Prediksi Tingkat Pengetahuan Mahasiswa Menggunakan Logistic Regression dan Random Forest - Triyasri, Novita; Andini, Rekha Apriliana; Chandra, Aurea Ivana; Saprianti, Assyifa; Yusiandra, Erick (Journal of Data Science Methods and Applications, 2026)
particularly for predicting students’ knowledge levels more accurately. This study aims to compare the performance of Logistic Regression and Random Forest algorithms in predicting students’ knowledge levels using the User Knowledge Modeling dataset. The dataset consists of 258 instances with five numerical attributes, namely STG, SCG, STR, LPR, and PEG, and one target variable UNS representing students’ knowledge levels. The reserch stages include data selection, preprocessing, data normalization, train-test splitting, and handling class imbalance using the SMOTE method. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results show that Logistic Regression outperforms Random Forest, achieving higher accuracy and F1-score values. These findings indicate that the relationships among variables in the dataset tend to be linear. Therefore, Logistic Regression is considered more suitable for predicting students’ knowledge levels in this study.