Open AccessDengue prediction model: A systematic review using social network analysis
Author(s) -
Sri Ngudi Wahyuni,
Eko Sediyono,
Irwan Sembiring
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1844/1/012012
Subject(s) - dengue fever , modularity (biology) , computer science , population , cluster (spacecraft) , data science , geography , environmental health , medicine , virology , genetics , biology , programming language
Since 1968, Dengue Harmonic Fever’s incidence in Indonesia has continued to rise and has become a public health issue. Indonesia has the largest number of Dengue Harmonic Fever cases than 30 other epidemic countries worldwide. It is very important to carry out research related to dengue cases’ prediction to prevent the spread of Dengue. This literature review is intended to determine the extent of the dengue prediction approach carried out by previous researchers, and a research gap will be obtained. The algorithm used to cluster articles is a modularity algorithm, using several open-source tools to process data. The online databases used are Google Scholar and Crossref by using keywords: journal, algorithm, prediction, and Dengue. The data are taken from the expansion of 1928-2020. This study’s results are 200 articles that are suitable and divided into four clusters of important articles. Also, several important parameters were obtained in the prediction study of dengue fever, namely humidity, temperature, rainfall, and population density.