Analisis Perbandingan Algoritma Apriori dan FP-Growth untuk Menentukan Strategi Penjualan Pada Maestro Jakarta Cafe & Space
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There has been a decline in sales at Maestro Jakarta Cafe & Space due to a lot of competition and not optimal management of transaction data so that innovation is needed to increase sales. This study aims to compare the performance and results of the Apriori and FP-Growth algorithms in analyzing sales transaction data to determine the optimal sales strategy. This research uses the Apriori and FP-Growth methods to analyze sales transaction data by applying the Cross-Industry Standard Process for Data Mining (CRISP-DM). The data used is product sales transaction data from November 2023 to April 2024. The results of performance comparisons in processing time speed and memory usage that have been carried out show that in processing time speed the FP-Growth algorithm is slightly faster than the Apriori algorithm while in the use of memory capacity the Apriori algorithm requires a larger memory capacity than the memory capacity used by the FP-Growth algorithm. This shows that the performance of the FP-Growth algorithm is better than the Apriori algorithm. The analysis results of the Apriori and FP-Growth algorithms on sales transaction data using a minimum support value of 1% and a minimum confidence value of 100% resulted in 22 association rules. Both algorithms produce identical rules, with the only difference being the occurrence index. The results of this analysis can be used by Maestro Jakarta Cafe & Space in determining sales strategies.
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Arhami, S. M., & Nasir, S. M. Data Mining-Algoritma dan Implementasi. Andi Offset, 2020.
Fadillah, N., & Wicaksono, B. S. (2023). IMPLEMENTASI ALGORITMA APRIORI PADA DATA MINING UNTUK MENENTUKAN STRATEGI PENJUALAN DENGAN MENGGUNAKAN DATA TRANSAKSI. OKTAL: Jurnal Ilmu Komputer dan Sains, 2(03), 882-889.
Kurniana, T., Lestari, A., & Oktaviyani, E. D. (2023). Penerapan Algoritma Apriori untuk Mencari Pola Transaksi Penjualan Berbasis Web pada Cafe Sakuyan Side. KONSTELASI: Konvergensi Teknologi Dan Sistem Informasi, 3(1), 13-23. DOI: https://doi.org/10.24002/konstelasi.v3i1.7005.
Lubis, Y. F. A., & Lestari, Y. D. (2023). IMPLEMENTASI ASSOCIATION RULES DENGAN ALGORITMA FP-GROWTH UNTUK MENINGKATKAN PENJUALAN BARANG. Jurnal Tekinkom (Teknik Informasi dan Komputer), 6(2), 886-892. DOI: https://doi.org/10.37600/tekinkom.v6i2.1038.
Musfar, T. F., Pratiwi, D., & Sitepu, R. F. (2022). Business Development Design Using the Business Model Canvas (BMC) Approach and Swot Analysis for One Alumni Coffee Pekan Baru. Jurnal Multidisiplin Madani, 2(12), 4377-4386. DOI: https://doi.org/10.55927/mudima.v2i12.2078.
Mustopa, M., Junaedi, I., & Sianipar, A. Z. (2021). Sistem Informasi Penjualan Dan Pengendalian Stock Barang Bangunan Pada Toko Bangunan Delima. Jurnal Manajamen Informatika Jayakarta, 1(2), 105-116. DOI: https://doi.org/10.52362/jmijayakarta.v1i2.447.
Nugroho, D. S., Islahudin, N., Normasari, V., & Al Hakiim, S. Z. (2024). PENERAPAN MARKET BASKET ANALYSIS (MBA) DATA MINING MENGGUNAKAN METODE ASOSIASI APPRIORI DAN FP-GROWTH PADA WAN CAFFEINE ADDICT YOGYAKARTA. JISI: Jurnal Integrasi Sistem Industri, 11(1), 121-134. DOI: https://doi.org/10.24853/jisi.11.1.121-134.
Prasetyo, E. (2012). Data mining konsep dan aplikasi menggunakan matlab. Yogyakarta: Andi, 1.
Rahimsyah, M. L., & Ramdhani, Y. (2024). ANALISIS ALGORITMA FP-GROWTH DAN APRIORI UNTUK MENEMUKAN MODEL ASOSIASI TERBAIK PADA DATASET ONLINE RETAIL. Kohesi: Jurnal Sains dan Teknologi, 3(1), 21-30. DOI: https://doi.org/10.3785/kohesi.v3i1.2866.
Rosmayati, I., Wahyuningsih, W., Harahap, E. F., & Hanifah, H. S. (2023). Implementasi Data Mining pada Penjualan Kopi Menggunakan Algoritma Apriori. jurnal algoritma, 20(1), 99-107. DOI: https://doi.org/10.33364/algoritma/v.20-1.1259.
Sihombing, E. I., Yetri, M., & Ginting, R. I. (2020). Penerapan Market Basket Analysis Terhadap Data Penjualan Produk Pupuk Cair Dengan Menggunakan Metode Algoritma Apriori Dan Frequent pattern growth (Fp Growth) Di Ud. Anugerah Tani Saribudolok. Jurnal Cyber Tech, 3(2). DOI: https://doi.org/10.53513/jct.v3i2.3130.
Syahriani, S. (2022). Penerapan Data Mining Dalam Menentukan Pola Penjualan Sepatu Menggunakan Algoritma FP-Growth. JURIKOM (Jurnal Riset Komputer), 9(6), 1920-1927. DOI: http://dx.doi.org/10.30865/jurikom.v9i6.5148.