Comparing Decision Tree and SVM with Hyperparameter Tuning for Classifying Perceived Social Media Impact on Academic Performance - Nur Sitorus; Yusuf Ramadhan Nasution (Journal of Digital Technology and Computer Science, 2026)
Purpose – This study compares the performance of Decision Tree and Support Vector Machine (SVM) in classifying students’ perceived academic impact of social media use and examines the contribution of Grid Search-based hyperparameter tuning to model performance. Methods – A quantitative experimental approach was applied to a subset of records obtained from a publicly available Kaggle dataset. The records were retained in their original sequence. Data preprocessing included data quality inspection, binary target encoding, One-Hot Encoding for categorical variables, and feature scaling for numerical variables used in the SVM model. The dataset was sequentially divided into training, validation, and testing subsets without shuffling or stratification. Grid Search was performed using a predefined validation split, while model performance was assessed using accuracy, weighted precision, weighted recall, and weighted F1-score. Findings – Hyperparameter tuning improved the overall performance of the Decision Tree model compared with its default configuration. In contrast, the default and optimized SVM models produced identical results because Grid Search selected the same parameter configuration as the default settings. Among the evaluated models, SVM achieved the highest observed testing performance. Research implications – The findings indicate that the effectiveness of hyperparameter tuning may vary across machine learning algorithms. Grid Search improved the Decision Tree configuration while confirming the suitability of the default SVM settings within the evaluated search space. Since the target variable reflects students’ perceived academic impact rather than objectively measured academic achievement, the results should be interpreted within this specific classification context and the limitations of a single public dataset. Originality – This study provides a controlled comparison of default and optimized Decision Tree and SVM models under identical experimental conditions, highlighting algorithm-specific responses to Grid Search-based hyperparameter tuning.