PREDIKSI KESEHATAN MASYARAKAT INDONESIA MENGGUNAKAN RECURENT NEURAL NETWORK

Abstract

Health is very important for all human beings, especially in Indonesia, because human health can do activities properly and have high performance for both work and other social life. The task of predicting the future values of a time series is a problem that applications have in areas such as sales, engineering, epidemiology, etc. Much research effort has been made in the development of predictive models and performance improvement. The level of public health in Indonesia from 1995 to 2018 varied with the percentage of the population who experienced health complaints. The purpose of this study is to predict the future health of the Indonesian public so that it can be used as a tool to determine government policies in the health sector. The method used in predicting is the Recurent Neural Network (RNN) with secondary data sourced from the Central Statistics Agency (BPS) in the form of data sets, and dividing the data sets into training data and test data. Before the data is used as training data, we clean and tidy up the data first so that when it is implemented there are no errors either during training or testing. The results showed that at the beginning of the method RNN, the prediction results were far from the data, after an interval of 7 and above the predicted results were actually the same. Based on Figures 5 and 6, it can be said that the RNN method is very good for the prediction method.