Examining the Local Effects of Food Security Index Components Across Kalimantan Using Geographically Weighted Regression
DOI:
https://doi.org/10.34123/jurnalasks.v18i1.971Keywords:
Adaptive Gaussian, Food Security Index, Geographically Weighted Regression, Haversine Distance, PovertyAbstract
Introduction/Main Objectives: Food security remains a critical concern across Kalimantan Island, where substantial spatial disparities exist among its 56 regencies and cities, making conventional global regression models inadequate for capturing localized differences. Background Problems: This study addresses the limitation of Multiple Linear Regression in accounting for spatial heterogeneity in the relationships between Food Security Index components and the overall index, raising the question of which components exhibit spatially varying local effects across locations. Novelty: This study presents the first spatially explicit analysis of food security determinants at the regency and city level across Kalimantan, employing Haversine distance combined with adaptive Gaussian kernel weighting within GWR a combination not previously applied in this context. Research Methods: GWR was applied to cross-sectional 2024 data from the Food Security and Vulnerability Atlas, incorporating Cross Validation bandwidth selection and Weighted Least Squares parameter estimation. Finding/Results: The GWR model outperformed MLR with an R² of 59.63% and MSE of 38.5241. The ratio of population per health worker and average years of schooling for women were the most spatially dominant components, significant in 45 and 43 locations respectively, supporting the need for location-specific policy interventions across Kalimantan.
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References
Badan Pangan Nasional, Indeks Ketahanan Pangan. Jakarta, Indonesia: Badan Pangan Nasional, 2024.
S. Hidayat and A. S. Radyawanto, “Batalyon Infanteri Teritorial Pembangunan TNI AD: Pelopor Ketahanan Pangan Indonesia,” Journal of Industrial Engineering & Management Research, vol. 6, no. 5, pp. 90–96, 2025.
Badan Pangan Nasional, Indeks Ketahanan Pangan (IKP) Tingkat Kabupaten/Kota Tahun 2024 (12 Indikator), 2025.
M. Syakirotin, T. Karyani, and T. I. Noor, “Model Pengaruh Geographically Weighted Regression dan Strategi Alternatif Ketahanan Pangan Kabupaten Bandung Saat Pandemi COVID-19,” in Prosiding Seminar Nasional Hasil Penelitian Agribisnis VII, pp. 48–55, 2023.
N. A. Evalia et al., “Faktor-Faktor yang Mempengaruhi Ketahanan Pangan di Sumatera Barat,” AKADEMIK: Jurnal Mahasiswa Humanis, vol. 5, no. 2, pp. 937–948, 2025.
A. Comber, C. Brunsdon, M. Charlton, G. Dong, R. Harris, B. Lu, Y, Lu, D. Murakami, T. Nayaka, Y. Wang, and P. Harris, “A Route Map for Successful Applications of Geographically Weighted Regression,” Geographical Analysis, vol. 55, no. 1, pp. 155-178, 2023.
A. S. Fotheringham, C. Brunsdon, and M. Charlton, Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Chichester, UK: John Wiley & Sons, 2002.
T. L. N. Azizah and Suliadi, “Pemodelan Spasial Ketahanan Pangan Jawa Timur Tahun 2023,” in Bandung Conference Series: Statistics, vol. 5, no. 2, pp. 557-566, 2025.
Y. Miftahuddin, S. Umaroh, and F. R. Karim, “Perbandingan Metode Perhitungan Jarak Euclidean, Haversine, dan Manhattan dalam Penentuan Posisi Karyawan (Studi Kasus : Institut Teknologi Nasional Bandung),” Jurnal Tekno Insenstif, vol. 14, no. 2, pp. 69-77, 2020.
Setiawati, “Analisis Pengaruh Kebijakan Dividen terhadap Nilai Perusahaan pada Perusahaan Farmasi di BEI,” JIP: Jurnal Inovasi Penelitian, vol. 8, no. 8, pp. 1581–1590, 2021.
W. Sulistiyowati and C. C. Astuti, Statistika Dasar: Konsep dan Aplikasinya. Sidoarjo, Indonesia: UMSIDA Press, 2017.
A. T. R. Dani, F. B. Putra, V. Ratnasari, and I. N. Budiantara, BTS (Buku Tentang Statistika): Regresi Parametrik dan Non Parametrik Teori dan Aplikasi dengan Software R. Tasikmalaya, Indonesia: Perkumpulan Rumah Cemerlang Indonesia, 2024.
R. A. Choerunnisa, R. R. Dewi, M. Bariklana, and E. Widodo, “Analisis Faktor yang Mempengaruhi Tingkat Produksi Jahe di Indonesia Menggunakan Metode Regresi Linier Berganda,” Jurnal Ilmiah Ilmu Terapan Universitas Jambi, vol. 5, no. 2, pp. 231–242, 2021.
A. K. Khotimah, A. A. Rahman, M. Z. Alam, R. Adawiyah, Y. H. Nur, T. R. Aufi, and Sifriyani, “Analisis Regresi Linier Berganda Dalam Estimasi Indeks Pembangunan Manusia di Indonesia,” Jurnal Eksponensial, vol. 15, no. 2, pp. 90–99, 2024.
N. M. S. Ananda, S. Suyitno, and M. Siringoringo, “Geographically Weighted Panel Regression Modelling of Human Development Index Data in East Kalimantan Province in 2017–2020,” Jurnal Matematika, Statistika dan Komputasi, vol. 19, no. 2, pp. 323–341, 2023.
A. Miranda, S. Suyitno, and M. Fauziyah, “Modelling the Probability of River Water Pollution Using Geographically Weighted Logistic Regression Model (Case Study: River Water DO Data in East Kalimantan),” Jurnal Matematika, Statistika dan Komputasi, vol. 21, no. 2, pp. 408–430, 2025.
S. Novianti, S. Suyitno, and M. Siringoringo, “Model Geographically Weighted Weibull Regression dengan Kriteria Penentuan Bandwidth Optimum Akaike Information Criterion (Studi Kasus: Indikator Pencemaran Air Biochemical Oxygen Demand di Daerah Hutan Tropis Lembab DAS Mahakam Tahun 2019),” in Prosiding Seminar Nasional Matematika, Statistika, dan Aplikasinya, pp. 43–68, 2022.
K. D. Lorenza, S. C. Pratiwi, D. Puspita, I. R. A. I, and D. S. Rini, “Penerapan Spatial Autoregressive Model (SAR) untuk Mengetahui Faktor-faktor yang Memengaruhi Indeks Pembangunan Manusia (IPM),” Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika, vol. 7, no. 1, pp. 267-279, 2024.
A. Septiani, R. H. Hirzi, and N. U. Fikriah, “Analisis Penyebaran Jumlah Kasus PMK Pada Hewan Ternak Sapi di Kabupaten Lombok Tengah Menggunakan Indeks Moran Tahun 2022,” Variance: Journal of Statistics and Its Application, vol. 5, no. 2, pp. 159-168, 2023.
R. E. Caraka and H. Yasin, Geographically Weighted Regression (GWR): Sebuah Pendekatan Regresi Geografis. Yogyakarta, Indonesia: Mobius, 2017.
S. H. Daulay and E. Simamora, “Pemodelan Faktor-faktor Penyebab Kemiskinan di Provinsi Sumatera Utara Menggunakan Metode Geographically Weighted Regression (GWR),” Jurnal Riset Rumpun Matematika dan Ilmu Pengetahuan Alam, vol. 2, no. 1, pp. 47–60, 2023.
A. H. Harahap and M. I. Farza, “Pengaplikasian Rumus Haversine Sebagai Upaya Mengukur Kekuatan Interaksi Antarwilayah Kabupaten Ogan Komering Ilir dan Kota Palembang,” Jurnal Geografi, vol. 21, no. 1, pp. 90–96, 2025.
V. H. Nabilla, D. Fitria, and F. Fitri, “Comparison of Haversine and Euclidean Distance Formulas for Calculating Distance Between Regencies in West Sumatra,” UNP Journal of Statistics and Data Science, vol. 1, no. 3, pp. 120-125, 2023.
N. L. A. C. Dewi, M. N. Hayati, and M. Fauziyah, “Pemodelan GWR Menggunakan Fungsi Pembobot Adaptive Box-Car pada Angka Kesakitan DBD di Pulau Kalimantan Tahun 2023,” VARIANSI: Journal of Statistics and Its Application on Teaching and Research, vol. 7, no. 1, pp. 55–65, 2025.
N. Y. Adrianingsih, A. T. R. Dani, I. N. Budiantara, D. L. Ull, and R. A. Kosasih, “A Computational Analysis of Kernel-Based Nonparametric Regression Applied to Poverty Data,” Mandalika Mathematics and Education Journal, vol. 7, no. 3, pp. 1336–1347, 2025.
R. M. Untsa, F. S. Akbar, H. Briantoro, N. Rachmaningrum, and H. U. Mustakim, “Filter Least Mean Square (LMS) untuk Mengurangi Noise pada Sinyal Suara Tembakan,” in CENTIVE Conference on Electrical Engineering, Informatics, Industrial Technology, and Creative Media, vol. 3, no. 1, pp. 1–15, 2023.
