Klasifikasi Penyakit Demam Berdarah Menggunakan Algoritma Stacking Ensemble Learning
DOI:
https://doi.org/10.31598/sintechjournal.v8i2.1854Keywords:
dengue hemorrhagic fever, Classification, machine learning, stacking ensemble learning, confusion matrixAbstract
Dengue Hemorrhagic Fever (DHF) is a contagious disease that remains a serious public health issue in Indonesia, with a consistent increase in cases each year. One of the main challenges is achieving accurate early diagnosis, as the initial symptoms often resemble other diseases such as influenza or chikungunya. This study aims to develop a DHF classification model using the stacking ensemble learning method to improve diagnostic accuracy compared to single classifier methods. The dataset consists of 650 patient medical records from Puskesmas I Mengwi District, including demographic data and clinical features such as rash, pain, body temperature, bleeding, and laboratory results. The research process involves data preprocessing, the implementation of three base learners (K-Nearest Neighbor, Naïve Bayes, and Decision Tree), and Logistic Regression as the meta-learner. Evaluation using k-fold cross-validation (5-fold and 10-fold) shows that the stacking ensemble achieves the highest accuracy of 84.15%, with a precision of 85.28%, recall of 92.62%, and F1-Score of 88.70%. These results demonstrate that stacking provides better and more stable performance than single classifiers. The proposed model has the potential to support early DHF diagnosis in healthcare facilities, helping medical personnel improve diagnostic accuracy and treatment effectiveness
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