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Kartika, D., & Safitri, W. (2024). Agriculture Application to Predict Soil Fertility with the Application of Fuzzy Tsukamoto. KnE Engineering, 6(1), 51–67. https://doi.org/10.18502/keg.v6i1.15349

https://doi.org/10.18502/keg.v6i1.15349

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authors

  • Devia Kartika

    Devia Kartika

    Universitas Putra Indonesia YPTK Padang, Indonesia
  • Wifra Safitri

    Wifra Safitri

    Universitas Putra Indonesia YPTK Padang, Indonesia

Published Date

  • Mar 7, 2024

Agriculture Application to Predict Soil Fertility with the Application of Fuzzy Tsukamoto

KnE Engineering / Jakarta International Conference on Multidisciplinary Studies towards Creative Industries (JICOMS) / Pages 51–67

Abstract

Indonesia is as an agricultural country, having majority of the population working in agriculture. Indonesia is an agricultural country that has extensive agricultural land and diverse and abundant natural resources. Based on Sakernas BPS 2021 data, Indonesian farmers numbered 38.77 million people. In the agricultural sector, land is a factor that plays a very important role in determining agricultural businesses. Where each region has different levels of soil fertility, depending on the type of soil and geographic location of an area. So, soil fertility is one of the determining factors for the success of agricultural businesses. Many farmers do not understand soil fertility in determining the right type of plant. This research aims to help people determine soil fertility parameters such as (C-Organic, P2O5 HCL, K2O HCL, KTK, Base Saturation, PH H2O), which will be processed using a WEB-based application using the Tsukamoto fuzzy method to predict fertility. Soil for selecting plant types and choosing the right land. From the land data obtained at the agricultural center, it is processed manually using the fuzzy method and applied to a system. Therefore, the results of this research will provide soil fertility status from data processed with C-organic (0.81%), pH H2O (5.38), P2O5 (2.32 ppm), KTK (8.5), K2O (50 ppm), Base Saturation (50%), we got a soil fertility status of 46.03, which is in the medium range. Meanwhile, in the system that has been built, the results obtained were 45.54, which is also in the same range, namely medium. From the results obtained, there was not much difference observed in the manual search and the system obtained results, it is estimated that they obtained 97% similarity. With this soil fertility detection system, it can increase the accuracy of soil fertility and make it easier to predict soil fertility. It is hoped that this system can have implications for the agricultural sector.


Keywords: agriculture, soil fertility, Fuzzy Tsukomoto, Web

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