Utilization of Data Mining on MSMEs using FP-Growth Algorithm for Menu Recommendations
DOI:
https://doi.org/10.30812/matrik.v22i2.2166Keywords:
Data Mining, Fp-Growth, Menu Recommendation, MSMEAbstract
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.
Downloads
References
[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.
Downloads
Published
Issue
Section
How to Cite
Similar Articles
- Irawan Afrianto, Andri Heryandi, Sufa Atin, Blockchain-based Trust, Transparent, Traceable Modeling on Learning Recognition System Kampus Merdeka , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 22 No. 2 (2023)
- Budi Sumanto, Salima Nurrahma, Comparison of Random Forest Support Vector Machine and Passive Aggressive Models on E-nose-Based Aromatic Rice Classification , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 24 No. 3 (2025)
- Dela Ananda Setyarini, Agnes Ayu Maharani Dyah Gayatri, Christian Sri Kusuma Aditya, Didih Rizki Chandranegara, Stroke Prediction with Enhanced Gradient Boosting Classifier and Strategic Hyperparameter , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 23 No. 2 (2024)
- Imam Fahrur Rozi, Ahmadi Yuli Ananta, Endah Septa Sintiya, Astrifidha Rahma Amalia, Yuri Ariyanto, Arin Kistia Nugraeni, Analyzing the Application of Optical Character Recognition: A Case Study in International Standard Book Number Detection , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 24 No. 2 (2025)
- Rofik Rofik, Roshan Aland Hakim, Jumanto Unjung, Budi Prasetiyo, Much Aziz Muslim, Optimization of SVM and Gradient Boosting Models Using GridSearchCV in Detecting Fake Job Postings , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 23 No. 2 (2024)
- Miftahus Sholihin, Mohd Farhan Bin Md. Fudzee, Lilik Anifah, A Novel CNN-Based Approach for Classification of Tomato Plant Diseases , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 24 No. 3 (2025)
- Eka Hartati, Mardiana Mardiana, Evaluasi Penerapan Computer Based Test (CBT) sebagai Upaya Perbaikan Sistem pada Ujian Nasional untuk Sekolah Terpencil di Sumatera Selatan , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 18 No. 1 (2018)
- Aji Supriyanto, Jeffry Alfa Razaq, Purwatiningtyas Purwatiningtyas, Agus Ariyanto, Keputusan Pemberian Bantuan Sosial Program Keluarga Harapan Menggunakan Metode AHP dan SAW , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 21 No. 3 (2022)
- Apriani Apriani, Sandi Justitia Putra, Ismarmiaty Ismarmiaty, Ni Gusti Ayu Dasriani, E-Alert Application in Facing Earthquake Disaster , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 19 No. 2 (2020)
- Anjar Wanto, Ni Luh Wiwik Sri Rahayu Ginantra, Surya Hendraputra, Ika Okta Kirana, Abdi Rahim Damanik, Optimization of Performance Traditional Back-propagation with Cyclical Rule for Forecasting Model , MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer: Vol. 22 No. 1 (2022)
You may also start an advanced similarity search for this article.