<![CDATA[Farmers often experience difficulties in determining the types of crops that are suitable for current weather conditions, which can lead to improper planting schedules and an increased risk of crop failure. This study aims to develop a crop selection recommendation system based on historical weather data using a machine learning approach. The method employed in this research is supervised learning with the Decision Tree C4.5 algorithm, which is capable of performing data classification and generating predictive models in the form of decision trees that are easy to understand for non-technical users such as farmers. The system is designed to process historical weather data and agricultural environmental characteristics to produce optimal crop recommendations. The Decision Tree C4.5 algorithm is chosen due to its high interpretability and good classification accuracy. With the implementation of this system, it is expected that fa]]>
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