The Use of A Geographically Weighted Regression Model to Analyze Predictors of The Rice Supply in Bojonegoro

Authors

  • Denny Nurdiansyah Universitas Nahdlatul Ulama Sunan Giri
  • Mochamad Nizar Palefi Ma'ady Institut Teknologi Telkom Surabaya
  • Alif Yuanita Kartini Universitas Nahdlatul Ulama Sunan Giri
  • Ummi Agustin Yuliana Universitas Nahdlatul Ulama Sunan Giri

DOI:

https://doi.org/10.30736/voj.v6i1.706

Keywords:

Rice Supply, Harvested Area, Rice Production, Population, GWR

Abstract

The research goal would be to understand all potential influences on the amount of rice available within every sub-district in the Bojonegoro district. Geographically weighted regression (GWR), a technique used for this study, uses kernels: adaptive bisquare, fixed bisquare, adaptive gaussian, and fixed gaussian. The state office for food security and farming inside the Bojonegoro district provided secondary statistics for the 2018 year that included information on the population, the harvested area, the rice production, and the rice supply. The outcomes from the kernel-fixed gaussian elected model using AIC minimum criteria for the GWR model. The implementation's conclusion is due to the impact of variety in locations. The next research recommendation is a time-series spatial study of the rice problem.

Downloads

Download data is not yet available.

Author Biography

Denny Nurdiansyah, Universitas Nahdlatul Ulama Sunan Giri

Program Studi Statistika

References

Aprilianti, V. A., & Harkeni, A. (2021). Pengaruh Indeks Pembangunan Manusia Terhadap Ketimpangan Wilayah Di Provinsi Jambi. Jurnal Khazanah Intelektual, 5(2), 1142–1160. https://doi.org/10.37250/newkiki.v5i2.111

Azies, H. Al. (2019). Analisis Pengaruh Fasilitas Kesehatan terhadap Kematian Bayi di Jawa Timur Menggunakan Pendekatan Geographically Weighted Regression. Jurnal Penelitian Dan Pengembangan Pelayanan Kesehatan, 3(2), 131–141. https://doi.org/10.22435/jpppk.v3i2.2431

Cahya, M. R., Wibowo, A. S., & Bukhari, A. (2018). Keberlanjutan Ketersediaan Beras Di Kabupaten Pandeglang Provinsi Banten. Jurnal Agribisnis Terpadu, 11(2), 181–195. https://doi.org/10.33512/jat.v11i2.5095

Cholid, F., Trishnanti, D., & Azies, H. Al. (2019). Pemetaan Faktor-Faktor yang Mempengaruhi Stunting pada Balita dengan Geographically Weighted Regression (GWR). Semnakes 2019, 156–165.

Darsan, & Dawud, M. Y. (2021). Strategi Pemasaran Beras Pada Agroindustri di Kabupaten Bojonegoro. Jurnal Ilmiah Ilmu-Ilmu Pertanian, 15(1), 65–71.

Ilyas, A., Noer, M., & Wahyuni, I. (2020). Analisis Faktor-Faktor Yang Memengaruhi Ketersediaan Beras Di Indonesia. Mimbar Agribisnis: Jurnal Pemikiran Masyarakat Ilmiah Berwawasan Agribisnis, 6(2), 740–753. https://doi.org/10.25157/ma.v6i2.3456

Li, S., Zhou, C., Wang, S., Gao, S., & Liu, Z. (2019). Spatial heterogeneity in the determinants of urban form: An analysis of Chinese cities with a GWR approach. Sustainability (Switzerland), 11(2), 479. https://doi.org/10.3390/su11020479

Lu, B., Brunsdon, C., Charlton, M., & Harris, P. (2017). Geographically weighted regression with parameter-specific distance metrics. International Journal of Geographical Information Science, 31(5), 982–998. https://doi.org/10.1080/13658816.2016.1263731

Nisa, K. (2022). Penerapan Model Geographically Weighted Poisson Regression untuk Demam Berdarah Dengue Di Kabupaten Bojonegoro. 1(1), 12–22.

Pemerintah. (2012). Undang - Undang Republik Indonesia Nomor 18 Tahun 2012 Tentang Pangan.

Pratama, A. R., Sudrajat, & Harini, R. (2019). Analisis Ketersediaan dan Kebutuhan Beras di Indonesia Tahun 2018. Media Komunikasi Geografi, 20(2), 101–114. https://doi.org/10.23887/mkg.v20i2.19256

Pratiwi, Y. D., Mariani, S., & Putriaji. (2019). Pemodelan Regresi Spasial Menggunakan Geographically Weighted Regression. Unnes Journal of Mathematics, 8(2), 32–41.

Pujiati, S., Pertiwi, A., Silfia, C. C., Ibrahim, D. M., & Nur Hafida, S. H. (2020). Analisis Ketersediaan, Keterjangkauan Dan Pemanfaatan Pangan Dalam Mendukung Tercapainya Ketahanan Pangan Masyarakat Di Provinsi Jawa Tengah. Jurnal Sosial Ekonomi Pertanian, 16(2). https://doi.org/10.20956/jsep.v16i2.10493

Putu, L., Hendayanti, N. P. N., & Suniantara, I. K. P. (2020). Perbandingan Pembobotan Seemingly Unrelated Regression – Spatial Durbin Model Untuk Faktor Kemiskinan Dan Pengangguran. Jurnal Varian, 3(2), 51–64. https://doi.org/10.30812/varian.v3i2.596

Supardi, S., Riptanti, E. W., & Qonita, A. (2020). Pemetaan Kondisi Kerawanan Pangan di Tingkat Wilayah di Kabupaten Bojonegoro (Food Insecurity Conditions Mapping in Bojonegoro Regency). Jurnal Ilmu-Ilmu Pertanian, 16(2).

Susanto, A., Hamzah, A., Irnawati, R., Nurdin, H. S., & Supadminingsih, N. (2020). Peran Sektor Perikanan Tangkap dalam Mendukung Ketahanan Pangan Perikanan di Provinsi Banten. 1, 9–17.

Tizona, A. R., Goejantoro, R., & Wasono. (2017). Pemodelan Geographically Weighted Regression (Gwr) Dengan Fungsi Pembobot Adaptive Kernel Bisquare Untuk Angka Kesakitan Demam Berdarah di Kalimantan Timur Tahun 2015. Jurnal Eksponensial, 8(1), 87–94.

Utami, N. P. M., Sumarjaya, I. W., & Srinadi, I. G. A. M. (2019). Memodelkan Rasio Ketersediaan Beras Menggunakan Regresi Data Panel Dinamis. E-Jurnal Matematika, 8(3), 199–203. https://doi.org/10.24843/mtk.2019.v08.i03.p253

Wijoyo, B. H. R., Hidayat, S. I., & Abidin, Z. (2020). Analisis Ketersediaan Beras Di Jawa Timur. Berkala Ilmiah AGRIDEVINA, 8(2), 83–98. https://doi.org/10.33005/adv.v8i2.1799

Yang, J., Bao, Y., Zhang, Y., Li, X., & Ge, Q. (2018). Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model. Chinese Geographical Science, 28(3), 505–515. https://doi.org/10.1007/s11769-018-0954-6

Downloads

PlumX Metrics

Published

2024-02-29

How to Cite

Nurdiansyah, D., Ma’ady, M. N. P., Kartini, A. Y., & Yuliana, U. A. (2024). The Use of A Geographically Weighted Regression Model to Analyze Predictors of The Rice Supply in Bojonegoro. Vygotsky: Jurnal Pendidikan Matematika Dan Matematika, 6(1), 1–12. https://doi.org/10.30736/voj.v6i1.706