Sandal Product Inventory Prediction System Using Apriori Algorithm on Web-Based Home Industry Dlioshoes

Septia Ona Sutra (Universitas Islam Negeri Sumatera Utara)
Triase Triase (Universitas Islam Negeri Sumatera Utara)

Journal of Dinda : Data Science, Information Technology, and Data Analytics
Vol 5 No 1 (2025): February

Sales of sandal products at Home Industry Dlioshoes are often faced with the problem of insufficient product inventory. This research aims to build a web-based sales information system by applying the Apriori algorithm to analyze purchasing patterns and predict product inventory needs. By utilizing sales transaction data, this system can identify product combinations that distributors often buy together. By implementing the Apriori Algorithm, it can help industry owners in making decisions regarding product inventory and can predict sales in the next period, thereby reducing the risk of product excess or shortage. The research results show that the types or models of sandals that are most popular with distributors are Heels, Flat Shoes, Mules, Ballet Shoes, High Heels, Ankle Strap and Pumps. With the highest Support value of 42% and Confidence value of 71.18%.