Penerapan Spatial Autoregressive Model pada Kasus Multikolinearitas (Studi Kasus: Indeks Ketahanan Pangan di Indonesia) Application of the Spatial Autoregressive Model to a Multicollinearity Case (Case Study: Food Security Index in Indonesia)
Article Sidebar
Page Numbers:
4785-4803
Digital Object Identifier:
10.58578/tsaqofah.v6i5.11813
Viewed: 0 times
Downloaded: 0 times
LYAS Publisher cordially invites qualified professionals to serve as Editors or Reviewers. Your expertise will make an important contribution to maintaining and strengthening the academic quality of our publications. Interested applicants are kindly requested to complete the application form at the following link: Editors & Reviewers
Please feel free to contact us if you need any further information about the submission process or if you have any additional questions.
Authors:Diah Uswatun Hasanah1, Zamahsary Martha2
Copyright :Authors retain copyright and grant the journal right of first publication.
Main Article Content
Abstract
Food security modeling in Indonesia needs to consider interregional linkages and strong correlations among predictor variables. However, the application of spatial models that simultaneously address spatial dependence and multicollinearity at the provincial level remains limited. This study aimed to apply the Spatial Autoregressive Model (SAR) to model Indonesia’s 2024 Food Security Index (FSI), using Principal Component Analysis (PCA) as an approach to address multicollinearity. The study employed an applied quantitative approach using secondary data from 38 provinces obtained from Badan Pangan Nasional and Badan Pusat Statistik. Seven predictor variables were analyzed using multiple linear regression, variance inflation factor (VIF), PCA, Moran’s I, Lagrange Multiplier, and SAR with a k-nearest neighbors (KNN) spatial weight matrix. The results indicated high multicollinearity among three predictor variables. PCA produced three principal components that explained 94.50% of the variance in the data. A Moran’s I value of 0.71005767 indicated positive spatial autocorrelation, while the SAR spatial coefficient of 0.52676 indicated a significant spatial effect. The SAR model performed better than multiple linear regression, with an AIC value of 242.5907 and an R² of 0.7577. These findings confirm that integrating PCA and SAR produces FSI modeling that is more consistent with the characteristics of the data and can support the formulation of food security policies that consider interprovincial linkages. Future research may develop a spatial panel approach to analyze FSI dynamics over time.
Citation Metrics:
Downloads
Citation Metrics & Similar Scopus Articles
-
Suarni S. (2027)SPATIAL MODELLING OF DENGUE OUTBREAK RISK BY INTEGRATING LARVAL DENSITY AND SOCIO-ENVIRONMENTAL FACTORS IN AN URBAN AREAIndian Journal of Entomology, 89(2), 314-317
-
Panigrahi S.K. (2027)SPATIAL ASSESSMENT OF DENGUE VECTORS IN KALAHANDI, ODISHAIndian Journal of Entomology, 89(2), 274-278
-
Mahmoudi P. (2027)Regionalization, temporal-spatial characteristics, and trend of changes in precipitation and drought in IranIranian Journal of Geophysics, 20(3), 205-240
Article Details
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
References
Anselin, L. (1988). Spatial econometrics: Methods and models. Kluwer Academic Publishers. https://doi.org/10.1007/978-94-015-7799-1
Badan Ketahanan Pangan Kementerian Pertanian. (2020). Indeks Ketahanan Pangan 2020. Kementerian Pertanian Republik Indonesia. https://badanpangan.go.id/storage/app/media/2021/ikp-2020-20210120fix.pdf
Badan Pangan Nasional. (2022). Indeks Ketahanan Pangan 2022. https://badanpangan.go.id/storage/app/media/2023/Buku%20Digital/Buku%20Indeks%20Ketahanan%20Pangan%202022%20Signed.pdf
Badan Pangan Nasional. (2025). Indeks Ketahanan Pangan (IKP) Tingkat Provinsi Tahun 2024 (12 Indikator) [Data set]. Open Data Badan Pangan Nasional. https://data.badanpangan.go.id/datasetpublications/elm/ikp-provinsi-2024-backcasting
Badan Pusat Statistik. (2025, February 3). Pada 2024, Luas Panen Padi Mencapai Sekitar 10,05 Juta Hektare dengan Produksi Padi Sebanyak 53,14 Juta Ton Gabah Kering Giling (GKG). https://www.bps.go.id/id/pressrelease/2025/02/03/2414/pada-2024--luas-panen-padi-mencapai-sekitar-10-05-juta-hektare-dengan-produksi-padi-sebanyak-53-14-juta-ton-gabah-kering-giling--gkg--.html
BBC News Indonesia. (2023, October 27). Puluhan Warga Yahukimo Dilaporkan Meninggal karena Kelaparan, Mengapa Bencana Ini Terus Berulang di Papua? https://www.bbc.com/indonesia/articles/cv207n5y9pdo
Cahyani, R., & Sutikno. (2018). Pemodelan Tingkat Kesejahteraan Masyarakat di Kota Surabaya Menggunakan Regresi Spasial. Jurnal Sains dan Seni ITS, 7(2), D206–D212. https://doi.org/10.12962/j23373520.v7i2.34092
Ghozali, I. (2018). Aplikasi Analisis Multivariate dengan Program IBM SPSS 25 (9th ed.). Badan Penerbit Universitas Diponegoro.
Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics (5th ed.). McGraw-Hill/Irwin.
Haris, M. S., Pradini, R. S., & Khudori, A. N. (2025). Pemetaan Disparitas Stunting di Jawa Timur dengan Spatial Autoregressive Model (SAR) dan Spatial Error Model (SEM). Jurnal Sistem Komputer dan Informatika (JSON), 7(1), 64–72. https://doi.org/10.30865/json.v7i1.8877
Hastuti, D. S., & Yulianto, S. (2024). Factors affecting the resilience index food in Papua Province and West Papua Province using a spatial model approach. J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika, 17(1), 635–644. https://doi.org/10.36456/jstat.vol17.no1.a9087
Jolliffe, I. T. (2002). Principal component analysis (2nd ed.). Springer. https://doi.org/10.1007/b98835
Kurniati, P. (2023, July 30). Duka di Papua Tengah, 6 Warga Meninggal Akibat Kekeringan dan Kelaparan. Kompas.com. https://regional.kompas.com/read/2023/07/30/060531778/duka-di-papua-tengah-6-warga-meninggal-akibat-kekeringan-dan-kelaparan?page=all
LeSage, J. P., & Pace, R. K. (2009). Introduction to spatial econometrics. CRC Press. https://doi.org/10.1201/9781420064254
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis (5th ed.). John Wiley & Sons.
Oktarina, C. R., Sriliana, I., Sidik, E. N. F., & Firmansyah, M. A. (2024). Pemodelan Data Geospasial Balita Kurang Gizi dengan Pendekatan Geographically Weighted Regression Principal Component Analysis. Jurnal Gaussian, 13(2), 339–350. https://doi.org/10.14710/j.gauss.13.2.339-350
Rahmawati, D., & Bimanto, H. (2021). Perbandingan Spatial Autoregressive Model dan Spatial Error Model dalam Pemodelan Indeks Pembangunan Manusia di Provinsi Jawa Timur. Jurnal Statistika dan Aplikasinya, 5(1), 41–50. https://doi.org/10.21009/JSA.05104
Rencher, A. C. (2002). Methods of multivariate analysis (2nd ed.). John Wiley & Sons. https://doi.org/10.1002/0471271357
Republik Indonesia. (2012). Undang-Undang Republik Indonesia Nomor 18 Tahun 2012 tentang Pangan. https://peraturan.bpk.go.id/Details/39100/uu-no-18-tahun-2012
United Nations Department of Economic and Social Affairs. (2025). Goal 2: End hunger, achieve food security and improved nutrition and promote sustainable agriculture. United Nations. https://sdgs.un.org/goals/goal2