Modeling of Poverty Rate in Indonesian Using Geographically Weighted Logistic Regression for Supporting the Sustainable Development Goals Program in 2030

Lailatus Syarifah, Putri Andriani, Nadhiyati Rizka, Retno Dwi Puspitasari, Nur Chamidah

2018

Abstract

Sustainable Development Goals (SDGs) is a world development agenda drafted by the United Nations containing 17 goals with 169 targets and 241 indicators. The objectives of SDGs are economic growth, social inclusion, and environmental protection. One of the goals of SDGs is to alleviate poverty. Poverty denotes the limited ability to meet the needs of decent living such as limitations in income, skills, health, economic assets control, or access to information. Indonesia is ranked 9th on the list of countries with the largest number of poor people in the world. Poverty rates vary greatly between one region and the next, which can be caused by the diversity of characteristics among the regions. The Indonesia government’s target is that the poverty rate in Indonesia must fall below 10%. The poverty rates can be categorized and analysed using the geographically weighted logistic regression (GWLR) model approach. The study found that the province with the highest poverty percentage is Papua where the significant variables are literacy rate, percentage of households with proper sanitation, household slum percentage, percentage of households occupying habitable homes, and percentage of malnutrition.

Paper Citation

in Harvard Style

Syarifah L., Andriani P., Rizka N., Puspitasari R. and Chamidah N. (2018). Modeling of Poverty Rate in Indonesian Using Geographically Weighted Logistic Regression for Supporting the Sustainable Development Goals Program in 2030.In Proceedings of the 2nd International Conference Postgraduate School - Volume 1: ICPS, ISBN 978-989-758-348-3, pages 935-938. DOI: 10.5220/0007554309350938

in Bibtex Style

@conference{icps18,
author={Lailatus Syarifah and Putri Andriani and Nadhiyati Rizka and Retno Dwi Puspitasari and Nur Chamidah},
title={Modeling of Poverty Rate in Indonesian Using Geographically Weighted Logistic Regression for Supporting the Sustainable Development Goals Program in 2030},
booktitle={Proceedings of the 2nd International Conference Postgraduate School - Volume 1: ICPS,},
year={2018},
pages={935-938},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007554309350938},
isbn={978-989-758-348-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference Postgraduate School - Volume 1: ICPS,
TI - Modeling of Poverty Rate in Indonesian Using Geographically Weighted Logistic Regression for Supporting the Sustainable Development Goals Program in 2030
SN - 978-989-758-348-3
AU - Syarifah L.
AU - Andriani P.
AU - Rizka N.
AU - Puspitasari R.
AU - Chamidah N.
PY - 2018
SP - 935
EP - 938
DO - 10.5220/0007554309350938