eprintid: 43780
rev_number: 10
eprint_status: archive
userid: 12259
dir: disk0/00/04/37/80
datestamp: 2021-09-03 12:11:04
lastmod: 2021-09-03 12:11:04
status_changed: 2021-09-03 12:11:04
type: thesis
metadata_visibility: show
creators_name: NOVIA AMILATUS SOLEKHAH, NIM. 17106010027
title: MODEL GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION PADA FUNGSI PEMBOBOT ADAPTIVE GAUSSIAN KERNEL
ispublished: pub
subjects: Matematika
divisions: jur_mat
full_text_status: restricted
keywords: Adaptive Gaussian Kernel, AIC, CV, GWLR, Poverty, MLE.
note: Mohammad Farhan Qudratullah, S.Si., M.Si
abstract: The Geographically Weighted Logistic Regression (GWLR) model is a logistic regression model development that is applied to spatial data from non-stationary processes. This model is used to predict a model of the data set that has a binary response variable which takes into account the spatial factor. This study will discuss the use of the GWLR model using the adaptive weighting function of the Gaussian kernel in a poverty case study in East Nusa Tenggara Province in 2019.
The parameter estimation of the Maximum Likelihood Estimation (MLE) method by giving different weights for each observation location. The weight used is the adaptive gaussian kernel with the optimum bandwidth selection using the Cross Validation (CV).
The results of the analysis of the GWLR model with adaptive gaussian kernel weighting are better because it has the smallest AIC value. Based on the results of testing the parameters of the GWLR model with a weighted adaptive gaussian kernel, it can be concluded that the factors that influence poverty are local and vary in the 22 observation locations, including GRDP per capita, acceptance of smart Indonesian programs, and projected population growth rates, with a classification accuracy rate of 81,82%.
date: 2021-05-11
date_type: published
pages: 195
institution: FAKULTAS SAINS DAN TEKNOLOGI
department: UIN SUNAN KALIJAGA YOGYAKARTA
thesis_type: skripsi
thesis_name: other
citation:   NOVIA AMILATUS SOLEKHAH, NIM. 17106010027  (2021) MODEL GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION PADA FUNGSI PEMBOBOT ADAPTIVE GAUSSIAN KERNEL.  Skripsi thesis, FAKULTAS SAINS DAN TEKNOLOGI.   
document_url: https://digilib.uin-suka.ac.id/id/eprint/43780/1/17106010027_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf
document_url: https://digilib.uin-suka.ac.id/id/eprint/43780/2/17106010027_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf