TY - GEN
T1 - Comparing activation functions in predicting dengue hemorrhagic fever cases in DKI Jakarta using u
AU - Sukama, Yuda
AU - Hertono, Gatot Fatwanto
AU - Handari, Bevina Desjwiandra
AU - Aldila, Dipo
N1 - Funding Information:
We thank to reviewer for their constructive comments, to Jakarta Health Office for availability of the dengue incidence data. This research is supported by PUT? Proceeding 2020 Research Grant scheme from Universitas Indonesia (ID Number: NKB-1011/UN2.RST/HKP.05.00/2020)
Publisher Copyright:
© 2020 American Institute of Physics Inc.. All rights reserved.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/11/16
Y1 - 2020/11/16
N2 - Dengue hemorrhagic fever (DHF) is a disease caused by the dengue virus and spread by infected Aedes aegypti and A. albopictus mosquitoes. Various socio-economic and environmental factors make it difficult to predict DHF incidents. However, with machine learning, we can make more accurate predictions based on historic data. The spread of DHF in a given region can be predicted based on incident data. In this research, one means of machine learning, the Recurrent Neural Network (RNN), is used to predict DHF incidents in DKI Jakarta by using historic DHF case data from 2009 to 2017. RNN is a neural network with a recurrent hidden state which is activated using current data and previous data. RNNs are well-suited to predicting time-series data. In the implementation, we use three activation functions that is sigmoid, tanh, and ReLU to determine which one is the most accurate in predicting DHF incidents in Jakarta. The implementation results show that the sigmoid activation function can give better results on the RNN model compared to tanh and ReLU activation functions.
AB - Dengue hemorrhagic fever (DHF) is a disease caused by the dengue virus and spread by infected Aedes aegypti and A. albopictus mosquitoes. Various socio-economic and environmental factors make it difficult to predict DHF incidents. However, with machine learning, we can make more accurate predictions based on historic data. The spread of DHF in a given region can be predicted based on incident data. In this research, one means of machine learning, the Recurrent Neural Network (RNN), is used to predict DHF incidents in DKI Jakarta by using historic DHF case data from 2009 to 2017. RNN is a neural network with a recurrent hidden state which is activated using current data and previous data. RNNs are well-suited to predicting time-series data. In the implementation, we use three activation functions that is sigmoid, tanh, and ReLU to determine which one is the most accurate in predicting DHF incidents in Jakarta. The implementation results show that the sigmoid activation function can give better results on the RNN model compared to tanh and ReLU activation functions.
UR - http://www.scopus.com/inward/record.url?scp=85096650266&partnerID=8YFLogxK
U2 - 10.1063/5.0030456
DO - 10.1063/5.0030456
M3 - Conference contribution
AN - SCOPUS:85096650266
T3 - AIP Conference Proceedings
BT - International Conference on Science and Applied Science, ICSAS 2020
A2 - Purnama, Budi
A2 - Nugraha, Dewanta Arya
A2 - Anwar, Fuad
PB - American Institute of Physics Inc.
T2 - 2020 International Conference on Science and Applied Science, ICSAS 2020
Y2 - 7 July 2020
ER -
