PREDIKSI KESEHATAN MASYARAKAT INDONESIA MENGGUNAKAN RECURENT NEURAL NETWORK
DOI:
https://doi.org/10.32627/internal.v4i1.139Keywords:
Kesehatan Masyarakat, Prediksi, 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.References
BPS, “Statistik Kesehatan Masyarakat Indonesia,” Badan Pusat Statistik, 2018.
S. A. Abdulkarim, “Time series prediction with simple recurrent neural networks,” Bayero J. Pure Appl. Sci., vol. 9, no. 1, p. 19, Jul. 2016, doi: 10.4314/bajopas.v9i1.4.
K. Park, J. Kim, and J. Lee, “Visual Field Prediction using Recurrent Neural Network,” Sci. Rep., vol. 9, no. 1, p. 8385, Dec. 2019, doi: 10.1038/s41598-019-44852-6.
O. C. K. Chang S. V., Gan K. S., “A review of stock market prediction with Artificial neural network (ANN),” IEEE Int. Conf. Control Syst. Comput. Eng., pp. 477–482, 2014.
T. S, “Stock Market Cost Forecasting by Recurrent Neural Network on Long Short-Term Memory Model,” [Online]. Available: http://www.irjet.net.
P. J. V Casas D. M, Gonzalez A.T, Rodrígue J.E. A., “Using Data-Mining for Short-Term Rainfall Forecasting,” Notes Comput. Sci., vol. 5518, pp. 487–490, 2009.
V. A. . Priya A.M., “Weather Prediction Model using Recurrent Neural Network,” eduindex, 2019, [Online]. Available: https://journals.eduindex.org/index.php/think-india/article/view/12116.
S. S, “Extracting Spatial Semantics in Association Rules for Weather Forecasting Image,” India IEEE, 2010.
Z. C. Lipton, J. Berkowitz, and C. Elkan, “A Critical Review of Recurrent Neural Networks for Sequence Learning,” May 2015, [Online]. Available: http://arxiv.org/abs/1506.00019.
Z. C. Lipton, D. C. Kale, C. Elkan, and R. Wetzel, “Learning to Diagnose with LSTM Recurrent Neural Networks,” Nov. 2015, [Online]. Available: http://arxiv.org/abs/1511.03677.
A. A. Rizal and S. Soraya, “Multi Time Steps Prediction dengan Recurrent Neural Network Long Short Term Memory,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 18, no. 1, pp. 115–124, Nov. 2018, doi: 10.30812/matrik.v18i1.344.
Downloads
Published
Issue
Section
License
Copyright Notice
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
