Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA
Andrew Aquila Chrisanto Pabendon
Universitas Kristen Satya Wacana, Salatiga
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Penerapan Algoritma Apriori dan FP-Growth Untuk Market Basket Analisis Pada Data Transaksi NonPromo Andrew Aquila Chrisanto Pabendon; Hindriyanto Dwi Purnomo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6153

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%.
Co-Authors Hindriyanto Dwi Purnomo