<![CDATA[Rice farming plays a strategic role in Indonesia's economy and food security because rice is the main source of energy for the community. Rice yields are greatly influenced by environmental factors, particularly weather and soil conditions. This study aims to predict rice yields by utilizing machine learning technology using multiple linear regression methods. The research was conducted in West Ungaran District with independent variables in the form of weather and soil condition data and dependent variables in the form of rice harvest yields. The analysis results show that the multiple linear regression model is valid and significant, as proven by the ANOVA test with a significance value of 0.03 (< 0.05). The correlation coefficient value of 0.739 indicates a strong relationship, while the coefficient of determination (R square) value of 0.630 indicates that 63% of the variation in yield can be explained by the model. These findings show that machine learning has the potential to support decision-making in maintaining food production stability.]]>
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