<![CDATA[Functional dyspepsia remains a prevalent gastrointestinal disorder globally, with a higher burden in low- and middle-income countries such as Indonesia. Diagnostic challenges are exacerbated by limited healthcare infrastructure and a low ratio of gastroenterologists. Machine learning approaches offer a promising solution to enhance diagnostic consistency and accuracy in resource-limited settings. This study aims to compare the performance of the Random Forest (RF) and Support Vector Machine (SVM) algorithms in differentiating dyspepsia from gastroenteritis using Indonesian clinical data. A quantitative experimental method was applied using patient medical records, including gastrointestinal disease categories, vital signs, and symptom profiles. Data preprocessing was carried out by handling missing values through imputation and Min-Max scaling normalization. The dataset was divided into 80% training data and 20% testing data using stratified random sampling. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Random Forest demonstrated superior performance on all evaluation metrics compared to SVM. RF achieved 86.5% accuracy, 86.0% precision, 85.0% recall, and 85.5% F1-score, while SVM achieved 83.2% accuracy, 83.0% precision, 81.0% recall, and 82.0% F1-score. The 3.3 percentage point improvement in accuracy and 4.0 percentage point improvement in recall are clinically significant. Random Forest proved more effective in dyspepsia classification, showing better handling of complex clinical data interactions and more reliable diagnostic performance. These findings support the implementation of an RF-based decision support system in Indonesian healthcare facilities to improve diagnostic consistency and patient outcomes.]]>
