Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal JURNAL ILMIAH INFORMATIKA
Unu, Dea Puspita
Unknown Affiliation
Computer Science & IT

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

Found 1 Documents
Search

 1 
data mining Implementasi Data Mining Menggunakan Algoritma Apriori dalam Memprediksi Persediaan Pupuk Tanaman di Toko Marni: Data Mining Unu, Dea Puspita; Yenni, Yusli
JURNAL ILMIAH INFORMATIKA Vol 14 No 01 (2026): Jurnal Ilmiah Informatika (JIF)
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v14i01.10674

Abstract

This research aims to apply the Apriori algorithm to predict the inventory of plant fertilizers at Toko Pupuk Marni. The primary focus of this study is to analyze sales transaction data as a means to enhance the efficiency of inventory management. The background of this research is rooted in the difficulties faced in managing inventory due to inefficient manual recording, which can lead to excess or shortage of stock. The method employed in this study is data mining, with the Apriori algorithm as the main technique for identifying purchasing patterns and relationships among items in the data. The analyzed data includes fertilizer sales transactions during the year 2024. The analysis results indicate that the Apriori algorithm can generate significant association rules, such as the relationship between Apsa and Rovral fertilizers, with a confidence level reaching 53.2%. Testing conducted using RapidMiner shows results consistent with manual analysis, thereby demonstrating the effectiveness of the Apriori algorithm in uncovering customer purchasing patterns. These findings provide valuable insights for store managers in determining better inventory strategies based on historical data. Thus, this research is expected to contribute significantly to the development of inventory information systems in the agricultural sector and serve as a reference for future studies related to data mining and inventory management.
Co-Authors Yenni, Yusli