TY - GEN
T1 - Diphtheria Case Number Forecasting using Radial Basis Function Neural Network
AU - Anggraeni, Wiwik
AU - Nandika, DIna
AU - Mahananto, Faizal
AU - Sudiarti, Yeyen
AU - Fadhilla, Cut Alna
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - In Indonesia, diphtheria ranks fourth as a deadly disease after cardiovascular, tuberculosis, and pneumonia. The death rate of diphtheria is estimated to 21% with symptoms of malaise, anorexia, sore throat, and increased body temperature. The diphtheria cases which was reported in 2014 showed that East Java occupied the highest number for diphtheria cases which reached until 295, contributed to 74% cases of 22 provinces in Indonesia. In the mid-2017 until mid-2018, the Ministry of Health of the Republic of Indonesia announced that there has been an ongoing diphtheria outbreak in Indonesia. The number of diphtheria cases in East Java were highly raising up at the end of 2018. Forecasting is needed to reduce the number of diphtheria cases. The method used for forecasting is the Radial Basis Function Neural Network. Several variables are involved, including Immunization Coverage, Population Density, and Number of Cases. It is observed from the experimental results that the best model indicates only one variable involved, which is Number of Cases. This model is used to forecast the number of cases of diphtheria in Malang Regency, Surabaya City, and Sumenep Regency. The results showed that RBFNN method has a good performance for forecasting in Malang with MASE value of 0.84, Surabaya with MASE value of 0.817, and Sumenep with MASE value of 0.820, which all MASE values are less than 1.
AB - In Indonesia, diphtheria ranks fourth as a deadly disease after cardiovascular, tuberculosis, and pneumonia. The death rate of diphtheria is estimated to 21% with symptoms of malaise, anorexia, sore throat, and increased body temperature. The diphtheria cases which was reported in 2014 showed that East Java occupied the highest number for diphtheria cases which reached until 295, contributed to 74% cases of 22 provinces in Indonesia. In the mid-2017 until mid-2018, the Ministry of Health of the Republic of Indonesia announced that there has been an ongoing diphtheria outbreak in Indonesia. The number of diphtheria cases in East Java were highly raising up at the end of 2018. Forecasting is needed to reduce the number of diphtheria cases. The method used for forecasting is the Radial Basis Function Neural Network. Several variables are involved, including Immunization Coverage, Population Density, and Number of Cases. It is observed from the experimental results that the best model indicates only one variable involved, which is Number of Cases. This model is used to forecast the number of cases of diphtheria in Malang Regency, Surabaya City, and Sumenep Regency. The results showed that RBFNN method has a good performance for forecasting in Malang with MASE value of 0.84, Surabaya with MASE value of 0.817, and Sumenep with MASE value of 0.820, which all MASE values are less than 1.
KW - Diphtheria
KW - Forecasting
KW - Neural Network
KW - Radial Basis Function Neural Network
UR - http://www.scopus.com/inward/record.url?scp=85081106838&partnerID=8YFLogxK
U2 - 10.1109/ICICoS48119.2019.8982403
DO - 10.1109/ICICoS48119.2019.8982403
M3 - Conference contribution
AN - SCOPUS:85081106838
T3 - ICICOS 2019 - 3rd International Conference on Informatics and Computational Sciences: Accelerating Informatics and Computational Research for Smarter Society in The Era of Industry 4.0, Proceedings
BT - ICICOS 2019 - 3rd International Conference on Informatics and Computational Sciences
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Informatics and Computational Sciences, ICICOS 2019
Y2 - 29 October 2019 through 30 October 2019
ER -
