TY - GEN
T1 - Prediction of Dengue Fever Outbreak Based on Climate Factors Using Fuzzy-Logistic Regression
AU - Anggraeni, Wiwik
AU - Sumpeno, Surya
AU - Yuniarno, Eko Mulyanto
AU - Rachmadi, Reza Fuad
AU - Gumelar, Agustinus Bimo
AU - Purnomo, Mauridhi H.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - Dengue fever outbreak prediction is said to be one way that can be used to restrain the spread of dengue fever. Thus, the accuracy of the outbreak prediction system becomes essential. Furthermore, the factors involved in the prediction are also crucial to note. This study combines temperature, rainfall, humidity, wind speed, and the number of dengue cases to predict the outbreak of dengue fever. The fuzzy-logistic regression model is used based on its compatibility with the input and output characteristics. The result shows that the fuzzy-logistic regression model can produce outbreak predictions for validation data in other regions with an average performance of 79.93%. This average performance is 14.95% higher than the average accuracy of the Neural Network, Random Forest, and Naive Bayes approaches. The prediction results for the next 24 periods show that the outbreak will occur seven times. Dengue fever case and temperature are two variables that have more influence than other variables.
AB - Dengue fever outbreak prediction is said to be one way that can be used to restrain the spread of dengue fever. Thus, the accuracy of the outbreak prediction system becomes essential. Furthermore, the factors involved in the prediction are also crucial to note. This study combines temperature, rainfall, humidity, wind speed, and the number of dengue cases to predict the outbreak of dengue fever. The fuzzy-logistic regression model is used based on its compatibility with the input and output characteristics. The result shows that the fuzzy-logistic regression model can produce outbreak predictions for validation data in other regions with an average performance of 79.93%. This average performance is 14.95% higher than the average accuracy of the Neural Network, Random Forest, and Naive Bayes approaches. The prediction results for the next 24 periods show that the outbreak will occur seven times. Dengue fever case and temperature are two variables that have more influence than other variables.
KW - climate factor
KW - dengue fever
KW - fuzzy
KW - logistic regression
KW - outbreak
KW - prediction
UR - http://www.scopus.com/inward/record.url?scp=85091709223&partnerID=8YFLogxK
U2 - 10.1109/ISITIA49792.2020.9163708
DO - 10.1109/ISITIA49792.2020.9163708
M3 - Conference contribution
AN - SCOPUS:85091709223
T3 - Proceedings - 2020 International Seminar on Intelligent Technology and Its Application: Humanification of Reliable Intelligent Systems, ISITIA 2020
SP - 199
EP - 204
BT - Proceedings - 2020 International Seminar on Intelligent Technology and Its Application
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Seminar on Intelligent Technology and Its Application, ISITIA 2020
Y2 - 22 July 2020 through 23 July 2020
ER -
