Performance Analysis of the Hybrid Voting Method on the Classification of the Number of Cases of Dengue Fever

arief rahman; Sri Suryani Prasetiyowati

doi:10.21108/ijoict.v8i1.614

arief rahman Telkom University
sri suryani prasetiyowati Telkom university

https://doi.org/10.21108/ijoict.v8i1.614

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Keywords: Classification, Support Vector Machine, K-Nearest Neighbor, Decision Tree, Hybrid Classifier

Abstract

Dengue hemorrhagic fever (DHF) is a health problem in Indonesia. The region in Indonesia that has the highest number of cases in West Java with the highest ranking with 10,772 cases. The city of Bandung is recorded to have the highest number of cases at this time, namely 4,424 cases. Dengue fever can be caused by high rainfall. Judging from the high number of cases and fluctuations that occur, it is necessary to predict the spread of the disease so that in the future it can be anticipated by the government. Prediction of the spread of dengue fever in the city of Bandung using various classification algorithms has been done. Therefore, the author wants to make a new breakthrough by using hybrid ensemble learning using a hard voting method from three classification methods, namely Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (DT). Using the Bandung City DHF disease dataset from 2012 to 2018. The results obtained using the Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Decision Tree (DT) were 84%, 87%, 79%. to improve the classification accuracy of the three methods using a hybrid classification with the hard voting method to get 91% results.

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