<![CDATA[The significant increase in diabetes mellitus cases within the community demands a technology-based solution that can provide accurate, efficient, and reliable predictions. This study aims to evaluate the impact of various data preprocessing schemes on the performance of the Gaussian Naive Bayes (GNB) and Support Vector Machine (SVM) algorithms in classifying diabetes risk. The dataset used in this research was sourced from the UCI Machine Learning Repository and consists of 520 records with 16 symptom features and 1 target label. The preprocessing stages include handling missing values, encoding categorical features, normalizing numerical data using StandardScaler, balancing the dataset with the Synthetic Minority Over-sampling Technique (SMOTE), and feature selection using the SelectKBest method. A total of nine preprocessing scheme combinations were tested for each algorithm. The experimental results show that for the GNB model, the best performance was achieved using the combination of StandardScaler, SMOTE, and SelectKBest (k=5), reaching an accuracy of 94.53%, precision 98.36%, recall 90.91%, and f1-score 94.49%. Meanwhile, for the SVM model, the highest performance was obtained through the combination of StandardScaler and RBF kernel hyperparameter tuning, achieving an accuracy of 99.04%, precision 99.05%, recall 99.04%, and f1-score 99.03%. The evaluation was conducted using metrics such as accuracy, precision, recall, F1-score, confusion matrix, and learning curve visualization. These findings highlight the critical role of proper preprocessing in enhancing predictive model performance. This study is expected to serve as a reference for developing early detection systems for diabetes based on machine learning.]]>
