Indonesia’s sugar industry has yet to become self-sufficient in sugar production. This is due to the unpredictable and fluctuating relationship between yield (sugar content (%)) and sugarcane productivity (Ton/Ha) in all state-owned and private Indonesian sugar mills. As a result, Indonesia’s domestic sugar consumption is still balanced by sugar imports. This study aimed to identify the main criteria and predict sugarcane yield and productivity using vegetative growth indicators in sugarcane cultivation using data science and a machine learning technique based on SVR and RF. The study found that the essential features for predicting sugarcane yield are clear juice, Pol, purity, Brix, and maturity factor, whereas the number of stems, stem height, stem weight, rainfall, and juring factor are important for predicting sugarcane productivity. The best model to predict sugarcane yield (%) was generated using RF with an average absolute error rate of 0.074% and accuracy in predicting yield with an average absolute percentage error of 0.010% and a sugarcane yield prediction error rate of 0.129%. The best sugarcane productivity prediction model was generated using SVR with an average absolute error rate of 0.051 tons/ha and accuracy in forecasting productivity with an average absolute percentage error of 0.001% and a sugarcane productivity prediction error rate of 0.058 tons/ha. This model may be used to optimize sugar cane cultivation and harvesting times, resulting in increased productivity and yields, which benefits corporate performance and increases national sugar output.

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