Prediksi Diabetes Berbasis Decision Tree Dengan Menggunakan Dataset Pima Indians Diabetes - Yustri Insani; Marcel Filemon Naibaho; Sardo Pardingotan Sipayung (Yayasan Pendidikan Penelitian Pengabdian Algero, 2026)
Diabetes mellitus is a chronic disease characterized by increased blood glucose levels and can lead to various serious complications if not treated early. This research aims to predict diabetes using the Decision Tree algorithm with the Pima Indians Diabetes dataset. The research stages include data processing, forming a Decision Tree model using the entropy criterion, and evaluating model performance. The results show that the model achieved an accuracy of 76.62%. Testing through a confusion matrix produced 83 True Negative samples, 35 True Positive samples, 16 False Positive samples, and 20 False Negative samples. The Glucose attribute was found to be the most dominant factor in the diagnosis, followed by BMI and Age. The resulting model is able to form clear and easy-to-understand decision rules so that it can be used as a decision support system in the early diagnosis of diabetes.