Algoritma Apriori Untuk Memberikan Rekomendasi Menu Makanan Berdasarkan Tren Belanja Konsumen
Abstract
The increasing number of competitors means culinary businesses must innovate to increase their sales. Culinary businesses, especially Resto Sebelas Rasa, find it challenging to sell various food products. One strategy that can be applied is providing additional menu recommendations based on consumer purchasing trends. The additional menu recommendations must be by the menu segmentation that consumers have purchased in previous transactions. The Apriori algorithm can answer the need for innovation in the culinary business to increase sales levels for more varied food menus. The association rule algorithm can identify patterns of food menu combinations in the purchase transaction dataset. The Apriori algorithm calculates the support, confidence level, and lift values to determine how big the opportunity is and how strong the relationship is between food menu combinations. The Apriori algorithm has been successfully developed to increase the efficiency of consumer purchasing time by up to 90% and increase the variation in sales levels by up to 80%.
Keywords: Segmentation; Buying trends; Apriori algorithm; Artificial intelligence
Abstrak
Semakin banyaknya kompetitor membuat bisnis kuliner harus berinovasi untuk meningkatkan penjualannya. Saat ini bisnis kuliner khususnya di Resto Sebelas Rasa merasa kesulitan menjual variasi produk makanan. Salah satu strategi yang dapat diterapkan yaitu memberikan rekomendasi menu tambahan kepada konsumen berdasarkan tren pembelian konsumen. Rekomendasi menu tambahan yang diberikan harus sesuai dengan segmentasi menu yang telah dibeli oleh konsumen pada transaksi sebelumnya. Algoritma Apriori dapat menjawab kebutuhan inovasi dalam bisnis kuliner untuk meningkatkan tingkat penjualan terhadap menu makanan yang lebih bervariatif. Algoritma asossiation rule dapat mengidentifikasi pola perpaduan menu makanan dalam dataset transaksi pembelian. Algoritma Apriori melakukan perhitungan terhadap nilai support, confidence level, dan lift untuk mengetahui seberapa besar peluang dan seberapa kuat hubungan perpaduan menu makanan. Algoritma Apriori berhasil dikembangan sehingga mampu meningkatkan efisiensi waktu pembelian konsumen hingga 90% dan meningkatkan variasi tingkat penjualan hingga 80%.
Keywords
References
U. Santoso, M. Gardjito, and E. Harmayani, Makanan Tradisional Indonesia Seri 2: Makanan Tradisional yang Populer (Sup, Mi, Set Menu Nasi, Nasi Goreng, dan Makanan Berbasis Sayur), 2nd ed. Yogyakarta: UGM Press, 2019.
BPS, Statistik-Karakteristik-Usaha-2022-2023, https://www.bps.go.id/id/publication/ 2023/12/22/ 140fcce371d95181d426827c/statistik-karakteristik-usaha-2022-2023.html, 2023, diakses pada 12-4-2025,
M. H. F. Fajri, N. N. Zaki, R. A. Pratama, and I. Noviyanti, “Keberlanjutan Usaha Kuliner: Analisis Tantangan Dan Strategi Pengembangan,” Jurnal Masharif Al-Syariah, vol. 9, no. 3, pp. 2297-2314, 2024, doi: https://doi.org/10.30651/jms.v9i3.23889.
S. Daud, M.L. Fransiska, W.N. Wijaya, & N.M. Augustine. Analisis Inovasi Produk dan Diversifikasi Menu Untuk Meningkatkan Daya Saing (Studi Kasus pada UMKM Kuliner “Dapur Rumi” di Bandar Jaya, Kabupaten Lampung Tengah, Provinsi Lampung). Yume: Journal of Management, vol. 8, no. 1, pp. 69-78, 2025
W. C. Anggundari, A. I. Suroso, and B. Setiawan, “Strategi dan Model Bisnis Usaha Kuliner Pasca Pandemi: Studi Kasus Rumah Makan XYZ,” Jurnal Aplikasi Bisnis dan Manajemen, vol. 9, no. 1, pp. 239-239, Jan. 2023, doi: 10.17358/jabm.9.1.239.
A. Muksin, F. Dwi Mulya, and I. R. Hadari, “Pengetahuan Dan Ketrampilan Wirausaha Sebagai Kunci Sukses Bisnis Kuliner,” Ikraith-Ekonomika, vol. 6, no. 3, pp. 287-292, 2023, doi: 10.37817/ikraith-ekonomika.v6i3.
B. H. Situmorang, A. Isra, D. Paragya, and D. A. A. Adhieputra, “Apriori Algorithm Application for Consumer Purchase Patterns Analysis,” Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika, vol. 21, no. 1, pp. 15-20, 2024.
K. D. Fernanda, A. P. Widodo, and J. Lemantara, “Analysis and Implementation of the Apriori Algorithm for Strategies to Increase Sales at Sakinah Mart,” Juita: Jurnal Informatika, vol. 11, no. 2, pp. 203–211, Nov. 2023.
Kavitha and Subbaiah, “Association Rule Mining using Apriori Algorithm for Extracting Product Sales Patterns in Groceries,” International Journal of Engineering Research & Technology (IJERT), vol. 8, no. 3, pp. 1-4, 2020. [Online]. Available: www.ijert.org
H. Xie, “Research and Case Analysis of Apriori Algorithm Based on Mining Frequent Item-Sets,” Open J Soc Sci, vol. 09, no. 04, pp. 458–468, 2021, doi: 10.4236/jss.2021.94034.
S. SWaghere, S. Sonar, S. Kawad, and K. Murudkar, “Apriori Algorithm Using Map Reduce,” International Journal of Research & Review (www.ijrrjournal.com), vol. 5, no. 3, pp. 129-138, 2018, [Online]. Available: www.ijrrjournal.com
M. H. Hasibuan and M. Fakhriza, “Apriori Algorithm to Predict Availability of Beauty Products,” Architecture and High Performance Computing, vol. 6, no. 3, pp.1232-1244, 2024, doi: 10.47709/cnapc.v6i3.4259.
H. Bin Wang and Y. J. Gao, “Research on parallelization of Apriori algorithm in association rule mining,” in Procedia Computer Science, Elsevier B.V., 2021, pp. 641–647. doi: 10.1016/j.procs.2021.02.109.
D. Rizaldi and A. Adnan, “Market Basket Analysis Menggunakan Algoritma Apriori: Kasus Transaksi 212 Mart Soebrantas Pekanbaru,” Jurnal Statistika dan Aplikasinya, vol. 5, no. 1, pp. 31-40, 2021.
M. Habib Nazlis, F. Insani, A. Nazir, and I. Afrianty, “Implementasi Algoritma Improve Apriori Terhadap Keluarga Beresiko Stunting,” vol. 5, no. 3, pp. 740–749, 2024, doi: 10.37859/coscitech.v5i3.8364.
J. Hong, R. Tamakloe, and D. Park, “Application of association rules mining algorithm for hazardous materials transportation crashes on expressway,” Accid Anal Prev, vol. 142, p. 105497, Jul. 2020, doi: 10.1016/j.aap.2020.105497.
C. Wang, “Efficient customer segmentation in digital marketing using deep learning with swarm intelligence approach,” Inf Process Manag, vol. 59, no. 6, p. 103085, Nov. 2022, doi: 10.1016/j.ipm.2022.103085.
D. Vyas, “Client Analytics Originated on Segmentaion, Holding Then Apriori Procedure,” IJRAR, vol. 6, no. 1, pp. 566-569, 2019, [Online]. Available: www.ijrar.org
S. Wei, Y. Lin, L. Wan, G. Lin, Y. Zhang, and H. Zhang, “Developing a grid-based association rules mining approach to quantify the impacts of urbanization on the spatial extent of mangroves in China,” International Journal of Applied Earth Observation and Geoinformation, vol. 102, p. 102431, Oct. 2021, doi: 10.1016/j.jag.2021.102431.
S. Sugriyono and M. U. Siregar, “Preprocessing kNN algorithm classification using K-means and distance matrix with students’ academic performance dataset,” Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 4, pp. 311-316, Oct. 2020, doi: 10.14710/jtsiskom.2020.13874.
K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Global Transitions Proceedings, vol. 3, no. 1, pp. 91–99, Jun. 2022, doi: 10.1016/j.gltp.2022.04.020.
L. Tommy, D. Novianto, & Y.S. Japriadi. Sistem Rekomendasi Hybrid untuk Pemesanan Hidangan Berdasarkan Karakteristik dan Rating Hidangan. Journal of Applied Informatics and Computing, vol. 4, no. 2, pp. 137-145, 2020.
A.A. Priatna, R. Megasari, & J. Kusnendar. Penerapan Association Rules Menggunakan Algoritma Apriori Pada Sistem Rekomendasi Pemilihan Resep Obat Berdasarkan Data Rekam Medis. JATIKOM: Jurnal Aplikasi dan Teori Ilmu Komputer, vol. 1, no. 2, pp. 55-60, 2018.
L. Tommy, C. Kirana, & V. Lindawati. Recommender System dengan Kombinasi Apriori dan Content-Based Filtering pada Aplikasi Pemesanan Produk. J. Teknoinfo, vol. 13, no. 2, pp. 84-95, 2019.
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