195.
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