<![CDATA[Student learning motivation and learning outcomes are important factors that influence educational success, especially at the junior high school level. Previous studies that primarily emphasize academic achievement prediction alone, this study simultaneously evaluates student motivation and learning outcomes as dual prediction targets. Moreover, while earlier research often applied only a single algorithm or focused on higher education datasets, this research specifically conducts a head-to-head comparison between Random Forest and Logistic Regression using junior high school data, thereby filling an important gap in secondary education predictive analytics. This study compares the performance of two machine learning algorithms, namely Random Forest and Logistic Regression, in predicting student motivation and learning outcomes based on data on learning habits, mental condition, attendance, sleep hours, family support, and academic grades. The study process included data pre-processing, normalization, separation of data into training and testing data, model training, and evaluation using accuracy, sensitivity, specificity, and AUC. Based on the study findings, Random Forest performed better with an accuracy of 0.91, sensitivity of 0.91, specificity of 0.94, and AUC of 0.94. Meanwhile, Logistic Regression obtained an accuracy of 0.84, sensitivity of 0.84, specificity of 0.90, and AUC of 0.95. These findings confirm that Random Forest is superior in its overall predictive ability, while Logistic Regression remains relevant due to its interpretability. This study aims to assist in the development of data-driven decision support systems in education to help schools identify students who require early intervention.]]>
