Comparative Performance of Apriori, FP-Growth, and ECLAT for Menu Bundling

Astriana Putri Kumala Dewi; Noor Latifah; Supriyono Supriyono

doi:10.30871/jaic.v10i4.13696

Authors

Astriana Putri Kumala Dewi Universitas Muria Kudus
Noor Latifah Universitas Muria Kudus
Supriyono Supriyono Universitas Muria Kudus

DOI:

https://doi.org/10.30871/jaic.v10i4.13696

Keywords:

Association Rule, Apriori, ECLAT, FP-Growth, Menu Bundling

Abstract

Transaction data at Selaras Coffee and Space had not been systematically utilized to evaluate menu combinations or determine which association rule mining algorithm best suited the data characteristics. This study compares Apriori, FP-Growth, and ECLAT for generating menu-bundling recommendations. Sales records from 1–31 December 2025 were preprocessed by removing 950 operational transaction-item pairs, resulting in 6,213 transactions and 76 unique menus. The algorithms were evaluated on the same binary matrix using a minimum support of 0.01, a minimum confidence of 0.20, and a lift ratio greater than 1. The evaluation included parameter sensitivity, 30 repeated measurements of execution time and peak Python memory allocation, scalability using 25–100% of the transactions, and rule quality based on support, confidence, lift, leverage, conviction, and cosine similarity. All algorithms produced identical outputs of 68 frequent itemsets and five eligible rules. On the full dataset, ECLAT recorded the lowest mean execution time at 0.037701 s, followed by Apriori at 0.040419 s and FP-Growth at 0.069637 s. FP-Growth used the lowest mean peak memory at 1.026966 MB, while ECLAT showed the lowest runtime growth as the dataset size increased. The strongest rule was Mie Laksa → Air Mineral 330 Ml, with a lift of 2.755987. These findings show that no algorithm dominated every criterion: ECLAT offered the best full-data runtime and scalability, FP-Growth was the most memory-efficient, and the extracted rules provided measurable candidates for menu-bundling strategies.

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References

[1] R. P. Aditya, N. Wanti, and W. Sari, “Rekomendasi Paket Menu Pada Cafe Abc Berbasis Website Implementation Of Apriori Algorithm For Menu Package Recommendations At Cafe Abc Based On Websites,” vol. 11, no. 2, 2023.

[2] T. Kurniana, A. Lestari, and E. D. Oktaviyani, “Penerapan Algoritma Apriori untuk Mencari Pola Transaksi Penjualan Berbasis Web pada Cafe Sakuyan Side,” vol. 3, no. 1, pp. 13–23, 2023.

[3] Y. Muharmi and W. A. Pulungan, “E ISSN : 2809-4069 Analisis Pola Transaksi Penjualan Untuk Rekomendasi Menu Menggunakan Algoritma Apriori,” vol. 5, no. 2, pp. 265–273, 2025.

[4] S. R. Karimah and E. D. Udayanti, “Comparison of Apriori and FP-Growth Algorithms in Market Basket Analysis for Online Book Sales,” vol. 10, no. 1, pp. 566–574, 2026.

[5] D. Dwiputra, A. M. Widodo, H. Akbar, G. Firmansyah, and U. E. Unggul, “Evaluating The Performance Of Association Rules In Apriori And Fp-Growth Algorithms : Market Basket Analysis To Discoverrules Of Item Combinations,” vol. 2, no. 8, pp. 1229–1248, 2023, doi: 10.58344/jws.v2i8.403.

[6] dan A. N. Pujiharto, Kusrini, “Comparative Analysis of The Performance of The Apriori, FP-Growth and Eclat Algorithms In Finding Frequency Patterns In The Ina-Cbg’s Dataset,” vol. 9, no. 2, pp. 340–354, 2023.

[7] R. Wahyuningsih, A. Suharsono, and N. Iriawan, “Comparison Of Market Basket Analysis Method Using Apriori Algorithm , Frequent Pattern Growth ( Fp- Growth ) And Equivalence Class Transformation ( Eclat ) ( Case Study : Supermarket ‘ X ’ Transaction Data For 2021 ),” pp. 192–201, 2021.

[8] S. Marselina, J. H. Jaman, and D. E. Kurniawan, “Sales Analysis Using Apriori Algorithm in Data Mining Application on Food and Beverage ( F & B ) Transactions,” vol. 7, no. 2, pp. 218–223, 2023.

[9] I. G. Ngurah, B. Picessa, K. Mandala, I. K. A. Purnawan, I. M. Agus, and D. Suarjaya, “Implementation of FP-Growth Algorithms for Promo Package Determination in a Scooter Motorcycle Workshop Business,” vol. 9, no. 3, pp. 756–764, 2025.

[10] S. R. Bagaskara and D. H. Bangkalang, “Analisis dan Implementasi Market Basket Analysis ( MBA ) Menggunakan Algoritma Apriori dengan Dukungan Visualisasi Data,” vol. 4, pp. 612–620, 2023, doi: 10.30865/json.v4i4.6351.

[11] M. H. Nasri, G. Y. Pratama, R. Hammad, and I. N. Switrayana, “Integrasi Association Rule Mining dan Cost-Plus Pricing untuk Optimasi Paket Produk dan Profitabilitas UMKM,” vol. 11, no. 1, pp. 48–56, 2026.

[12] Y. Husain, E. D. Oktaviyani, and S. Christina, “Analisis Perbandingan Algoritma Apriori, FP-Growth, dan Eclat dalam Menemukan Pola Pembelian Konsumen,” vol. 3, no. 2, pp. 231–243, 2023.

[13] A. D. Ardiani, M. P. Kein, S. Marfuah, and N. Wanti, “Perbandingan Algoritma Apriori dan FP- Growth dalam Menemukan Pola Asosiasi pada Data Penjualan Produk Ritel di Toko IT Data Mining,” vol. 5, no. April, 2026.

[14] R. A. Putra, M. Amalia, M. Putri, and S. M. Sinaga, “Implementation of Association Rules Algorithm to Identify Popular Topping Combinations in Orders,” vol. 1, no. January, pp. 95–101, 2024.

[15] D. Alfitra, M. Afdal, M. Fronita, and E. Saputra, “Analisa Keranjang Belanja untuk Menentukan Tata Letak Barang Menggunakan Algoritma FP-Growth Market Basket Analysis for Determine Goods Layout Using FP-Growth Algorithm,” vol. 13, pp. 1651–1661, 2024.

[16] G. T. Alfaridzi, F. N. Salisah, and I. Permana, “Application of Apriori and FP-Growth Algorithms in Analyzing Drug Purchasing Patterns,” vol. 5, no. 1, pp. 128–134, 2026.

[17] A. C. Simanjuntak, M. E. Sitanggang, and M. Indah, “Penerapan Metode Data Mining Market Basket Analysis Terhadap Data Penjualan Produk Pada Toko Iblite Luxury Menggunakan Algoritma Apriori,” no. 3, 2024.

[18] A. J. Purwanto, A. Setiawan, and N. Latifah, “Penerapan UCD dalam Sistem Peminjaman Barang dan Ruangan Dengan Laravel,” vol. 5, pp. 7061–7074, 2025.

[19] A. Z. Mubarok and N. Latifah, “Decision Support System Internet Disruption Using ORESTE and Geolocation PT Cloud Solution,” vol. 18, no. 2, pp. 915–927, 2025.