Open AccessClassification of Dengue Hemorrhagic Fever (DHF) Spread in Bandung using Hybrid Naïve Bayes, K-Nearest Neighbor, and Artificial Neural Network Methods
Author(s) -
Fatri Nurul Inayah,
Sri Suryani Prasetiyowati,
Yuliant Sibaroni
Publication year - 2021
Publication title -
international journal on information and communication technology
Language(s) - English
Resource type - Journals
ISSN - 2356-5462
DOI - 10.21108/ijoict.v7i1.562
Subject(s) - dengue fever , dengue hemorrhagic fever , naive bayes classifier , artificial neural network , artificial intelligence , computer science , machine learning , k nearest neighbors algorithm , dengue virus , support vector machine , pattern recognition (psychology) , virology , medicine
Dengue fever is a dangerous disease caused by the dengue virus. One of the factors causing dengue fever is due to the place where you live in the tropics, so that cases of dengue fever in Indonesia, especially in the Bandung Regency area, will continue to show high numbers. Therefore, information is needed on the spread of this disease by requiring the accuracy and speed of diagnosis as early prevention. In terms of compiling this information, classification techniques can be done using a combination of methods Naïve Bayes, K-Nearest Neighbor(KNN), and Artificial Neural Network(ANN) to build predictions of the classification of dengue fever, and the data used in this Final Project are dataset affected by the spread of dengue fever in Bandung regency in the 2012-2018 period. The hybrid classifier results can improve accuracy with the voting method with an accuracy level of 90% in the classification of dengue fever.