Backpropagation untuk Memprediksi Tingkat Inflasi di Kota Malang

Aisyah Sukmaindah Tristianti, Hisyam Fahmi, Erna Herawati

Abstract


Backpropagation is a model of Artificial Neural Networks (ANN) that uses supervised learning to solve complex problems, such as predicting the inflation rate by being trained using forward learning methods and backward error correction. The inflation rate is the percentage change in prices of goods and services in the economy during a certain period. Throughout December 2022, the Central Bureau of Statistics recorded the inflation rate in Malang City at 0.58%. One of the causes is the increase in prices of rice and other commodities such as eggs, chicken, and cayenne pepper. From this, predictions need to be made to help prepare programs that must be carried out to overcome inflation. This research aims to determine the level of accuracy of the backpropagation algorithm by testing several parameters and finding out the results of inflation predictions in Malang City in 2019. The findings of this research are the best inflation prediction results were obtained with the 7-14-1 architectural model with the tanh activation function parameters, batch size 16, and number of epochs 1000, with accuracy results of 87.49%. From the results of these predictions, the highest inflation in Malang City is predicted to occur in January 2019 and the lowest inflation is predicted to occur in February 2019.


Keywords


Backpropagation; Inflation Rate; Prediction; Artificial Neural Network

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DOI: https://doi.org/10.18860/jrmm.v3i6.28428

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