Utilization of Data Mining on MSMEs using FP-Growth Algorithm for Menu Recommendations

  • Firman Noor Hasan Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta, Indonesia
  • Achmad Sufyan Aziz Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta, Indonesia
  • Yos Nofendri Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta, Indonesia
Keywords: Data Mining, Fp-Growth, Menu Recommendation, MSME


Existing transaction data is only recorded and stored as a sales transaction memorandum, so it has not been utilized optimally. The data is only stored and used as transaction history. The availability of a lot of data and having a pattern of sales transactions that are similar to MSME Cafe Over Limit will be utilized by using data mining science. This research uses the association rules method. Implementation of fp-growth to get item combinations. The purpose of this research is to make it easier for MSMEs to determine menu recommendations for customers. The fp-growth algorithm is used to process as many as 2038 transaction data with a minimum support value of 10%, while for a minimum confidence value of 50%. So that there are 3 rules, namely "if you order Mariam chocolate cheese milk then the customer will order Kopsus Overlimit", from this rule it will form a support value of 10.79%, using a confidence value of 54.19% and a lift ratio of 0.93. Furthermore "if you order Kopsus Overlimit then you will order tofu at grandma's house", from the rule it will produce a support value of 34.69%, with a specified confidence value of 59.76%, so the lift ratio value is 1.15. The last rule "if you order tofu at grandma's house, the customer orders Kopsus Overlimit", from the rule that occurs, the support value is 34.69%, with a confidence value of 66.7% and a lift ratio of 1.15. The results of the study found the two best rules, namely "if the customer orders over-limit Kopsus, he will order tofu at grandma's house" and "if he orders tofu at grandma's house, the customer orders over-limit Kopsus". Based on the results of the rules formed, it can be concluded that only two rules can be categorized as valid and can be used as a reference in food and beverage menu recommendations at MSME Cafe Over Limit. So the results of this study can be useful to be applied to MSMEs, especially in terms of menu recommendations.


Download data is not yet available.


[1] F. N. Hasan and A. Febriandirza, “Perancangan Data Warehouse Untuk Data Penelitian di Perguruan Tinggi Menggunakan Pendekatan Nine Steps Methodologhy,” Pseudocode, vol. VIII, no. 1, pp. 49–57, 2021.
[2] V. Plotnikova, M. Dumas, and F. Milani, “Applying the CRISP-DM data mining process in the financial services industry: Elicitation of adaptation requirements,” Data Knowl. Eng., 2022.
[3] M. Shawkat, M. Badawi, S. El-ghamrawy, R. Arnous, and A. El-desoky, “An optimized FP-growth algorithm for discovery of association rules,” J. Supercomput., vol. 78, pp. 5479–5506, 2022.
[4] F. N. Hasan, “Implementasi Sistem Business Intelligence Untuk Data Penelitian di Perguruan Tinggi,” in Prosiding Seminar Nasional TEKNOKA 4, 2019, vol. 4, no. 2502, pp. I1–I10.
[5] F. N. Dhewayani, D. Amelia, D. N. Alifah, B. N. Sari, and M. Jajuli, “Implementasi K-Means Clustering untuk Pengelompokkan Daerah Rawan Bencana Kebakaran Menggunakan Model CRISP-DM,” J. Teknol. dan Inf., vol. 12, no. 1, pp. 76–89, 2022.
[6] J. Ferreira, “Café nation? Exploring the growth of the UK café industry,” AREA Ethics In/Of Geoghraphical Res., vol. 49, no. 1, pp. 69–76, 2016.
[7] A. Bartschat, M. Reischl, and R. Mikut, “Data Mining Tools,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 9, no. 4, pp. 1–14, 2019.
[8] P. S. Nishtala, T. yuan Chyou, F. Held, D. G. Le Couteur, and D. Gnjidic, “Association rules method and big data: Evaluating frequent medication combinations associated with fractures in older adults,” Pharmacoepidemiol. Drug Saf., vol. 27, no. 10, pp. 1123–1130, 2018.
[9] J. R. Chang, Y. S. Chen, C. K. Lin, and M. F. Cheng, “Advanced Data Mining of SSD Quality Based on FP-Growth Data Analysis,” Appl. Sci., vol. 11, no. 4, pp. 1–15, 2021.
[10] S. Kurniawan, W. Gata, and H. Wiyana, “Analisis Algoritma FP-Growth Untuk Rekomendasi Produk Pada Data Retail Penjualan Produk Kosmetik (Studi Kasus: MT Shop Kelapa Gading).,” Semin. Nas. Teknol. Inf. Dan Komun. 2018 (SENTIKA 2018), vol. 8, pp. 61–69, 2018.
[11] A. Maulana and A. A. Fajrin, “Penerapan Data Mining Untuk Analisis Pola Pembelian Konsumen Dengan Algoritma Fp-Growth Pada Data Transaksi Penjualan Spare Part Motor,” KLIK Kumpul. J. Ilmu Komput., vol. 5, no. 1, 2018.
[12] A. Salam, J. Zeniarja, W. Wicaksono, and L. Kharisma, “Pencarian Pola Asosiasi Untuk Penataan Barang Dengan Menggunakan Perbandingan Algoritma Apriori Dan Fp-Growth (Study Kasus Distro Epo Store Pemalang),” DInamik, vol. 23, no. 2, pp. 57–65, 2018.
[13] W. N. Setyo and S. Wardhana, “Implementasi Data Mining Pada Penjualan Produk Di Cv Cahaya Setya Menggunakan Algoritma Fp-Growth,” PETIR J. Pengkaj. dan Penerapan Tek. Inform., vol. 12, no. 1, pp. 54–63, 2019.
[14] G. Gunadi and D. I. Sensuse, “Penerapan Metode Data Mining Market Basket Analysis Terhadap Data Penjualan Produk Buku Dengan Menggunakan Algoritma Apriori dan Frequent Pattern Growth(FP-Growth): Studi Kasus Percetakan PT.Gramedia,” Telemat. MKOM, vol. 4, no. 1, pp. 118–132, 2022.
[15] F. Sidik, I. Suhada, A. H. Anwar, and F. N. Hasan, “Analisis Sentimen Terhadap Pembelajaran Daring Dengan Algoritma Naive Bayes Classifier,” J. Linguist. Komputasional, vol. 5, no. 1, pp. 34–43, 2022.
[16] F. N. Hasan and M. Dwijayanti, “Analisis Sentimen Ulasan Pelanggan Terhadap Layanan Grab Indonesia Menggunakan Multinominal Naïve Bayes Classifier,” J. Linguist. Komputasional, vol. 4, no. 2, pp. 52–58, 2021.
[17] L. Shabtay, P. Fournier-viger, R. Yaari, and I. Dattner, “A Guided FP-Growth algorithm for mining multitude-targeted item-sets and class association rules,” Inf. Sci. (Ny)., vol. 553, pp. 353–375, 2020.
[18] V. Plotnikova, M. Dumas, and F. Milani, “Adapting the CRISP-DM Data Mining Process: A Case Study in the Financial Services Domain,” RCIS (International Conf. Res. Challenges Inf. Sci., vol. 415, 2021.
[19] D. Venkatesan, S. K. Kannan, M. Arif, M. Atif, and A. Ganeshan, “Sentimental Analysis of Industry 4.0 Perspectives Using a Graph-Based Bi-LSTM CNN Model,” Mob. Inf. Syst., vol. 2022, no. Special Issues, pp. 1–14, 2022.
[20] F. N. Hasan, F. Sidik, and P. Afikah, “Sentiment Analysis of Community Response on Cooking Oil Price Increase Policy with Naïve Bayes Classifier Algorithm,” J. Linguist. Komputasional, vol. 5, no. 2, pp. 71–76, 2022.
[21] R. M. Anggraeni, “Perbandingan Algoritma Apriori dan FP-Growth Untuk Perekomendasi Buku Pada Transaksi Peminjaman di Perpustakaan Universitas Dian Nuswantoro,” Universitas Dian Nuswantoro, 2014.
How to Cite
Hasan, F., Aziz, A., & Nofendri, Y. (2023). Utilization of Data Mining on MSMEs using FP-Growth Algorithm for Menu Recommendations. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 22(2), 261-270. https://doi.org/https://doi.org/10.30812/matrik.v22i2.2166