TY - GEN
T1 - Multilevel Logistic Regression and Neural Network-Genetic Algorithm for Modeling Internet Access
AU - Wibowo, Wahyu
AU - Abdul-Rahman, Shuzlina
AU - Cahyani, Nita
N1 - Publisher Copyright:
© 2019, Springer Nature Singapore Pte Ltd.
PY - 2019
Y1 - 2019
N2 - Logistic regression is one of the classical methods for classification. Meanwhile, neural network is the recent method for classification. Both methods are widely used in the supervised learning and competing to be the best methods in many classifications research. This paper aims to study the performance of both methods using data of youth internet access of East Java Province of Indonesia. The first method used is Multilevel Logistic Regression, a hierarchical model which is part of Generalized Linear Mixed Model (GLMM) where the response variable is influenced by fixed and random factors. The second one is Neural Network-Genetic Algorithm in which the weight optimization is performed by selecting the relevant input variables, the optimal number of hidden nodes, and the optimal connection weights. The result shows that Multilevel Logistic Regression produced a slightly better accuracy rate of 0.873 compared to Genetic Neural Network Algorithm with an accuracy rate of 0.871.
AB - Logistic regression is one of the classical methods for classification. Meanwhile, neural network is the recent method for classification. Both methods are widely used in the supervised learning and competing to be the best methods in many classifications research. This paper aims to study the performance of both methods using data of youth internet access of East Java Province of Indonesia. The first method used is Multilevel Logistic Regression, a hierarchical model which is part of Generalized Linear Mixed Model (GLMM) where the response variable is influenced by fixed and random factors. The second one is Neural Network-Genetic Algorithm in which the weight optimization is performed by selecting the relevant input variables, the optimal number of hidden nodes, and the optimal connection weights. The result shows that Multilevel Logistic Regression produced a slightly better accuracy rate of 0.873 compared to Genetic Neural Network Algorithm with an accuracy rate of 0.871.
KW - Accuracy
KW - Multilevel Logistic Regression
KW - Neural Network Genetic Algorithm
UR - http://www.scopus.com/inward/record.url?scp=85076082067&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-0399-3_14
DO - 10.1007/978-981-15-0399-3_14
M3 - Conference contribution
AN - SCOPUS:85076082067
SN - 9789811503986
T3 - Communications in Computer and Information Science
SP - 169
EP - 180
BT - Soft Computing in Data Science - 5th International Conference, SCDS 2019, Proceedings
A2 - Berry, Michael W.
A2 - Yap, Bee Wah
A2 - Mohamed, Azlinah
A2 - Köppen, Mario
PB - Springer
T2 - 5th International Conference on Soft Computing in Data Science, SCDS 2019
Y2 - 28 August 2019 through 29 August 2019
ER -