Perbandingan Kinerja Algoritma Decision Tree dan Naive Bayes dalam Klasifikasi Risiko Diabetes Menggunakan Dataset Pima Indians Diabetes Berdasarkan Nilai Akurasi dan Confusion Matrix

Destry Widiani Nurul Ummi (Universitas Bina Darma)
Alex Wijaya (Universitas Bina Darma)
Rezania Agramanisti Azdy (Universitas Bina Darma)

Journal Digital Technology Trend
Vol. 5 No. 2 (2026): December

This study compares the performance of Decision Tree and Naive Bayes algorithms in classifying diabetes mellitus risk using the Pima Indians Diabetes Dataset (768 female patient medical records, 8 predictor attributes) without additional optimization techniques. The methodology includes data preprocessing, data splitting (80% training and 20% testing ratio), model construction, and evaluation based on accuracy, confusion matrix, and classification report, reinforced by Stratified 5-Fold Cross Validation. Hold-Out Validation results show that Decision Tree achieved an accuracy of 83.77%, while Naive Bayes achieved 76.62%. In Stratified 5-Fold Cross Validation, Decision Tree recorded an average accuracy of 85.56% (std: 0.0339), whereas Naive Bayes achieved 75.77% (std: 0.0492). Confusion matrix and classification report evaluations further confirm the superiority of Decision Tree across precision, recall, and f1-score metrics. In conclusion, the Decision Tree algorithm delivers superior and more stable performance compared to Naive Bayes in diabetes risk classification.