Testing the C45 Algorithm with Rapid Miner for Stock Selection (Case Study: Toko Usaha Muda) - Hasugian, Paska Marto (Journal Of Data Science, 2023)
One of the keys to the success of a retail company is good stock management. Intuition-based methods are often not enough because customer demands are always changing. This research concentrates on the use of the C4.5 decision tree algorithm on the RapidMiner platform to optimize the selection of goods in the Toko Usaha Muda. This algorithm is used to predict future stock requirements by looking at previous sales patterns in stores and historical sales data. The results show a significant increase in the accuracy of stock predictions and a decrease in the probability of loss due to excess or stockouts. This implementation not only enhances the operations of the Toko Usaha Muda, but also provides a framework that other retail businesses can use to increase their profits through better stock management.