WASTE GENERATION MODELING BASED ON SOCIOECONOMIC AND SOCIODEMOGRAPHIC FACTORS IN WEST JAVA USING GEOGRAPHICALLY WEIGHTED REGRESSION

Sri Pingit Wulandari(1*), Sri Mumpuni Retnaningsih(2), Nimas Ayu Prabawani(3), Apsarini Pradipta(4)


(1) Department of Business Statistics, Institut Teknologi Sepuluh Nopember Surabaya
(2) Department of Business Statistics, Institut Teknologi Sepuluh Nopember Surabaya
(3) Department of Business Statistics, Institut Teknologi Sepuluh Nopember Surabaya
(4) Department of Business Statistics, Institut Teknologi Sepuluh Nopember Surabaya
(*) Corresponding Author

Abstract


Waste has become a national concern, reflecting the increasing consumption patterns within society. The rise in consumption contributes to the growing volume and diversity of waste generated. This issue is also aligned with Sustainable Development Goals (SDGs) Goal 12, which emphasises responsible consumption and production. West Java Province is one of the provinces with the highest total waste generation in Indonesia. Several factors likely influence the annual increase in waste generation in West Java. Therefore, this study aims to model the factors affecting waste generation in West Java Province by incorporating spatial aspects using the Geographically Weighted Regression (GWR) method. Based on the analysis, the GWR model was applied using an adaptive bisquare kernel function, achieving a model fit of 96.65%. The factors found to have a significant influence on waste generation in West Java Province include life expectancy of schooling (HLS), the percentage of the population living in poverty, and the Gross Regional Domestic Product (GRDP) at constant prices.

Keywords


Geographically Weighted Regression; West Java Province; SDGs Goal 12; Waste Generation

Full Text:

PDF

References


A. A. Anas, “Mendukung Sustainable Development Goals (SDGs) Melalui Pengelolaan Sampah Yang Tepat.” Accessed: May 18, 2025. [Online]. Available: https://menpan.go.id/site/berita-terkini/mendukung-sustainability-development-goals-sdgs-melalui-pengelolaan-sampah-yang-tepat

S. I. P. S. N. SIPSN, “Timbulan Sampah.” Accessed: May 18, 2025. [Online]. Available: https://sipsn.kemenlh.go.id/sipsn/public/data/timbulan

G. Prajati, T. Padmi, and B. Rahardyan, “Pengaruh Faktor-Faktor Ekonomi Dan Kependudukan Terhadap Timbulan Sampah Di Ibu Kota Provinsi Jawa Dan Sumatera the Influence of Economic and Demographic Factors To Waste Generation in Capital City of Java and Sumatera,” J. Tek. Lingkung., vol. 21, pp. 39–47, 2015.

G. Prajati and A. J. Pesurnay, “the Analyze of Sociodemographic and Socioeconomic Factors To Municipal Solid Waste Generated in Sumatera Island,” J. Rekayasa Sipil dan Lingkung., vol. 3, no. 1, p. 8, 2019, doi: 10.19184/jrsl.v3i1.8721.

Amrin, “Data Mining Dengan Regresi Linier Berganda Untuk Peramalan Data Mining Dengan Regresi Linier Berganda Untuk,” J. Techno Nusa Mandiri, vol. XIII, no. March 2016, pp. 74–79, 2018.

R. E. Walpole, R. H. Myers, S. L. Myers, and K. Ye, Probability & Statistics for Engineers & Scientists NINTH EDITION, vol. 11, no. 1. 2019. [Online]. Available: http://scioteca.caf.com/bitstream/handle/123456789/1091/RED2017-Eng-8ene.pdf?sequence=12&isAllowed=y%0Ahttp://dx.doi.org/10.1016/j.regsciurbeco.2008.06.005%0Ahttps://www.researchgate.net/publication/305320484_SISTEM_PEMBETUNGAN_TERPUSAT_STRATEGI_MELESTARI

R. Kurniawan, Analisis Regresi : Dasar dan Penerapannya dengan R, 1st ed. Jakarta: PT Kharisma Putra Utama, 2016.

N. Astriawati, “Penerapan Analisis Regresi Linier Berganda Untuk Menentukan Pengaruh Pelayanan Pendidikan Terhadap Efektifitas Belajar Taruna Di Akademi Maritim Yogyakarta,” J. Ilmu-Ilmu Kemaritiman, Manaj. dan Transp., vol. 14, no. 23, pp. 22–37, 2016, [Online]. Available: http://jurnal.stimaryo.ac.id/index.php/MIBJ/article/view/90

N. Ratih, Iis D; Haryanto, Albertus E P; Wulandari, Sri P; Santoso, Metode Regresi : Teori dan Aplikasi Menggunakan SPSS. Surabaya: ITS Press, 2025.

S. Haryanto and G. A. Andriani, “Pemodelan Jumlah Penduduk Miskin Di Jawa Tengah Menggunakan Geographically Weighted Regression (Gwr),” J. Litbang Sukowati Media Penelit. dan Pengemb., vol. 4, no. 2, p. 10, 2019, doi: 10.32630/sukowati.v4i2.122.

E. Amalia and L. K. Sari, “Analisis Spasial Untuk Mengidentifikasi Tingkat Pengangguran Terbuka Berdasarkan Kabupaten/Kota Di Pulau Jawa Tahun 2017,” Indones. J. Stat. Its Appl., vol. 3, no. 3, pp. 202–215, 2019, doi: 10.29244/ijsa.v3i3.240.

B. Warf, “Geographically Weighted Regression,” Encycl. Geogr., pp. 1226–1232, 2010.

A. K. Lumaela, B. W. Otok, and S. Sutikno, “Pemodelan Chemical Oxygen Demand (Cod) Sungai di Surabaya Dengan Metode Mixed Geographically Weighted Regression,” J. Sains dan Seni ITS, vol. 2, no. 1, pp. 100–105, 2013.

P. Astuti, N. N. Debataraja, and E. Sulistianingsih, “Analisis Kemiskinan dengan Pemodelan Geographically Weighted Regression (GWR) di Provinsi Nusa Tenggara Timur,” Bul. Ilm. Mat. Stat. dan Ter., vol. 7, no. 3, pp. 169–176, 2018.


Article Metrics

Abstract view : 14 times
PDF - 7 times

DOI: https://doi.org/10.26714/jsunimus.13.2.2025.110-122

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Jurnal Statistika Universitas Muhammadiyah Semarang

Editorial Office:
Department of Statistics
Faculty Of Mathematics And Natural Sciences 
Universitas Muhammadiyah Semarang

Jl. Kedungmundu No. 18 Semarang Indonesia



Published by: 
Department of Statistics Universitas Muhammadiyah Semarang

View My Stats

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License