Penerapan Algoritma Apriori dan FP-Growth Untuk Market Basket Analisis Pada Data Transaksi NonPromo
Abstract
This research aims to find association rules based on the transactions of Aksesmu members on non-promo items. The method in this study uses Association rules using the a priori algorithm and FP-Growth to obtain Frequent Itemsets. The data analysis phase is carried out starting with Exploratory Data Analysis, Pre-Processing Data, Transformation Data, and Data Mining, to evaluate the results of the formed association rules. Researchers conducted 4 experiments with a minimum support of 0.02 and a minimum confidence of 0.25 on a priori and FP-Growth was the best by producing 52 frequent itemsets and 17 association rules. With a dataset of 379,635, a priori is faster in processing frequent itemsets with a time of 1.10 seconds while FP-Growth is with 1.86 seconds. Apriori and FP-Growth produce the same frequent itemset, namely the highest category is obtained by SKT with a support of 0.32 and SKM with a support of 0.26, but the best association rules are produced by the Extruded & Pellet and Sweetened Condensed Milk categories with a confidence of 0.47, which if items in the Extruded & Pellet category are purchased together with Sweetened Condensed Milk category items have a success rate of 47%.
Keywords
Full Text:
PDFReferences
C. DATA, “Indonesia Pertumbuhan Penjualan Ritel,†ceicdata.com, 2023. https://www.ceicdata.com/id/indicator/indonesia/retail-sales-growth#:~:text=Pertumbuhan Penjualan Ritel Indonesia dilaporkan,-03%2C dengan 147 observasi. (accessed Dec. 12, 2022).
Aksesmu, “Aksesmu,†aksesmu.id, 2022. https://aksesmu.id/ (accessed Feb. 04, 2023).
T. Y. Tulsi, A. Erianda, and R. Afyenni, “Implementasi Metode Least Square untuk Peramalan Persediaan Barang Pada Sistem Inventori CV. Tre Jaya Perkasa,†JITSI J. Ilm. Teknol. Sist. Inf., vol. 3, no. 4, pp. 137–142, 2022, doi: 10.30630/jitsi.3.4.100.
C. R. Artsitella, A. R. Apriliani, and S. Ashari, “Penerapan Association Rules - Market Basket Analysis untuk Mencari Frequent Itemset dengan Algoritma FP-Growth,†J. Al-AZHAR Indones. SERI SAINS DAN Teknol., vol. 6, no. 2, p. 61, 2021, doi: 10.36722/sst.v6i2.661.
D. R. Pramuditya, M. A. Wicaksana, D. Kusuma, and Fiaz Ahmad Hanif, “Analisis Startegi Pemasaran Menggunakan Metode Assosiation Rule,†IENACO (Industrial Eng. Natl. Conf. 8 2020, vol. 8, pp. 274–278, 2020.
I. A. Ashari, A. Wirasto, D. Nugroho Triwibowo, and P. Purwono, “Implementasi Market Basket Analysis dengan Algoritma Apriori untuk Analisis Pendapatan Usaha Retail,†MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 21, no. 3, pp. 701–709, 2022, doi: 10.30812/matrik.v21i3.1439.
R. Haristyarini and W. Yustanti, “Penerapan Metode Market Basket Analysis dengan Algoritma Eclat dan Prediksi dengan Artificial Neural Network pada Data Transaksi Penjualan,†J. Emerg. Inf. Syst. Bus. Intell., vol. 02, no. 3, p. 2021, 2021.
H. Harianto and H. Eddy, “Analisa data transaksi penjualan barang menggunakan algoritme Apriori dan FP-Growth,†Jnanaloka, pp. 35–43, 2020, doi: 10.36802/jnanaloka.2020.v1-no1-6.
M. A. Febriyanti, "Analisis Pola Pembelian Pelanggan Berdasarkan Transaksi Penjualan Pada Ritel Dengan Metode Multilevel Association Rules", Universitas Islam Yogyakarta, no. 8.5.2017, pp. 2003–2005, 2022.
A. Rifqy Alfiyan, A. Hafidzul Kahfi, M. Rizky Kusumayudha, and M. Rezki, “Analisis Market Basket Dengan Algoritma Apriori Pada Transaksi Penjualan Di Freshfood,†IJCIT (Indonesian J. Comput. Inf. Technol., vol. 4, no. 1, pp. 1–8, 2019.
K. Nugraha, A. Handojo, and A. Setiawan, “Aplikasi Channel Management dan Point of Sales pada perusahaan retail PT. XYZ dengan menggunakan metode Cross-channel dan Market Basket Analysis,†J. Infra, 2022, [Online]. Available: https://publication.petra.ac.id/index.php/teknik-informatika/article/view/12652%0Ahttps://publication.petra.ac.id/index.php/teknik-informatika/article/viewFile/12652/10949
M. Mariko, “Perbandingan Algoritma Apriori Dan Algoritma Fp-Growth Untuk Rekomendasi Item Paket Pada Konten Promosi,†Explore, vol. 11, no. 2, p. 24, 2021, doi: 10.35200/explore.v11i2.438.
N. Rumui, rd Andi Roy, and th Pujo Hari Saputro Informatics Manajemen Politeknik Negeri FakFak FakFak, “Comparison of Apriori Algorithm and FP-Growth in Managing Store Transaction Data 1 st Syukron Anas, 2 nd,†Int. J. Comput. Inf. Syst. Peer Rev. J., vol. 03, no. 04, pp. 2745–9659, 2022, [Online]. Available: https://ijcis.net/index.php/ijcis/index
M. Radhi, A. Amalia, D. R. H. Sitompul, S. H. Sinurat, and E. Indra, “Analisis Big Data Dengan Metode Exploratory Data Analysis (Eda) Dan Metode Visualisasi Menggunakan Jupyter Notebook,†J. Sist. Inf. dan Ilmu Komput. Prima(JUSIKOM PRIMA), vol. 4, no. 2, pp. 23–27, 2022, doi: 10.34012/jurnalsisteminformasidanilmukomputer.v4i2.2475.
R. B. B. Sumantri and E. Utami, “Penentuan Status Tahapan Keluarga Sejahtera Kecamatan Sidareja Menggunakan Teknik Data Mining,†Respati, vol. 15, no. 3, p. 71, 2020, doi: 10.35842/jtir.v15i3.375.
A. M. A. A. Walenna and C. S. Pramudyo, “Analisis perancangan tata letak toko retail menggunakan metode market basket analysis dan activity relationship chart,†1st Conf. Ind. Eng. Halal Ind., no. 2007, pp. 267–274, 2019.
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, 2019, doi: 10.35315/dinamik.v23i2.7178.
E. Umar, D. Manongga, and A. Iriani, “Market Basket Analysis Menggunakan Association Rule dan Algoritma Apriori Pada Produk Penjualan Mitra Swalayan Salatiga,†J. Media Inform. Budidarma, vol. 6, no. 3, p. 1367, 2022, doi: 10.30865/mib.v6i3.4217.
A. A. Firdaus, N. Iksan, D. N. Sadiah, L. Sagita, and D. Setiawan, “Penerapan Algoritma Apriori untuk Prediksi Kebutuhan Suku Cadang Mobil,†J. Sist. dan Teknol. Inf., vol. 9, no. 1, p. 13, 2021, doi: 10.26418/justin.v9i1.41151.
H. E. Simanjuntak and W. Windarto, “Analisa Data Mining Menggunakan Frequent Pattern Growth pada Data Transaksi Penjualan PT Mora Telematika Indonesia untuk Rekomendasi Strategi Pemasaran Produk Internet,†J. Media Inform. Budidarma, vol. 4, no. 4, pp. 914–923, 2020, doi: 10.30865/mib.v4i4.2300.
I. Syukra, A. Hidayat, and M. Z. Fauzi, “Implementation of K-Medoids and FP-Growth Algorithms for Grouping and Product Offering Recommendations,†Indones. J. Artif. Intell. Data Min., vol. 2, no. 2, pp. 107–115, 2019.
P. I. Purnamasari, F. Marisa, I. D. Wijaya, T. Informatika, and U. W. Malang, “Sistem Pendukung Keputusan Rekomendasi Paket Menu,†vol. 11, no. 1, 2019.
DOI: https://doi.org/10.30865/mib.v7i3.6153
Refbacks
- There are currently no refbacks.
Copyright (c) 2023 JURNAL MEDIA INFORMATIKA BUDIDARMA

This work is licensed under a Creative Commons Attribution 4.0 International License.
JURNAL MEDIA INFORMATIKA BUDIDARMA
Universitas Budi Darma
Secretariat: Sisingamangaraja No. 338 Telp 061-7875998
Email: mib.stmikbd@gmail.com

This work is licensed under a Creative Commons Attribution 4.0 International License.