Permodelan Indeks Ketahanan Pangan di Pulau Papua Menggunakan Geographically Weighted Regression

Ramadina, Aisyatul Kamilah (2026) Permodelan Indeks Ketahanan Pangan di Pulau Papua Menggunakan Geographically Weighted Regression. Other thesis, Institut Teknologi Sepuluh Nopember.

Text 2043221128-Undergraduate_Thesis.pdf - Accepted Version Restricted to Repository staff only Download (3MB) | Request a copy

Abstract

Ketahanan pangan merupakan isu strategis nasional yang berperan penting dalam mendukung kesejahteraan masyarakat dan pembangunan berkelanjutan. Pulau Papua tercatat sebagai salah satu wilayah dengan tingkat ketahanan pangan terendah di Indonesia, sebagaimana tercermin dalam Indeks Ketahanan Pangan (IKP) tahun 2024 yang masih berada pada kategori sangat rentan. Kondisi ini menunjukkan adanya ketimpangan dalam distribusi akses pangan serta dipengaruhi oleh berbagai faktor sosial, ekonomi, dan geografis. Penelitian ini bertujuan untuk mendeskripsikan karakteristik ketahanan pangan di Pulau Papua pada tahun 2024 serta mengetahui faktor yang memengaruhinya menggunakan metode Geographically Weighted Regression (GWR). Data yang digunakan merupakan data sekunder. Permodelan dilakukan menggunakan fungsi pembobot Adaptive Gaussian. Hasil permodelan menunjukkan bahwa variabel prediktor yang berpengaruh signifikan terhadap indeks ketahanan pangan pada hampir seluruh kabupaten/kota di Papua adalah variabel persentase penduduk yang hidup di bawah garis kemiskinan (X2) yaitu 41 kabupaten/kota. Selain itu, variabel rasio konsumsi normatif per kapita terhadap produksi bersih beras, jagung, ubi jalar, dan ubi kayu, serta stok beras pemerintah daerah dan bantuan pangan (X1) berpengaruh signifikan pada 40 kabupaten/kota, variabel persentase rumah tangga tanpa akses listrik (X4) signifikan pada 26 kabupaten/kota. Kemudian variabel persentase rumah tangga tanpa akses air bersih (X6) berpengaruh signifikan pada 38 kabupaten/kota. Terbentuk 6 kelompok berdasarkan kesamaan variabel prediktor yang berpengaruh signifikan dengan kebaikan model sebesar 99,782%.
==================================================================================================================================
Food security is a national strategic issue that plays an important role in supporting public welfare and sustainable development. Papua Island is recorded as one of the regions with the lowest level of food security in Indonesia, as reflected in the 2024 Food Security Index (FSI), which remains in the very vulnerable category. This condition indicates disparities in access to food distribution and is influenced by various social, economic, and geographical factors. This study aims to describe the characteristics of food security in Papua Island in 2024 and to identify the factors influencing it using the Geographically Weighted Regression (GWR) method. The data used in this study are secondary data. The modeling was conducted using an Adaptive Gaussian weighting function. The modeling results show that the predictor variable that significantly affects the food security index in almost all districts/cities in Papua is the percentage of the population living below the poverty line (X2), which is significant in 41 districts/cities. In addition, the ratio of normative per capita consumption to the net production of rice, maize, sweet potatoes, and cassava, as well as regional government rice stocks and food assistance (X1) is significant in 40 districts/cities. The percentage of households without access to electricity (X4) is significant in 26 districts/cities, while the percentage of households without access to clean water (X6) is significant in 38 districts/cities. A total of six groups were formed based on the similarity of significant predictor variables, with a model goodness of fit of 99.782%.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Geographically Weighted Regression, Indeks Ketahanan Pangan, Pulau Papua, Food Security Index, Geographically Weighted Regression, Papua Island
Subjects:	H Social Sciences > HA Statistics > HA30.6 Spatial analysis H Social Sciences > HA Statistics > HA31.3 Regression. Correlation. Logistic regression analysis.
Divisions:	Faculty of Vocational > 49501-Business Statistics
Depositing User:	Aisyatul Kamilah Ramadina
Date Deposited:	05 Feb 2026 01:01
Last Modified:	05 Feb 2026 01:01
URI:	http://repository.its.ac.id/id/eprint/132150

Actions (login required)

View Item