Analisis dan Implementasi Market Basket Analysis (MBA) Menggunakan Algoritma Apriori dengan Dukungan Visualisasi Data

 (*)Septembri Rio Bagaskara Mail (Universitas Kristen Satya Wacana, Salatiga, Indonesia)
 Dwi Hosanna Bangkalang (Universitas Kristen Satya Wacana, Salatiga, Indonesia)

(*) Corresponding Author

Submitted: June 14, 2023; Published: July 2, 2023


Culture Coffee MSME is one of the MSMEs engaged in the culinary field and is experiencing business competition. A marketing strategy is needed with the right decision-making process so that the business can survive and excel. UMKM Culture Coffee uses a point of sales application to accommodate the transaction process and record transactions. Historical customer data can be processed into a basis for decision making for marketing strategies that effectively increase sales. However, the transaction data has not been used optimally. There is a need to analyze historical customer data that can generate information to form marketing strategies. Market Basket Analysis (MBA) is one of the methods in data mining used in knowing products that tend to be purchased together by customers known as Association Rule.  Association rules produce products in the form of packages or bundling which are used as marketing strategies. The marketing strategy obtained is supported by data visualization which contains information from the data. Apriori algorithm is used to generate association rules. The result of this research is an association rule on the historical data of MSME Culture Coffee customer purchases. Based on these rules, recommendations for selling menu packages to customers can be given. The purpose of this research is to find customer purchasing patterns which are used as the basis for decision making in determining menu sales. The results showed 2 product packages, namely, nuggets and french fries with sausages and french fries with a support and confidence value of 12.5% and 37.6% with 10.8% and 29% respectively. The results of this study can be used as a basis for the sales and marketing strategy of Culture Coffee MSMEs to increase business revenue.


Association Rule; Data Mining; Apriori algorithm; Market Basket Analysis; MSME

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A. E. P. Nugraha, “Pengelolaan dan Strategi UMKM di Era Disrupsi Digital,” Semin. Nas. KeIndonesiaan III, pp. 887–890, 2018.

T. T. H. Tambunan, UMKM di Indonesia: perkembangan, kendala, dan tantangan. Prenada Media, 2021.

M. H. Santoso, “Application of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom,” Brill. Res. Artif. Intell., vol. 1, no. 2, pp. 54–66, Dec. 2021, doi: 10.47709/brilliance.v1i2.1228.

I. N. W. Arta and K. Mandala, “Perumusan Strategi Pemasaran untuk Meningkatkan Keunggulan Kompetitif pada Koperasi Kuta Mimba di Kuta Badung,” E-Jurnal Manaj. Univ. Udayana, vol. 10, no. 6, 2021.

D. Sunyoto, “Dasar-Dasar Manajemen Pemasaran (Konsep, Strategi, dan kasus) Edisi Ketiga,” Jakarta CAPS (Center Acad. Publ. Serv., 2018.

Efrans Christian and W. Widiatry, “Sistem Informasi Point of Sale Berbasis Web Pada Distributor Alat Kesehatan,” J. Teknol. Inf. J. Keilmuan dan Apl. Bid. Tek. Inform., vol. 17, no. 1, pp. 69–80, 2023, doi: 10.47111/jti.v17i1.8003.

R. Novriansyah, I. Aknuranda, and W. Purnomo, “Pengembangan Sistem Informasi Musyawarah Dengan Metode Iteratif (Studi Kasus : Masjid Ibnu Sina Jl. Veteran, Malang),” J. Pengemb. Teknol. Inf. dan Ilmu Komputer; Vol 3 No 6, vol. 3, no. 6, p. 6200, 2019, [Online]. Available:

A. Amado, P. Cortez, P. Rita, and S. Moro, “Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis,” Eur. Res. Manag. Bus. Econ., vol. 24, no. 1, pp. 1–7, 2018, doi:

I. H. Witten, E. Frank, and M. A. Hall, “Data Mining: Practical Machine Learning Tools and Techniques, Third Edition,” Data Min. Pract. Mach. Learn. Tools Tech. Third Ed., pp. 1–629, Jan. 2011, doi: 10.1016/C2009-0-19715-5.

M. L. Waskom, “Seaborn: statistical data visualization,” J. Open Source Softw., vol. 6, no. 60, p. 3021, 2021.

A. C. Telea, Data visualization: principles and practice, Second edi. CRC Press, 2014.

M. Engebretsen and H. Kennedy, Data visualization in society. Amsterdam University Press, 2020.

D. Jollyta, W. Ramdhan, and M. Zarlis, Konsep Data Mining Dan Penerapan. Deepublish, 2020.

J. Han, M. Kamber, and J. Pei, “Data Mining. Concepts and Techniques, 3rd Edition (The Morgan Kaufmann Series in Data Management Systems),” 2011.

E. Alpaydin, Introduction to machine learning. MIT press, 2020.

H. O. L. Wijaya, A. A. T. S, A. Armanto, and W. M. Sari, “Prediksi Pola Penjualan Barang pada UMKM XYZ dengan Metode Algoritma Apriori,” J. Sist. Komput. dan Inform., vol. 3, no. 4, p. 432, 2022, doi: 10.30865/json.v3i4.4200.

M. P. Tana, F. Marisa, and I. D. Wijaya, “Penerapan Metode Data Mining Market Basket Analysis Terhadap Data Penjualan Produk Pada Toko Oase Menggunakan Algoritma Apriori,” J I M P - J. Inform. Merdeka Pasuruan, vol. 3, no. 2, pp. 17–22, 2018, doi: 10.37438/jimp.v3i2.167.

D. Rizaldi and A. Adnan, “Market Basket Analysis Menggunakan Algoritma Apriori: Kasus Transaksi 212 Mart Soebrantas Pekanbaru,” J. Stat. dan Apl., vol. 5, no. 1, pp. 31–40, 2021, doi: 10.21009/jsa.05103.

J. Han, J. Pei, and H. Tong, Data Mining: Concepts and Techniques. in The Morgan Kaufmann Series in Data Management Systems. Elsevier Science, 2022. [Online]. Available:

H. Yang, “Data preprocessing,” Pennsylvania State Univ. Citeseer, 2018.

M. Soni, Y. Barot, and S. Gomathi, “A review on privacy-preserving data preprocessing,” J. Cybersecurity Inf. Manag., vol. 4, no. 2: Special Issue-RIDAPPH, p. 16, 2021.

M. S. Zakaria, “Data visualization as a research support service in academic libraries: An investigation of world-class universities,” J. Acad. Librariansh., vol. 47, no. 5, p. 102397, 2021, doi:

S. Darrab, D. Broneske, and G. Saake, “Modern Applications and Challenges for Rare Itemset Mining,” Int. J. Mach. Learn. Comput., vol. 11, no. 3, pp. 208–218, 2021, doi: 10.18178/ijmlc.2021.11.3.1037.

M. K. Gupta and P. Chandra, “A comprehensive survey of data mining,” Int. J. Inf. Technol., vol. 12, no. 4, pp. 1243–1257, 2020.

N. A. Azeez, T. J. Ayemobola, S. Misra, R. Maskeliūnas, and R. Damaševičius, “Network intrusion detection with a hashing based apriori algorithm using Hadoop MapReduce,” Computers, vol. 8, no. 4, p. 86, 2019.

J. M. Luna, P. Fournier‐Viger, and S. Ventura, “Frequent itemset mining: A 25 years review,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 9, no. 6, p. e1329, 2019.

M. Abdel-Basset, M. Mohamed, F. Smarandache, and V. Chang, “Neutrosophic association rule mining algorithm for big data analysis,” Symmetry (Basel)., vol. 10, no. 4, pp. 1–19, 2018, doi: 10.3390/sym10040106.

C. O. Wilke, Fundamentals of data visualization: a primer on making informative and compelling figures. O’Reilly Media, 2019.

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