Spatial Analysis of the Driving Factors Behind Agricultural Land Conversion in Relation to Food Security Policy (Case Study: Bogor Regency)
DOI:
10.29303/jppipa.v12i2.13903Published:
2026-02-25Downloads
Abstract
The conversion of agricultural land to non-agricultural uses poses a significant threat to food security, particularly in metropolitan buffer regions such as Bogor Regency. Rapid population growth and economic restructuring have led to a 24.5% reduction in rice field area, declining from 48,177 ha in 2003 to 36,355 ha in 2019, contributing to a regional rice deficit of 37%. This study employs a quantitative spatial approach using Geographic Information Systems (GIS), integrating Spatial Overlay, Cellular Automata (CA), and a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) to analyze land conversion dynamics and evaluate the effectiveness of spatial planning policies. The results reveal persistent conversion of rice fields, including within designated food crop zones under the Regional Spatial Plan (RTRW), indicating a misalignment between spatial planning and actual land-use dynamics. This study contributes to applied environmental science by demonstrating the value of integrated spatial modeling in assessing land-use policy effectiveness and supporting spatially informed strategies for sustainable agricultural land management and local food security.
Keywords:
Cellular automata Food security Land conversion MLP-ANN Spatial planning policyReferences
Ayuningrum, F. T. (2023). Proyeksi Penduduk Indonesia 2020-2050 : Hasil Sensus Penduduk 2020. Badan Pusat Statistik.
Castillo-Girones, S., Munera, S., Martínez-Sober, M., Blasco, J., Cubero, S., & Gómez-Sanchis, J. (2025). Artificial Neural Networks in Agriculture, the core of artificial intelligence: What, When, and Why. Computers and Electronics in Agriculture, 230, 230. https://doi.org/10.1016/j.compag.2025.109938
Faoziyah, U., Rosyaridho, M. F., & Panggabean, R. (2024). Unearthing Agricultural Land Use Dynamics in Indonesia: Between Food Security and Policy Interventions. Land, 13(12), 2030. https://doi.org/10.3390/land13122030
Hezbollah, M. Z., Hermawan, E., & Agustian H, S. (2025). Spatial Analysis Model of Land Use Change and Prediction in Ciawi District Using Cellular Automata - Artificial Neural Network. E-Jurnal Penyelidikan Dan Inovasi, 12(2), 43–58. https://doi.org/10.53840/ejpi.v12i2.279
Ivanka, R., Atalla, F., Dita Limbong, A., & Simarmata, T. (2024). Assessing the Current State and Future Trends of Land Use Conversion: Implications for Food Security in Indonesia. International Journal of Life Science and Agriculture Research, 3(4), 284–90. https://doi.org/10.55677/ijlsar/V03I4Y2024-10
Kartikasari, A. N. I., Prasojo, S. I. L., Kaffa, N. S., & Robbani, H. W. (2025). Land Cover Projection of Jember Irrigation Area Using MOLUSCE QGIS. Geoid, 20(2), 35–44. Retrieved from https://tanahair.indonesia.go.id/.
Liu, X., Huang, Y., Xu, X., Li, X., Li, X., Ciais, P., Lin, P., Gong, K., Ziegler, A. D., Chen, A., Gong, P., Chen, J., Hu, G., Chen, Y., Wang, S., Wu, Q., Huang, K., Estes, L., & Zeng, Z. (2020). High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. Nature Sustainability, 3(7), 564–570. https://doi.org/10.1038/s41893-020-0521-x
Murdaning, M. (2017). Analisis Spasial Perubahan Penggunaan Lahan Pertanian Untuk Mendukung Kemandirian Pangan Di Kabupaten Indramayu. Majalah Ilmiah Globe, 19(2), 175. https://doi.org/10.24895/mig.2017.19-2.604
Nugroho, A. E. J. K., Mahdi, I., Utami, S. D., Wijayanti, P., & Noviani, R. (2025). Pemodelan Spasial untuk Prediksi Perubahan Penggunaan Lahan di Kabupaten Kendal, Provinsi Jawa Tengah. Jurnal Pembangunan Wilayah Dan Kota, 21(2), 198–214. https://doi.org/10.14710/pwk.v21i2.70203
Pribadi, D. O., & Pauleit, S. (2015). The dynamics of peri-urban agriculture during rapid urbanization of Jabodetabek Metropolitan Area. Land Use Policy, 48, 13–24. https://doi.org/10.1016/j.landusepol.2015.05.009
Rizqi, B., & Manessa, M. D. M. (2025). Tinjauan Sistematis Prisma. Jurnal Tanah Dan Sumberdaya Lahan, 12(2), 333–346. https://doi.org/10.21776/ub.jtsl.2025.012.2.11
Sasmita, V. (2024). Organ Perusahaan Dalam Bentuk Usaha Di Indonesia. Jurnal Hukum Dan Kewarganegaraan, 4(7). https://doi.org/10.3783/causa.v2i9.2461
Sumunar, A. A. K., & Budiman, S. (2021). Proyeksi Ketahanan Pangan Provinsi Nusa Tenggara Timur Tahun 2015-2045 Ditinjau Dari Ketersediaan, Kebutuhan, Dan Persediaan Beras. SEPA: Jurnal Sosial Ekonomi Pertanian Dan Agribisnis, 18(1), 80. https://doi.org/10.20961/sepa.v18i1.47131
Tsumita, N., Piyapong, S., Kaewkluengklom, R., Jaensirisak, S., & Fukuda, A. (2025). Flood susceptibility mapping of urban flood risk: comparing autoencoder multilayer perceptron and logistic regression models in Ubon Ratchathani, Thailand. Natural Hazards, 121(15), 17833–17867. https://doi.org/10.1007/s11069-025-07494-8
Viana, L. R., Dessureault, P.-L., Marty, C., Boucher, J.-F., & Paré, M. C. (2023). Life Cycle Assessment of Oat Flake Production with Two End-of-Life Options for Agro-Industrial Residue Management. Sustainability, 15(6), 5124. https://doi.org/10.3390/su15065124
Wibisono, G., & Yeni Widowaty. (2023). Urgensi Perlindungan Lahan Pertanian Pangan Terhadap Alih Fungsi Lahan di Daerah Istimewa Yogyakarta. Krtha Bhayangkara, 17(1), 93–106. https://doi.org/10.31599/krtha.v17i1.1962
Wilimitis, D., & Walsh, C. G. (2023). Practical Considerations and Applied Examples of Cross-Validation for Model Development and Evaluation in Health Care: Tutorial. JMIR AI, 2(1), e49023. https://doi.org/10.2196/49023
Wirapradeksa, H. (2024). Analysis of The Implementation of Sustainable Food Agricultural Land Protection Policy in Bandung District: A Case Study of Rice Field Conversion in Cikancung District. Jurnal Indonesia Sosial Teknologi, 5(4), 1663–1681. https://doi.org/10.59141/jist.v5i4.893
Zahrayni, A. N., Tjaturahono Budi Sanjoto, Rahma Hayati, & Ariyani Indrayati. (2025). Prediksi Perubahan Penggunaan Lahan Tahun 2034 Dengan Permodelan Cellular Automata Di Kabupaten Semarang. Jurnal Geografi, Edukasi Dan Lingkungan (JGEL), 9(2), 387–410. https://doi.org/10.22236/jgel.v9i2.17775
Zhu, Y., Geiß, C., So, E., Bardhan, R., Taubenböck, H., & Jin, Y. (2024). Urban expansion simulation with an explainable ensemble deep learning framework. Heliyon, 10(7), e28318. https://doi.org/10.1016/j.heliyon.2024.e28318
License
Copyright (c) 2026 Rizki Pranata Dewa, Perdinan, Lukytawati Anggraeni
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with Jurnal Penelitian Pendidikan IPA, agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License (CC-BY License). This license allows authors to use all articles, data sets, graphics, and appendices in data mining applications, search engines, web sites, blogs, and other platforms by providing an appropriate reference. The journal allows the author(s) to hold the copyright without restrictions and will retain publishing rights without restrictions.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in Jurnal Penelitian Pendidikan IPA.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
