Implementation of Fuzzy Inference Mamdani System Based on Graphic User Interface for the timing of planting rice on rice plants in traditional farming systems in South Aceh district

Abstract

The agricultural sector plays an important role in a developing country that covers all economic life because it can become the main source of national income. Efforts that must be avoided are to avoid crop failure, one of the things that cause crop failure is that there is no match with the right planting period at the time of planting. So the purpose of this research is to build a decision-making system in the form of a system for determining planting time based on the traditional Aceh farming system. The process developed with information technology in the decision-making method using Mamdani fuzzy logic based on the Graphic User Interface by involving several criteria including the calculation of the month in the Hijri year, the calculation of days, the calculation of the time of planting, and the level of rainfall. So that when starting the process of planting rice traditionally, farmers with this system can choose the best time to grow crops in rice farming. This study used 40 samples as test data obtained from several villages in South Aceh Regency. The results of the study after testing, several classifications become decisions where the results that show very right are 2 respondents or 5%, exactly 16 respondents or 40%, less precise as many as 10 people or 25% and inaccurate as many as 12 people or 30%. This study used 40 samples as test data obtained from several villages in South Aceh Regency. The results of the study after testing, several classifications become decisions where the results that show very right are 2 respondents or 5%, exactly 16 respondents or 40%, less precise as many as 10 people or 25% and inaccurate as many as 12 people or 30%. This study used 40 samples as test data obtained from several villages in South Aceh Regency. The results of the study after testing, several classifications become decisions where the results that show very right are 2 respondents or 5%, exactly 16 respondents or 40%, less precise as many as 10 people or 25% and inaccurate as many as 12 people or 30%.