Comparative Analysis of Muslim Clothing Sales Predictions Using the C4.5 Method and Linear Regression
Abstract
This study aims to develop a sales data prediction model using the machine learning method. Sales are an important indicator in the business world because they can provide information about company performance, market trends, and support better decision-making. However, accurate and reliable prediction of sales data is often a complex challenge. In this study, the researchers collected historical sales data from Farhan Stores that included information about time, product, category, and price. This study also aims to apply data mining techniques to predict sales of Muslim clothes at Farhan stores using the C4.5 algorithm and the linear regression algorithm. The prediction method is used in this study and the calculations are performed using Google Collab. The results of the research that was conducted to predict sales of robes and shirts at Farhan Stores show that the best-selling item during the sales period from January to July 2022 was Sabiyan robes, which were the most sold item or can be said to be the Best Seller item at Farhan Stores. In this study, the parameters MAE (Mean Absolute Error), MSE (Mean Squared Error), and the R2 score are used to evaluate prediction performance. In the linear regression algorithm, the MAE value is 43,633.21, the MSE value is 4,005,924,352.66, and the R2 score is 0.94. Whereas in the C4.5 algorithm, the MAE value was 44,823.96, the MSE value was 50,233,775.14, and the R2 score was 0.94.
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