Analysis of Open Unemployment Rate Prediction Using Backpropagation Method
DOI:
https://doi.org/10.55123/jomlai.v4i2.5957Keywords:
Optimization , ANN , Prediction , Machine Learning , Covid-19Abstract
The open unemployment rate (TPT) is one of the important indicators in assessing the economic health of a region. This study aims to develop an accurate prediction model for the open unemployment rate using the backpropagation algorithm, as well as to evaluate the factors that influence the prediction. Accurate TPT prediction can help the government and policy makers in designing strategies to alleviate unemployment based on the results of the analysis of the developed model. This study aims to analyze and predict the Open Unemployment Rate (TPT) in various provinces in Indonesia in 2024 to 2026 using an artificial neural network model with the Backpropagation algorithm. Based on the test results, the 3-6-1 architecture model showed a prediction ability with 100% accuracy, while other architectures also gave very good results, with 100% accuracy for the 3-3-1 model and 97.06% for the 3-12-1 model. The TPT prediction results show that the unemployment rate is predicted to continue to increase from year to year, indicating the potential for an increase in the number of unemployed in the future. On the other hand, the accuracy analysis shows that each architecture produces different results, with the 3-6-1 architecture producing a longer time for the testing process, but still providing optimal accuracy. This finding illustrates that choosing the right architecture greatly affects the accuracy and efficiency in predicting TPT, which can be an important basis in formulating policies to eradicate unemployment in Indonesia.
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