IMPROVING SALES CLASSIFICATION OF FASHION PRODUCTS AT SABHIRA OFFICIAL WITH RANDOM FORES

Abstract

Abstract

This research focuses on improving the accuracy of the fashion product sales classification model at Sabhira Official Store by applying the Random Forest algorithm. The approach used follows the stages of Knowledge Discovery in Database (KDD), which includes data selection, preprocessing, transformation, data mining, and evaluation. The research data consisted of 1,559 transactions in the period August to October 2023, with attributes such as product category, number of items sold, price, and sales category (low, medium, high). The model was developed using RapidMiner software, with a data split of 70% for training and 30% for testing. The analysis results show that the Random Forest algorithm is able to achieve an accuracy rate of 99.81%, with precision for the “High” category reaching 100%, while other categories have values above 99%. Evaluation using confusion matrix shows a very low prediction error rate, so this model can classify sales levels more accurately. The results of this study provide useful insights for Sabhira Official Store in stock management and data-driven promotion strategies.

Keywords— Random Forest, sales classification, fashion products, KDD.