{"title":"基于Elman Levenberg神经网络和遗传算法的登革出血热暴发预测","authors":"N. Saptarini, Rocky Yefrenes Dillak, P. Pakan","doi":"10.1109/EIConCIT.2018.8878529","DOIUrl":null,"url":null,"abstract":"Dengue Hemorrhagic Fever (DHF) is a very dangerous disease and has threatened human survival. This disease usually spreads easily and causes of death in children especially those younger than 15 years. The purpose of this research is to develop a system that can be used to predict the number of DHF cases in the city of Kupang, Indonesia accurately using improved Elman Levenberg-Marquardt Neural Network algorithm. This study used four (4) input variables, which are the factors that contribute to the spread of DHF, namely: (i) The average temperature, (ii) The average of rainfall, (iii) The humidity and (iv) The sea level. Based on test results, the model can predict DHF cases with RMSE of 1,384.","PeriodicalId":424909,"journal":{"name":"2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Dengue Haemorrhagic Fever Outbreak Prediction using Elman Levenberg Neural Network and Genetic Algorithm\",\"authors\":\"N. Saptarini, Rocky Yefrenes Dillak, P. Pakan\",\"doi\":\"10.1109/EIConCIT.2018.8878529\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Dengue Hemorrhagic Fever (DHF) is a very dangerous disease and has threatened human survival. This disease usually spreads easily and causes of death in children especially those younger than 15 years. The purpose of this research is to develop a system that can be used to predict the number of DHF cases in the city of Kupang, Indonesia accurately using improved Elman Levenberg-Marquardt Neural Network algorithm. This study used four (4) input variables, which are the factors that contribute to the spread of DHF, namely: (i) The average temperature, (ii) The average of rainfall, (iii) The humidity and (iv) The sea level. Based on test results, the model can predict DHF cases with RMSE of 1,384.\",\"PeriodicalId\":424909,\"journal\":{\"name\":\"2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT)\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EIConCIT.2018.8878529\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EIConCIT.2018.8878529","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Dengue Haemorrhagic Fever Outbreak Prediction using Elman Levenberg Neural Network and Genetic Algorithm
Dengue Hemorrhagic Fever (DHF) is a very dangerous disease and has threatened human survival. This disease usually spreads easily and causes of death in children especially those younger than 15 years. The purpose of this research is to develop a system that can be used to predict the number of DHF cases in the city of Kupang, Indonesia accurately using improved Elman Levenberg-Marquardt Neural Network algorithm. This study used four (4) input variables, which are the factors that contribute to the spread of DHF, namely: (i) The average temperature, (ii) The average of rainfall, (iii) The humidity and (iv) The sea level. Based on test results, the model can predict DHF cases with RMSE of 1,384.