PrediksiBelanja Pemerintah Indonesia Menggunakan Long Short-Term Memory (LSTM)

PrediksiBelanja Pemerintah Indonesia Menggunakan Long Short-Term Memory (LSTM)

Government Expenditure, MachineLearning, LSTM, ARIMA

Estimates of government expenditure for the next period are very important in the government, for instance for the Ministry of Finance of the Republic of Indonesia, because this can be taken into consideration in making policies regarding how much money the government should bear and whether there is sufficient availabilityof funds to finance it. As is the case in the health, education and social fields, modeling technology in machine learning is expected to be applied in the financial sector in government, namely in making modeling for spending predictions. In this study, it is proposed the application of Long Short-Term Memory (LSTM)Modelfor expenditure predictions. Experiments show that LSTMmodel using three hidden layers and the appropriate hyperparameterscan produce Mean Square Error (MSE) performance of 0.2325, Root Mean Square Error (RMSE) of 0.4820, Mean Average Error (MAE) of 0.3292 and Mean Everage Presentage Error (MAPE) of 0.4214. This is better than conventional modeling using the Auto Regressive Integrated Moving Average (ARIMA) as a comparison model

Sabar Sautomo1, Hilman Ferdinandus Pardede1,2

https://jurnal.iaii.or.id/index.php/RESTI/issue/view/20

STMIK Nusa Mandiri Jakarta

20 Februari 2021

Fajar bagus W

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Indonesia

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