Prediction of dengue incidence in DKI Jakarta using adaptive neuro-fuzzy inference system

Hajratul Hasanah, Gatot Fatwanto Hertono, Dewi Sarwinda

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Dengue Hemorrhagic Fever (DHF) is one of the virus diseases transmitted by mosquitoes that has spread rapidly in recent years. Based on data, for the last 3 years, 2019 have the highest number of dengue cases, reaching a total of 813 cases in DKI Jakarta. To overcome the widespread DHF, a method is needed to predict the incidence of DHF in DKI Jakarta. In this study, we present an Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the number of dengue incidence. ANFIS is a Multi-Layer Feed-Forward network using learning algorithms of neural networks and fuzzy logic. The number of dengue incidence data obtained from the DKI Jakarta Health Services website. We used the data from 2009 to 2017 with several clustering methods to find the parameter input in ANFIS. Fuzzy C-Means, Grid Partition, and Subtractive Clustering are chosen as the clustering method. Simulation results show that the ANFIS method is best used to predict the incidence of dengue with the best MSE testing results of 0.000731784 with a correlation value of 0.99104.

Original languageEnglish
Title of host publicationInternational Conference on Science and Applied Science, ICSAS 2020
EditorsBudi Purnama, Dewanta Arya Nugraha, Fuad Anwar
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735440302
DOIs
Publication statusPublished - 16 Nov 2020
Event2020 International Conference on Science and Applied Science, ICSAS 2020 - Surakarta, Indonesia
Duration: 7 Jul 2020 → …

Publication series

NameAIP Conference Proceedings
Volume2296
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference2020 International Conference on Science and Applied Science, ICSAS 2020
Country/TerritoryIndonesia
CitySurakarta
Period7/07/20 → …

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