Use of Expert System in Determining Water Management in Agriculture with Methods: Fuzzy Logic and Rule-Based Decision Making

Abstract

This project aims to develop an expert system to support agricultural water management using Fuzzy Logic and Rule-Based Decision Making methods. This system is important for improving agricultural yields and environmental sustainability, especially with the increasing demand for food and the impacts of climate change. Data was taken from Kaggle, including information on soil conditions, temperature, and rainfall. Data processing includes missing value removal, outlier detection, and splitting the data into 80% training and 20% testing. Fuzzy Logic was chosen because it is able to handle data uncertainty and provide accurate output regarding crop water requirements, while Rule-Based Decision Making utilizes expert knowledge-based rules for decision making. Simulation results show that the Fuzzy Logic model provides recommendations for water needs according to actual conditions, with high responsiveness to soil moisture and temperature. The system is expected to be a tool to assist farmers in decision-making, increase agricultural productivity, and support water sustainability. This research contributes to the development of expert systems in agriculture and natural resource management based on modern technology.