Construction of Fuzzy Inference Model to Predict Percentage of Poor Population in Indonesia

Abstract

Fuzzy inference system is a soft computing model which has been widely applied to build a prediction model. This model applied the fuzzy sets theory and believed providing high accuracy for prediction. This study aims to construct the fuzzy inference model to predict the percentage of poor population in Indonesia based on unemployment rate and Gini index. The data of unemployment rates and Gini index are used as input while the data of poor population percentage is used as output. This fuzzy inference model consists of 5 rules. It used Mamdani Max-Min method for inference and centroid defuzzifier for defuzzification. The result demonstrated that the performance of this fuzzy inference model can predict the percentage of poor population with an accuracy level of 94.34%. Therefore, it can be concluded that fuzzy inference system can be used as an appropriate alternative model for predicting the percentage of poor population because it provides high accuracy.