Kajian Model Backpropagation dan Hybrid ANFIS Dalam Memprediksi Pertumbuhan Penduduk di Kabupaten Karawang - Tatang Rohana; Jamaludin Indra; Gugy Guztaman Munzi (Journal of Information System Research (JOSH), 2023)
Population growth rate prediction is a process of estimating the population in the future. Predictions are made so that the government can prepare strategic steps in anticipating the negative impact of an uncontrolled population increase. The research data is the population of Karawang Regency from 2011 to 2020. Backpropagation and Hybrid ANFIS are the models used in this study. The purpose of this study was to determine the RMSE value and scatter data formed from the results of the ANFIS Backpropagation and Hybrid training models in predicting population growth rates in Karawang Regency. In addition, this study is intended to determine the level of accuracy of the two models. The research step begins with research data validation, preprocessing, training and testing, as well as accuracy testing. Accuracy testing uses the Mean Absolute Percentage Error (MAPE) method. Backpropagation and Hybrid models in predicting the rate of population growth have worked well. This can be seen from the training results of the two models. Backpropagation model has the best RMSE of 0.0328 and Hybrid has the best RMSE of 0.021884. The results of the analysis of the accuracy of predicting population growth rates for 2019 and 2020 that have been carried out, both models have a good level of accuracy. Backpropagation has an average accuracy rate of 84.76%, while the Hybrid model has an average accuracy rate of 93.71%. Based on the results of accuracy testing, the Hybrid model has a better level of accuracy than the Backpropagation model.