Algoritma Random Forest dan Synthetic Minority Oversampling Technique (SMOTE) untuk Deteksi Diabetes
DOI:
https://doi.org/10.14421/jiska.2025.10.2.221-234Keywords:
Detection, Diabetes, Random Forest, Synthetic Minority Oversampling Technique, EnsembleAbstract
Diabetes is one of the challenges in global health. Indonesia ranks 5th in the world with the highest rate of diabetes. This research uses the Random Forest algorithm for diabetes detection. The purpose of the study is to detect diabetes with the Random Forest algorithm that provides accurate and efficient results in the early diagnosis of diabetic patients. The data used is secondary data "Diabetes Dataset" which consists of 952 data and has 17 features. The test scenario in this study divides the data consisting of 3 parts, namely scenario 1 90%:10% ratio, scenario 2 70%:30% ratio, scenario 3 50%:50% ratio. In each scenario, a comparison between using SMOTE and not using SMOTE is applied. The best performance results are obtained in scenario 1 which uses SMOTE, which produces 97% accuracy, 100% precision, 94% recall and the last is F1-Score which produces 97%.
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