Prediksi Belanja Pemerintah Indonesia Menggunakan Long Short-Term Memory (LSTM)

  • Sabar Sautomo STMIK Nusa Mandiri Jakarta
  • Hilman Ferdinandus Pardede STMIK Nusa Mandiri Jakarta
Keywords: Government Spending, Deep Learning, 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 availability of 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) Model for expenditure predictions. Experiments show that LSTM model using three hidden layers and the appropriate hyperparameters can 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.


Download data is not yet available.


I. Iskandar, R. Willett, and S. Xu, “The development of a government cash forecasting model,” J. Public Budgeting, Account. Financ. Manag., vol. 30, no. 4, pp. 368–383, 2018, doi: 10.1108/JPBAFM-04-2018-0036.

E. Sumando, “Pengembangan Metode Cash Forecasting Pemerintah: Studi Kasus Saldo Kas Pemerintah 2009 – 2011,” Kaji. Ekon. dan Keuang., vol. 2, pp. 70–93, Sep. 2018, doi: 10.31685/kek.v2i1.284.

R. Magdalena, B. Logica, and T. Zamfiroiu, “Forecasting public expenditure by using feed-forward neural networks,” Econ. Res. Istraz. , vol. 28, no. 1, pp. 668–686, 2015, doi: 10.1080/1331677X.2015.1081828.

S. Siami-Namini and A. S. Namin, “Forecasting Economics and Financial Time Series: ARIMA vs. LSTM,” pp. 1–19, 2018, [Online]. Available:

X. Shao, C. S. Kim, and P. Sontakke, “Accurate deep model for electricity consumption forecasting using multi-channel and multi-scale feature fusion CNN-LSTM,” Energies, vol. 13, no. 8, 2020, doi: 10.3390/en13081881.

R. Fu, Z. Zhang, and L. Li, “Using LSTM and GRU neural network methods for traffic flow prediction,” 2017, doi: 10.1109/YAC.2016.7804912.

A. G. Salman, Y. Heryadi, E. Abdurahman, and W. Suparta, “Single Layer & Multi-layer Long Short-Term Memory (LSTM) Model with Intermediate Variables for Weather Forecasting,” Procedia Comput. Sci., vol. 135, pp. 89–98, 2018, doi: 10.1016/j.procs.2018.08.153.

S. García, J. Luengo, and F. Herrera, “Data Preprocessing in Data Mining,” Intell. Syst. Ref. Libr., 2015.

Z. Zeng, H. Zhang, R. Zhang, and C. Yin, “A novel feature selection method considering feature interaction,” Pattern Recognit., 2015, doi: 10.1016/j.patcog.2015.02.025.

A. M. De Silva and P. H. W. Leong, “Grammar-based feature generation for time-series prediction,” SpringerBriefs Appl. Sci. Technol., 2015, doi: 10.1007/978-981-287-411-5.

F. Chollet, “keras,” Github, 2015.

O. B. Sezer, M. U. Gudelek, and A. M. Ozbayoglu, “Financial time series forecasting with deep learning: A systematic literature review: 2005–2019,” Appl. Soft Comput. J., 2020, doi: 10.1016/j.asoc.2020.106181.

How to Cite
Sabar Sautomo, & Hilman Ferdinandus Pardede. (2021). Prediksi Belanja Pemerintah Indonesia Menggunakan Long Short-Term Memory (LSTM). Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(1), 99 - 106.
Artikel Teknologi Informasi