ANALISIS DATA MINING DALAM PENGELOLAAN PERSEDIAAN STOK DENGAN ALGORITMA RANDOM FOREST DAN APRIORI (STUDI KASUS: TOKO CERIA BABYSHOP)

ANALISIS DATA MINING DALAM PENGELOLAAN PERSEDIAAN STOK DENGAN ALGORITMA RANDOM FOREST DAN APRIORI (STUDI KASUS: TOKO CERIA BABYSHOP), Data mining, Random Forest, Apriori, stok Inventory, Toko Ceria Babyshop...

Author: ANZAS IBEZATO ZALUKHU
Date: 2025
Keywords: Data mining, Random Forest, Apriori, stok Inventory, Toko Ceria Babyshop
Type: Jurnal
Category: penelitian

This research analyzes inventory management at Toko Ceria Babyshop by applying data mining techniques, specifically Random Forest and Apriori algorithms. Effective inventory management is crucial for aligning product availability with market demand, preventing overstocking or stockouts, and optimizing operational costs. Sales transaction data from June to December 2024, comprising 20,578 sales transactions, 3,593 purchase entries, 2,736 initial stock entries, and 1,331 final stock entries, were divided into 80:20 training and testing sets. The Random Forest implementation showed that weekly purchase quantity predictions were more effective than monthly predictions, evidenced by lower Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) values for weekly predictions (16.10, 1.76, 4.01) compared to monthly (39.68, 3.19, 6.30). Furthermore, the R-squared (R²) value was higher for the weekly model (0.21) than the monthly (0.04), indicating better weekly prediction accuracy. The Apriori algorithm successfully identified product association patterns for both 2-itemsets and 3-itemsets, with all rules exhibiting lift values above 1, signifying positive relationships between products. This purchasing pattern information is highly beneficial for developing marketing strategies such as bundling, shelf arrangement, cross-selling promotions, and improved inventory planning.

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