Analisis Perbandingan Algoritma Apriori dan FP-Growth dalam Asosiasi Data Mining pada Data Transaksi Minimarket FS Barokah Berbasis Google Colab

Bagus Aris Setiawan

doi:10.33365/jdmsi.v7i2.2340

Bagus Aris Setiawan Universitas Muria Kudus

DOI: https://doi.org/10.33365/jdmsi.v7i2.2340

Abstrak

Data Mining is a data analysis technique used to discover hidden patterns in transaction data to support business decision-making. This study aims to analyze consumer purchasing patterns and compare the performance of the Apriori and Frequent Pattern Growth (FP-Growth) algorithms using transaction data from FS Barokah Minimarket. The dataset consisted of 1,000 transactions with 27 unique product items and underwent data cleaning, transformation, and one-hot encoding processes. The analysis was conducted using Google Colab with the pandas and mlxtend libraries. The results showed that both Apriori and FP-Growth produced identical outputs, namely 139 frequent itemsets and 224 association rules. The strongest association pattern was found between Air Galon and Chocolate with a lift ratio of 1.31, followed by Biscuits and Cheese with a lift ratio of 1.24. In terms of execution time, Apriori performed faster than FP-Growth on the analyzed dataset. These findings can support cross-selling strategies, product bundling, and more effective product placement in FS Barokah Minimarket.

Referensi

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