Hybrid Weighted K-Nearest Neighbor dan Naive Bayes untuk Klasifikasi Diabetes Melitus
-
Aklima Laduna Ramadya
*
STIKOM Tunas Bangsa, Pematang Siantar,
Indonesia
- Solikhun Solikhun STIKOM Tunas Bangsa, Pematang Siantar, Indonesia
- Timbo Faritcan P. Siallagan Universitas Mandiri, Jawa Barat, Indonesia (*) Corresponding Author
Abstract
Abstract−Diabetes mellitus is a chronic metabolic disease with a continuously increasing prevalence and the potential to cause various serious complications if not detected early. The utilization of machine learning has become one of the effective approaches to support a faster and more accurate diagnostic process. This study proposes a Hybrid Weighted K-Nearest Neighbor (WKNN)–Naive Bayes model that combines a distance-based approach with a probabilistic approach for the classification of diabetes mellitus. The dataset used is the Pima Indians Diabetes Dataset, consisting of 768 patient records with eight predictor attributes and one target variable. The preprocessing stage included missing value handling using imputation, data normalization using RobustScaler, feature engineering, and class balancing using the Synthetic Minority Over-sampling Technique (SMOTE). Model evaluation was performed using Stratified 10-Fold Cross Validation and compared against K-Nearest Neighbor (KNN), Weighted K-Nearest Neighbor (WKNN), and Naive Bayes models. The results show that the Hybrid WKNN–Naive Bayes model achieved an Accuracy of 88.50%, Precision of 85.32%, Recall of 93.00%, F1-Score of 88.99%, and AUC-ROC of 91.09%, demonstrating good classification performance for diabetes mellitus diagnosis. The Weighted K-Nearest Neighbor (WKNN) model also achieved an Accuracy of 88.50%, with a Recall of 94.00%, F1-Score of 89.10%, and AUC-ROC of 91.73%, while the Naive Bayes model produced an Accuracy of 71.00%. These results indicate that the Hybrid WKNN–Naive Bayes approach is able to deliver competitive classification performance through the combination of distance-based and probabilistic methods, making it a promising candidate for use as a decision support system in the early detection of diabetes mellitus.
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Copyright (c) 2026 Aklima Laduna Ramadya, Solikhun Solikhun, Timbo Faritcan P. Siallagan
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