Penerapan Jaringan Saraf Tiruan untuk Memprediksi Jumlah TKW SBD di BP2MI dengan Pendekatan Algoritma Backropagation
DOI:
https://doi.org/10.61132/saturnus.v4i1.1405Keywords:
ANN, Backpropagation Algorithm, Government Policy, TKW, TKW Export PredictionAbstract
The increasing number of Indonesian Migrant Workers (TKW) working abroad, particularly through programs organized by BP2MI, has become a significant concern in managing the labor export process. One of the challenges faced is the uncertainty of the number of TKW to be sent each year, which is influenced by various external and internal factors. Therefore, this study aims to apply artificial neural networks (ANN) with a backpropagation algorithm approach to predict the number of TKW that will be processed by BP2MI. This method was chosen due to its ability to recognize patterns and nonlinear relationships between variables that affect the decision-making process for TKW export. In this study, the data used includes factors such as the number of job seekers, government policies, and the condition of the international labor market. The artificial neural network with the backpropagation algorithm is used to train the model based on existing historical data, with the goal of generating accurate predictions regarding the number of TKW to be processed in the coming years. The results of the tests show that the developed model can provide fairly accurate predictions and can serve as a tool for BP2MI in planning and managing the export of TKW more effectively. With the application of this technology, it is expected that the decision-making process related to TKW export can become more efficient and well-predicted.
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