PREDIKSI KEMISKINAN DI PROVINSI PAPUA MENGGUNAKAN ALGORITMA NEURAL NETWORK
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Institusion
Universitas Muhammadiyah Gorontalo
Author
Wire, Giluwi
Subject
QA Mathematics
Datestamp
2023-06-19 07:40:59
Abstract :
Poverty is one of the fundamental problems for every country, especially for developing countries. Prediction is a process of systematically estimating something that is most likely to happen in the future based on past and present information is owned, therefore the error (the difference between something that happened and the forecast result) can be minimized. The Neural Network Algorithm is an artificial neural network built to copy the work of the human brain. This research uses poverty data in Papua province from 2010-2021 with a total of 352 data. The results of poverty prediction in the Papua province using a neural network algorithm showed the accuracy of the Neural Network algorithm in predicting poverty is good. This is evidenced by the smallest RMSE level, which is still at 0.072. and after testing using calculations in Microsoft Excel it can be seen that the prediction results from 2010-2021 and the range of error data from denormalized data with values ranging from 1045.183 to 0.843.
Poverty is one of the fundamental problems for every country, especially for developing countries. Prediction is a process of systematically estimating something that is most likely to happen in the future based on past and present information is owned, therefore the error (the difference between something that happened and the forecast result) can be minimized. The Neural Network Algorithm is an artificial neural network built to copy the work of the human brain. This research uses poverty data in Papua province from 2010-2021 with a total of 352 data. The results of poverty prediction in the Papua province using a neural network algorithm showed the accuracy of the Neural Network algorithm in predicting poverty is good. This is evidenced by the smallest RMSE level, which is still at 0.072. and after testing using calculations in Microsoft Excel it can be seen that the prediction results from 2010-2021 and the range of error data from denormalized data with values ranging from 1045.183 to 0.843.
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