Analysis of The Influence of Changing Land Area on The Value of Gross Regional Domestic Product (GRDP) (Case Study: Lampung Province)

Ratna Mustika Sari; Albertus Deliar; Andri Hernandi

doi:10.30871/jagi.v7i1.4924

Ratna Mustika Sari Sumatera Institute of Technology / Geomatics Engineering / research / Jln Terusan Ryacudu, Kel. Way Hui, Kec. Jati Agung, Lampung Selatan 35365
Albertus Deliar Faculty of Earth Sciences and Technology ITB /Geodesy and Geomatics Engineering/ research / Jln Ganesha No. 10 Bandung 40132, West Java, Indonesia
Andri Hernandi Faculty of Earth Sciences and Technology ITB /Geodesy and Geomatics Engineering/ research / Jln Ganesha No. 10 Bandung 40132, West Java, Indonesia

DOI: https://doi.org/10.30871/jagi.v7i1.4924

Keywords: Land Cover, GRDP, Panel Data Linear Regression, Correlation

Abstract

Residents' activities will always use the land as a place to carry out activities which will affect the GRDP value. This study uses data on the GRDP values of Lampung Province in 2014–2021 obtained from BPS Lampung Province and uses land cover area data obtained from the classification results of Landsat 8 imagery for 2014–2021. The data analysis technique used in this study is correlation analysis and linear regression using panel data. The results of the correlation analysis found that the value of GRDP in total, GRDP in the agriculture, forestry, and fisheries sectors, GRDP in the processing industry sector, and GRDP in the construction sector all correlated with built-up land cover, for GRDP in the agriculture, forestry, and fisheries sectors, in addition to correlation with built-up land cover as well correlated with paddy field cover, and pond land. GRDP values that correlated with the land cover area were carried out by a linear regression analysis of panel data. The results of panel data linear regression calculations show that changes in the area of built-up land cover in Lampung Province do not affect the total value of GRDP, but the built-up land cover has a negative effect on GRDP in the agriculture, forestry, and fisheries sectors and a positive effect on GRDP in the processing industry sector and GRDP in the construction sector. The area covered by paddy fields and ponds has a positive effect on GRDP in the agriculture, forestry, and fisheries sectors.

Keywords: Land Cover, GRDP, Panel Data Linear Regression, Correlation

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References

BPS. (2022a). Jumlah Penduduk (jiwa). https://lampung.bps.go.id/indicator/12/45/3/jumlah-penduduk.html

BPS. (2022b). Produk Domestik Bruto (PDB)/ Produk Domestik Regional Bruto (PDRB). https://sirusa.bps.go.id/sirusa/index.php/indikator/4

Hidayat, A. (2014). Penjelasan Metode Analisis Regresi Data Panel. In Statistikian. Statistikian.

Indrasetianingsih, A., & Wasik, T. K. (2020). Model Regresi Data Panel Untuk Mengetahui Faktor Yang Mempengaruhi Tingkat Kemiskinan Di Pulau Madura. Jurnal Gaussian, 9(3), 355–363. https://doi.org/10.14710/j.gauss.v9i3.28925

Iqbal, M. (2015). Regresi Data Panel (2) “Tahap Analisis.” https://dosen.perbanas.id/regresi-data-panel-2-tahap-analisis/

Juniyanti, L., Prasetyo, L. B., Aprianto, D. P., Purnomo, H., & Kartodihardjo, H. (2020). Perubahan Penggunaan dan Tutupan Lahan, Serta Faktor Penyebabnya di Pulau Bengkalis, Provinsi Riau (periode 1990-2019). Jurnal Pengelolaan Sumberdaya Alam Dan Lingkungan (Journal of Natural Resources and Environmental Management), 10(3), 419–435. https://doi.org/10.29244/jpsl.10.3.419-435

Nur, S. (2019). Analisis Pengaruh Luas Lahan, Tenaga Kerja dan Ekspor Crude Palm Oil (CPO) Terhadap PDRB Sub Sektor Perkebunan Kelapa Sawit di Provinsi Riau Tahun 009-20015. 10(4), 228–231.

Pangestika, S. (2015). Analisis Estimasi Model Regresi Data Panel dengan Pendekatan Common Effect Model (CEM), Fixed Effect Model (FEM), dan Random Effect Model (REM). Universitas Negeri Semarang.

Putri, N. F. A. (2018). Pengaruh Luas Lahan, Jumlah Tenaga Kerja, Kredit Perbankan dan Infrastruktur Irigasi Terhadap PDRB Sektor Pertanian di Kabupaten Sumedang Periode 2005-2015. Universitas Pasundan, 11.

Sampurno, R., & Thoriq, A. (2016). Klasifikasi Tutupan Lahan Menggunakan Citra Landsat 8 Operational Land Imager (Oli) Di Kabupaten Sumedang. Jurnal Teknotan, 10(2), 61–70. https://doi.org/10.24198/jt.vol10n2.9

Sugiono, Izzaty, R. E., Astuti, B., & Cholimah, N. (2015). Analisa Korelasi. Analisa Kolerasi, 6(11), 951–952., 5–24.

Sutanto, A., Trisakti, B., & Arimurti, A. M. (2014). Perbandingan Klasifikasi Berbasis Objek dan Klasifikasi Berbasis Piksel Pada Data Citra Satelit Synthetic Aperture Radar Untuk Pemetaan Lahan. Jurnal Pengindraan Jauh, 11(1), 63–75.

UURI. (2014). Undang-Undang Republik Indonesia Nomor 37 Tahun 2014 Tentang Konservasi Tanah Dan Air. 3, 1–47.

yy Azimah, N., & Antomi, Y. (2020). Pengaruh Perubahan Penggunaan Lahan Terhadap Produk Domestik Regional Bruto Di Kota Padang Panjang dan Bukit Tinggi Tahun 2014-2018. Jurnal Buana, 4(5), 1–10.