TY - JOUR
T1 - The comparison between extreme learning machine and artificial neural network-back propagation for predicting the dengue incidences number in DKI Jakarta
AU - Tiffany, S.
AU - Sarwinda, D.
AU - Handari, B. D.
AU - Hertono, G. F.
N1 - Funding Information:
This research is supported by PUTI 2020 Research Grant No: NKB-1011/UN2.RST/HKP.05.00/2020 from Universitas Indonesia.
Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2021/3/29
Y1 - 2021/3/29
N2 - The existence of COVID-19 in Indonesia is not the only disease which we must be aware of. The Health Ministry has said that Dengue Hemorrhagic Fever is as dangerous as COVID-19 and must also be treated with caution. Based on data, until July 2020, there are 71,633 dengue cases in Indonesia and DKI Jakarta has the sixth-highest dengue incidence number. One of the factors that affects the spread of dengue vector is weather. It is necessary to predict the number of dengue incidences so that the dengue handling and prevention efforts can be done optimally. In this study, the number of dengue incidences will be predicted by involving weather factors (rainfall, temperature, and humidity) using Extreme Learning Machine and Artificial Neural Network-Back Propagation and also comparing the both of their performance. The result shows that Extreme Learning Machine can give the dengue incidence prediction in DKI Jakarta with the best RMSE testing result of 0.04584, which is more accurate than the dengue incidence prediction that is given by using Artificial Neural Network-Back Propagation with 100 epochs. Moreover, Extreme Learning Machine can do the training process faster than Artificial Neural Network-Back Propagation.
AB - The existence of COVID-19 in Indonesia is not the only disease which we must be aware of. The Health Ministry has said that Dengue Hemorrhagic Fever is as dangerous as COVID-19 and must also be treated with caution. Based on data, until July 2020, there are 71,633 dengue cases in Indonesia and DKI Jakarta has the sixth-highest dengue incidence number. One of the factors that affects the spread of dengue vector is weather. It is necessary to predict the number of dengue incidences so that the dengue handling and prevention efforts can be done optimally. In this study, the number of dengue incidences will be predicted by involving weather factors (rainfall, temperature, and humidity) using Extreme Learning Machine and Artificial Neural Network-Back Propagation and also comparing the both of their performance. The result shows that Extreme Learning Machine can give the dengue incidence prediction in DKI Jakarta with the best RMSE testing result of 0.04584, which is more accurate than the dengue incidence prediction that is given by using Artificial Neural Network-Back Propagation with 100 epochs. Moreover, Extreme Learning Machine can do the training process faster than Artificial Neural Network-Back Propagation.
UR - http://www.scopus.com/inward/record.url?scp=85103880372&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1821/1/012025
DO - 10.1088/1742-6596/1821/1/012025
M3 - Conference article
AN - SCOPUS:85103880372
SN - 1742-6588
VL - 1821
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012025
T2 - 6th International Conference on Mathematics: Pure, Applied and Computation, ICOMPAC 2020
Y2 - 24 October 2020
ER -