Fuzzy Inference System for Suitability of Corn Crop Land in Bogor Regency

Authors

  • Rozali Ilham Institut Pemerintahan Dalam Negeri, Indonesia
  • Wenty Zahrati Institut Pemerintahan Dalam Negeri, Indonesia

DOI:

https://doi.org/10.30983/knowbase.v3i1.6674

Keywords:

Fuzzy Inference System, Corn, Land Suitabillity

Abstract

Corn is one of the crucial food crop commodities in Indonesia. The Ministry of Agriculture has stated that the national corn production has shown a rapid increase in the last three years, with an average growth reaching 24,037,581 tons. This upward trend in corn production over the past three years indicates the government's commitment to making corn a primary food crop commodity that requires attention. One of the efforts to sustain corn production is by enhancing productivity, including evaluating land suitability based on corn planting characteristics. This research aims to develop a fuzzy inference system for corn land suitability. The fuzzy inference system comprises nine parameters to determine land suitability, consisting of seven numeric parameters: temperature, rainfall, slope, base saturation, cation exchange capacity (CEC), soil acidity, soil depth, and two categorical parameters: drainage and soil texture. In this research, land suitability evaluation was employed to classify land suitability into categories: Highly Suitable (S1), Moderately Suitable (S2), Marginally Suitable (S3), and Not Suitable (N). The research successfully developed a fuzzy inference system for corn land suitability assessment. Experimental results utilizing several corn growth condition data obtained from expert knowledge revealed that in Bogor Regency, the land classified as Highly Suitable (S1) covers an area of 151089.90 Ha, Moderately Suitable (S2) covers 59701.35 Ha, Marginally Suitable (S3) covers 53737.34 Ha, and Not Suitable (N) covers 23118.16 Ha.

References

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Published

2023-06-30

How to Cite

Ilham, R., & Zahrati, W. (2023). Fuzzy Inference System for Suitability of Corn Crop Land in Bogor Regency. Knowbase : International Journal of Knowledge in Database, 3(1), 105–115. https://doi.org/10.30983/knowbase.v3i1.6674

Issue

Vol. 3 No. 1 (2023): June 2023

Section

Articles

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