<![CDATA[Purpose – This study evaluated Random Forest with the Synthetic Minority Oversampling Technique (SMOTE) for classifying questionnaire-derived career-related psychological categories among fresh graduates and descriptively compared performance before and after class balancing. Methods – Data were collected from 250 fresh graduates using a self-developed 13-item, four-point Likert questionnaire covering seven operational dimensions. A stratified 80:20 holdout split was used for the primary comparison, while 5 × 5 repeated stratified cross-validation examined stability. Standard SMOTE and random oversampling were applied only to training data, and SMOTE-generated profiles were audited in both stored-integer and continuous-interpolation representations. Findings – On the selected holdout split, Random Forest without SMOTE achieved 96.00% accuracy and a 95.93% weighted F1-score, whereas the SMOTE model reached 100.00% for both measures by correcting two Moderate-to-Poor errors. Random oversampling achieved 98.00% holdout accuracy. Across 25 repeated validation folds, mean accuracy was 96.08% without SMOTE, 95.76% with SMOTE, and 95.92% with random oversampling; the baseline–SMOTE accuracy difference was not statistically significant (Wilcoxon p = 0.412). Research implications – This study combines a controlled comparison of baseline Random Forest, standard SMOTE, and random oversampling with synthetic-profile auditing and repeated validation, while explicitly distinguishing operational score-category reproduction from independently validated psychological prediction. Originality – This study combines a controlled comparison of baseline Random Forest, standard SMOTE, and random oversampling with synthetic-profile auditing and repeated validation, while explicitly distinguishing operational score-category reproduction from independently validated psychological prediction.]]>
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