PENERAPAN ALGORITMA GLOWWORM SWARM OPTIMIZATION PADA MODEL GEOGRAPHICALLY WEIGHTED REGRESSION DENGAN KERNEL ADAPTIF

This study aimed to apply glowworm swarm optimization (GSO) algorithm as an alternate way to obtain optimal bandwidth in geographically weighted regression (GWR) model with adaptive kernel function. The result showed that GSO was able to obtain optimal bandwidth with lower cross validation (CV) value than the traditional way that was using k-nearest neighbor (KNN) algorithm. Unfortunately, the running time of GSO was far slower than KNN was.