Analisis Kesesuaian Lahan Pertanian di Provinsi Jawa Timur Menggunakan Pendekatan Random Forest dan Sistem Informasi Geografis

Authors

  • Isna Kumala Sari Universitas Islam Negeri Sayyid Ali Rahmatullah Tulungagung, Tulungagung, Indonesia
  • Ardiana Fatma Dewi Universitas Islam Negeri Sayyid Ali Rahmatullah Tulungagung, Tulungagung, Indonesia

DOI:

https://doi.org/10.53863/agronu.v5i02.2330

Keywords:

agricultural land suitability, East Java, Geographic Information System, Random Forest, remote sensing

Abstract

East Java Province is one of the major agricultural production centers in Indonesia with diverse biophysical characteristics. Variations in topography, climate, soil properties, and vegetation conditions contribute to differences in agricultural land suitability across the region. This study aimed to analyze agricultural land suitability in East Java Province using a Random Forest algorithm integrated with Geographic Information Systems (GIS) and multisource geospatial data. The variables employed included elevation, slope, aspect, rainfall, temperature, soil pH, soil organic carbon (SOC), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Normalized Difference Water Index (NDWI). Data were obtained from SRTM, CHIRPS, TerraClimate, SoilGrids, Landsat 8, and ESA WorldCover datasets. The Random Forest model was developed using 80% of the samples for training and 20% for testing. Model evaluation showed an overall accuracy of 80.12%, a Cohen’s Kappa coefficient of 0.4212, and an ROC-AUC value of 0.8596, indicating good classification performance. Feature importance analysis revealed that slope was the most influential variable (18.43%), followed by NDWI (14.69%), elevation (12.24%), and NDVI (11.63%). Spatial analysis indicated that moderately suitable land dominated East Java, covering approximately 3.28 million ha (48.71%), followed by land of low suitability, covering 2.00 million ha (29.75%), and highly suitable land, covering 1.45 million ha (21.54%). The results demonstrate that integrating Random Forest with GIS provides an effective approach for assessing agricultural land suitability and can support sustainable agricultural planning and spatial decision-making in East Java Province

References

Al-Taani, A., Al-Husban, Y., & Farhan, I. (2021). Land suitability evaluation for agriculture use using GIS and remote sensing techniques: The case study of Ma'an Governorate Jordan. Egyptian Journal of Remote Sensing and Space Science, 24(1), 109–117. https://doi.org/10.1016/j.ejrs.2020.01.001

Belgiu, M., & Drăguț, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011

Badan Pusat Statistik Provinsi Jawa Timur. (2023). Jawa Timur dalam angka 2023. Badan Pusat Statistik Provinsi Jawa Timur.

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324

Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. https://doi.org/10.1177/001316446002000104

Food and Agriculture Organization of the United Nations. (1976). A framework for land evaluation. FAO.

Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., & Alsdorf, D. (2007). The shuttle radar topography mission. Reviews of Geophysics, 45(2), RG2004. https://doi.org/10.1029/2005RG000183

Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Scientific Data, 2, Article 150066. https://doi.org/10.1038/sdata.2015.66

Gao, B. C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3

Giannarakis, G., Kalivas, D., & Zervas, E. (2022). Machine learning approaches for agricultural land suitability assessment using geospatial data. Sustainability, 14(9), Article 5438. https://doi.org/10.3390/su14095438

Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., & Kempen, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLOS ONE, 12(2), e0169748. https://doi.org/10.1371/journal.pone.0169748

Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2

Li, X., Zhang, Y., Wang, H., & Liu, J. (2024). Spatial suitability evaluation based on multisource geospatial data and random forest modeling for sustainable land management. Frontiers in Environmental Science, 12, Article 1338931. https://doi.org/10.3389/fenvs.2024.1338931

Malczewski, J. (2004). GIS-based land-use suitability analysis: A critical overview. Progress in Planning, 62(1), 3–65. https://doi.org/10.1016/j.progress.2003.09.002

Nguyen, T. T., Nguyen, L. T., Nguyen, T. H., & Pham, T. D. (2023). Agricultural land suitability mapping using multi-criteria evaluation and GIS: A case study in the Mekong Delta, Vietnam. Land, 12(4), Article 812. https://doi.org/10.3390/land12040812

Powers, D. M. W. (2011). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. Journal of Machine Learning Technologies, 2(1), 37–63.

Rossiter, D. G. (2023). Updating and extending the land suitability evaluation framework for the 21st century. Geoderma, 429, Article 116248. https://doi.org/10.1016/j.geoderma.2022.116248

Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the Third Earth Resources Technology Satellite Symposium, 1, 309–317.

Shevchenko, O., Terekhov, A., & Kussul, N. (2023). Application of machine learning algorithms for agricultural land suitability mapping using remote sensing data. Remote Sensing Applications: Society and Environment, 31, Article 100996. https://doi.org/10.1016/j.rsase.2023.100996

Sys, C., Van Ranst, E., & Debaveye, J. (1991). Land evaluation. Part I: Principles in land evaluation and crop production calculations. Agricultural Publications.

Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Wevers, J., Cartus, O., Santoro, M., Fritz, S., & Herold, M. (2021). ESA WorldCover 10m 2020 product validation report. European Space Agency.

Downloads

Published

2026-07-15

How to Cite

Sari, I. K., & Dewi, A. F. (2026). Analisis Kesesuaian Lahan Pertanian di Provinsi Jawa Timur Menggunakan Pendekatan Random Forest dan Sistem Informasi Geografis. Jurnal Agroteknologi, 5(02), 101–112. https://doi.org/10.53863/agronu.v5i02.2330

Issue

Vol. 5 No. 02 (2026): Jurnal Agroteknologi

Section

Articles

License

Copyright (c) 2026 Isna Kumala Sari, Ardiana Fatma Dewi

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal