<![CDATA[This study aims to predict product sales at CV Pelumas Murni Keluarga using the Random Forest method to overcome sales fluctuations that impact stock management and production planning. Uncertainty in sales forecasting can cause excess or shortage of stock, thus hampering the company's growth. This method was chosen because of its advantages in handling complex data and producing accurate predictions. The study was conducted quantitatively through observation and collection of automotive lubricant sales data from January to June 2023. Data was analyzed using the Google Colab application to implement the Random Forest model. The process involves data preprocessing, model building, and evaluation using out-of-bag data. The results of the study show that the Random Forest method is able to significantly increase the accuracy of sales predictions, providing a stronger foundation in developing sales strategies and inventory management. Thus, this study is expected to help CV Pelumas Murni Keluarga in optimizing operational efficiency and increasing profitability.]]>
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