Predicting Poverty Percentage Based on Satellite Imagery and Point of Interest Using Support Vector Regression and Random Forest Regression (Case Study of Central Java Province)-Reference-Cited by-同舟云学术

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Predicting Poverty Percentage Based on Satellite Imagery and Point of Interest Using Support Vector Regression and Random Forest Regression (Case Study of Central Java Province)

Published:2024 Issue: Volume: Page:309-323
ISSN:2194-1009
Container-title:Springer Proceedings in Mathematics & Statistics
language:en
Short-container-title:

Author:

Putra I Komang Pande Prajadhita Wibawa,Irhamah ,Iriawan Nur,Fithriasari Kartika

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-97-2136-8_23

Reference18 articles.

1. BPS: Profil Kemiskinan di Indonesia September 2021. BPS, Jakarta (2021)

2. United Nations: United Nations about the Sustainable Development Goals, 25 September 2015. https://www.un.org/sustainabledevelopment/poverty/. Last accessed 15 Feb 2022

3. BPS: Statistik Indonesia 2022. BPS, Jakarta (2022)

4. Romzi, M., Pramana, S.: Pemodelan CItra Malam Untuk Estimasi Kemiskinan Desa. BPS, Jakarta (2022)

5. Zhao, X., Yu, B., Liu, Y., Chen, Z., Li, Q., Wang, C., Wu, J.: Estimation of poverty using random forest regression with multi-source data: a case study in Bangladesh. MDPI, 1–14 (2019)

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