Analisis Preferensi Pelanggan Semanis Coffe And Resto terhadap Kombinasi Menu Makanan dan Minuman Menggunakan Data Mining: (Association Rule Dan Clustering)

Adit Septian Saepul Millah; Hendi Suhendi

doi:10.61132/merkurius.v4i3.1460

Authors

Adit Septian Saepul Millah Universitas Adhirajasa Reswara Sanjaya
Hendi Suhendi Universitas Adhirajasa Reswara Sanjaya

DOI:

https://doi.org/10.61132/merkurius.v4i3.1460

Keywords:

Coffee Shop, Customer Preferences, Data Mining, FP-Growth, K-Means

Abstract

The coffee shop industry in Indonesia is experiencing rapid growth that requires business owners to optimize data-driven strategies. This study aims to analyze customer preferences at Semanis Coffee and Resto using data mining methods to support more effective business decision-making. The method used is Market Basket Analysis with the FP-Growth algorithm for association rule mining and the K-Means algorithm for customer segmentation. The research data consists of 672 sales transactions during the March-May 2025 period. The results of the association analysis with a minimum support of 0.004 and a minimum confidence of 0.2 resulted in five valid rules with a lift ratio above 1. The strongest rule is the combination of Americano→Milk Choco with a confidence of 42.9% and an elevator ratio of 5.229, indicating a strong linkage between products. The most popular products are Milk Choco (10.8%) and Americano (8.5%). Customer segmentation analysis identified three clusters: Cluster 0 (Loyal Customers) 80% with high frequency but low transaction value; Cluster 1 (Occasional Customers) 10% with low activity; and Cluster 2 (Large Buyers) 10% with high transaction value but low frequency. This study concludes that product bundling strategies, loyalty programs, reactivation campaigns, and premium services can be applied to increase the effectiveness of coffee shop businesses.

References

Ananda, R., & Yamani, A. Z. (2020). Penentuan centroid awal K-means pada proses clustering data evaluasi pengajaran dosen. Jurnal, 1(3), 544–550.

Aqliyah, Z. H., Kurniawan, R., & Suprapti, T. (2025). FP-growth algorithm for association model optimization in household sales data. Journal of Artificial Intelligence and Engineering Applications. Retrieved from https://ioinformatic.org/

Dio, R., Hidayati, J., Arifin, R., Putera, D. A., & Dermawan, A. A. (2023). Analisis data mining pembelian dengan association rule market basket analysis menggunakan algoritma FP-growth. Jurnal PASTI (Penelitian dan Aplikasi Sistem dan Teknik Industri), 17(2), 187. https://doi.org/10.22441/pasti.2023.v17i2.005

Fadhilah, S. A. (2022). Analisis perilaku pembelian konsumen menggunakan metode association rule–market basket analysis dan clustering analysis (Studi kasus: Jore Coffee & Eatery).

Gohae, M., Dakhi, P., & Duha, T. (2021). Pengaruh perilaku konsumen terhadap keputusan pembelian pada UD Anisa Kecamatan Telukdalam Kabupaten Nias Selatan.

Irawan, Y. (n.d.). Penerapan data mining untuk evaluasi data penjualan menggunakan metode clustering dan algoritma hirarki divisive.

Komitek, J. (n.d.). Application of the K-means clustering algorithm in mapping the regional voter strategy for the legislative candidates for the DPR RI.

Laksono, B., Syahidin, Y., & Yunengsih, Y. (2024). Implementasi data mining klasterisasi data pasien rawat inap dengan algoritma K-means clustering. Jurnal Teknologi Sistem Informasi dan Aplikasi, 7(2), 621–627. https://doi.org/10.32493/jtsi.v7i2.39354

Minarni, P. A., Hasibuan, M. S., & Prasetya, F. D. (n.d.). Implementasi data mining dalam analisa pola peminjaman buku di perpustakaan Universitas Muhammadiyah Pringsewu menggunakan metode association rule.

Munanda, E., & Monalisa, S. (2021). Penerapan algoritma FP-growth pada data transaksi penjualan untuk penentuan tata letak barang. Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi, 7(2), 173–184.

Nugraheni, W., & Nugroho, A. (2023). Penerapan metode market basket analysis (MBA) dengan algoritma. Jurnal Teknologi Informasi dan Komunikasi, 7(4). https://doi.org/10.35870/jti

Padli, M., Yasir, A., & Hasugian, B. S. (2023). Computer science and information technology. Computer Science and Information Technology, 4(2), 182–191.

Purwati, N., & Karnila, S. (2023). Strategi peningkatan penjualan produk menggunakan market basket analysis. Jurnal Sistem Informasi Bisnis, 13(2), 96–103. https://doi.org/10.21456/vol13iss2pp96-103

Srikandi, A. E., & Sunarti, K. (2016). Analisis faktor-faktor preferensi pelanggan dan pengaruhnya terhadap keputusan pembelian (Studi terhadap pelanggan McDonald’s di Indonesia dan Malaysia). Retrieved from http://www.aboutmcdonalds.com

Sugianto, C. A., & Sukmawati, D. (2023). Penerapan algoritma FP-growth untuk mengetahui pola pada data transaksi percetakan (Studi kasus Java Printing Batujajar). Jurnal, 5, 20–26.

Valencia, S., & Atmojo, W. T. (2024). Analisis pola pembelian pada data penjualan CanNgopi menggunakan algoritma FP-growth. Jurnal Komtika (Komputasi dan Informatika), 8(2), 214–224. https://doi.org/10.31603/komtika.v8i2.12672

Whendasmoro, R. G., & Joseph, J. (2022). Analisis penerapan normalisasi data dengan menggunakan Z-score pada kinerja algoritma K-NN. JURIKOM (Jurnal Riset Komputer), 9(4), 872. https://doi.org/10.30865/jurikom.v9i4.4526