Prediction of Dengue Incidence in DKI Jakarta Using Adaptive Neuro-Fuzzy Inference System

被引：1

作者：

Hasanah, Hajratul ^{[1
]}

Hertono, Gatot Fatwanto ^{[1
]}

Sarwinda, Devvi ^{[1
]}

机构：

[1] Univ Indonesia, Fac Math & Nat Sci FMIPA, Dept Math, Depok 16424, Indonesia

来源：

INTERNATIONAL CONFERENCE ON SCIENCE AND APPLIED SCIENCE (ICSAS2020) | 2020年 / 2296卷

关键词：

C-MEANS;

D O I：

10.1063/5.0030455

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Dengue Hemorrhagic Fever (DHF) is one of the vines 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.

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