Penerapan Algoritma Apriori dalam Menganalisis Pola Minat Beli Konsumen di Coffee Shop
DOI:
https://doi.org/10.47065/bulletincsr.v6i4.1056Keywords:
Apriori Algorithm; Association Rule Mining; Purchase Intention Pattern; Data Mining; Street CoffeeAbstract
The currently intensive increase in competition within the cafe industry demands that business operators, such as Coffee shop Zecoff Tenggarong, not only focus on product quality but also gain a deep understanding of consumer behavior and buying interest patterns. This understanding is crucial for formulating targeted and sustainable business strategies. This research specifically focuses on analyzing consumer buying interest patterns at Coffee shop Zecoff Tenggarong through the identification of products that tend to be purchased together in a single transaction. To achieve this objective, the study employs a Data Mining approach using the Association Rule Mining technique. The core method implemented on the cafe's sales transaction data over a specific period is the Apriori Algorithm. This algorithm was chosen due to its effectiveness in processing large datasets and identifying frequently co-occurring itemsets. The data analysis process includes the stage of determining critical parameters: support (the frequency degree of the itemset), confidence (the strength of the causal relationship), and lift (the value of association improvement), which are collectively used to filter and generate the strongest and most relevant association rules. The empirical results of the study show that the Apriori Algorithm is highly effective in uncovering hidden purchasing patterns that are difficult to detect through conventional data analysis. The strong association rules derived from this mining process provide important and actionable information for the owner of Zecoff Tenggarong. The strategic implications of these findings include: formulating more targeted cross-selling marketing strategies (for example, recommending companion products that are certainly in demand), optimizing product arrangement (placing strongly associated items in close proximity), and increasing inventory management efficiency (ensuring that items frequently bought together are always in stock). In conclusion, this research concludes that the utilization of Data Mining technology with the Apriori Algorithm is a vital and transformative tool. It not only supports daily operational decision-making but also significantly enhances the coffee shop's competitiveness amidst a tight market rivalry.
Downloads
References
K. B. Andri, "Tren 2025: Peluang dan Daya Saing Kopi Indonesia," Badan Standardisasi Instrumen Pertanian (BSIP), 2025. [Online]. Available: https://tanamanindustri.bsip.pertanian.go.id/berita/tren-2025-peluang-dan-daya-saing-kopi-indonesia
M. Wildan, M. Irfan, and R. D. Sanjaya, "Perkembangan Dan Strategi Bisnis Coffee Shop di Era Modern: Studi Kasus Pada Coffee Shop Lokal di Indonesia," Al Mikraj: Jurnal Studi Islam dan Humaniora, vol. 5, no. 2, pp. 2210–2220, 2025, doi: 10.37680/almikraj.v5i2.7624.
Y. Muharmi and W. A. Pulungan, "Analisis Pola Transaksi Penjualan untuk Rekomendasi Menu Menggunakan Algoritma Apriori," Jurnal Pustaka AI, vol. 5, no. 2, pp. 265–273, 2025, doi: 10.55382/jurnalpustakaai.v5i2.1128.
F. Nuryawan and E. Mailoa, "Analisis Pola Minat Konsumen dengan Algoritma Apriori," Jurnal Ilmiah Teknologi Informasi dan Komunikasi (JTIK), vol. 15, no. 2, pp. 269–276, 2024.
R. Suganda and A. Solichin, "Analisis Pola Pembelian Pelanggan Menggunakan Algoritma Apriori untuk Strategi Cross Selling," Jurnal Sistem Informasi dan Teknologi, vol. 6, no. 1, pp. 45–53, 2024.
S. M. Amanda, D. Setiawan, and L. Trisnawati, "Penerapan Algoritma Apriori dalam Menganalisis Pola Minat Beli Konsumen di Coffee Shop," JEKIN (Jurnal Teknik Informatika), vol. 1, no. 2, pp. 26–32, 2023.
I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed. Burlington, MA, USA: Morgan Kaufmann, 2011.
D. T. Larose and C. D. Larose, Discovering Knowledge in Data: An Introduction to Data Mining, 2nd ed. Hoboken, NJ, USA: Wiley, 2014.
J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Waltham, MA, USA: Morgan Kaufmann, 2012.
C. I. Wiryawan, D. Nugroho, and Y. R. W. Utami, "Algoritma Apriori Untuk Penentuan Asosiasi Penjualan Barang," Jurnal TIKomSiN, vol. 9, no. 1, pp. 1–15, 2021.
P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining. Boston, MA, USA: Pearson, 2006.
R. Agrawal and R. Srikant, "Fast algorithms for mining association rules," in Proc. 20th Int. Conf. Very Large Data Bases (VLDB), Santiago, Chile, 1994, pp. 487–499.
R. Agrawal, T. Imieli?ski, and A. Swami, "Mining association rules between sets of items in large databases," in Proc. 1993 ACM SIGMOD Int. Conf. Management of Data (SIGMOD), Washington, DC, USA, 1993, pp. 207–216, doi: 10.1145/170035.170072.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Penerapan Algoritma Apriori dalam Menganalisis Pola Minat Beli Konsumen di Coffee Shop
ARTICLE HISTORY
How to Cite
Issue
Section
Copyright (c) 2026 Renaldi Nur Fahrizal, Ita Arfyanti, Ulfah Nurfadhila

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).













