<![CDATA[This study aims to classify product quality in the agricultural industry using the Random Forest algorithm. The data used includes various inspection result parameters, such as dimensions, weight, product color, quality status, defect image, inspection time, temperature, machine speed, and indicator lights. The model is developed to classify products into "good" and "defective" categories, and is evaluated based on accuracy metrics and confusion matrix analysis. The results show that the Random Forest model is able to achieve an accuracy of 85% in classifying product quality. Based on the confusion matrix, the model has a perfect prediction rate for good quality products (100% precision) and several misclassifications in the defect category. Feature importance analysis shows that the parameters of inspection time, machine temperature, and defect image are the most significant factors in determining product quality. This study proves that the Random Forest algorithm can be a reliable tool to support the product quality inspection process in the agricultural industry, with further integration into IoT-based systems, this approach can improve the efficiency of the inspection process, reduce manual errors, and ensure more consistent product quality standards.]]>
