<![CDATA[Facing the dynamics of a competitive and rapidly changing business environment, PT Prima Niaga Indomas, a snack food distribution company, encounters challenges in optimizing its product sales. Accurate sales forecasting is crucial to maintaining inventory efficiency, avoiding overstock, and meeting customer demand. This research aims to implement the C4.5 algorithm to predict the sales of snack food products at PT Prima Niaga Indomas. The location of PT Prima Niaga Indomas is at Puri Industrial Park 2000 Block D No. 5, Batam Centre, Batam City, Riau Islands. By leveraging historical sales data, product information, marketing factors, and external variables, this study seeks to develop a predictive model to assist the company in forecasting snack food product sales more accurately. The data generated from this research is expected to make a significant contribution to improving the operational efficiency of PT Prima Niaga Indomas. Data analysis is conducted by applying the C4.5 algorithm using WEKA software version 3.8.5, with manual calculations for comparison using Microsoft Excel. The research findings indicate the accuracy of data in the rules of delivery, durability, price, size, and quality. The researchers utilized two decision variables, namely, whether a product is sold or not. This study provides valuable insights into optimal inventory management, identification of sales trends, and faster responsiveness to market changes. The implication is that PT Prima Niaga Indomas can leverage this predictive model to enhance operational efficiency and productivity. Keywords: Algorithm C4.5, Data Mining, Sales Prediction]]>
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