Journal of Mathematics and Statistics


Rokhana Dwi Bekti, Andiyono and Edy Irwansyah

DOI : 10.3844/jmssp.2014.130.138

Journal of Mathematics and Statistics

Volume 10, Issue 2

Pages 130-138


Geographically Weighted Regression (GWR) is a technique that brings the framework of a simple regression model into a weighted regression model. Each parameter in this model is calculated at each point geographical location. The significantly parameter can be used for mapping. In this research GWR model use for mapping Information and Communication Technology (ICT) indicators which influence on illiteracy. This problem was solved by estimation GWR model. The process was developing optimum bandwidth, weighted by kernel bisquare and parameter estimation. Mapping of ICT indicators was done by P-value. This research use data 29 regencies and 9 cities in East Java Province, Indonesia. GWR model compute the variables that significantly affect on illiteracy (α = 5%) in some locations, such as percent households members with a mobile phone (x2), percent of household members who have computer (x3) and the percent of households who access the internet at school in the last month (x4). Ownership of mobile phone was significant (α = 5%) at 20 locations. Ownership of computer and internet access were significant at 3 locations. Coefficient determination at all locations has R2 between 73.05-92.75%. The factors which affecting illiteracy in each location was very diverse. Mapping by P-value or critical area shows that ownership of mobile phone significantly affected at southern part of East Java. Then, the ownership of computer and internet access were significantly affected on illiteracy at northern area. All the coefficient regression in these locations was negative. It performs that if the number of mobile phone ownership, computer ownership and internet access were high then illiteracy will be decrease.


© 2014 Rokhana Dwi Bekti, Andiyono and Edy Irwansyah. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.