Indonesian Government Revenue Prediction Using Long Short-Term Memory
Government revenue plays an important role in achieving national development goals. In the context of optimal state treasury management, accurate forecasts of government revenue are needed so that cash can be utilized optimally for the coming period. This study examines the appropriate method for pr...
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| Format: | Article |
| Language: | English |
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Universitas Teknologi Akba Makassar, Lembaga Penelitian dan Pengabdian Masyarakat
2024-06-01
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| Series: | Inspiration |
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| Online Access: | https://ojs.unitama.ac.id/index.php/inspiration/article/view/67 |
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| _version_ | 1832583825008361472 |
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| author | Mahmud Windu Gata Hafifah Bella Novitasari Sigit Kurniawan Dedi Dwi Saputra |
| author_facet | Mahmud Windu Gata Hafifah Bella Novitasari Sigit Kurniawan Dedi Dwi Saputra |
| author_sort | Mahmud |
| collection | DOAJ |
| description | Government revenue plays an important role in achieving national development goals. In the context of optimal state treasury management, accurate forecasts of government revenue are needed so that cash can be utilized optimally for the coming period. This study examines the appropriate method for predicting government revenue based on historical data from 2013 to 2022. It proposes applying the Long Short-Term Memory (LSTM) model for this purpose. Experiments show that the LSTM model, using two hidden layers and the right hyperparameters, can produce a Mean Absolute Percentage Error (MAPE) of 11.14% and a Root Mean Square Error (RMSE) of 15.43%. These results are better than those obtained using conventional modeling techniques such as Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA). The findings indicate that the LSTM model offers superior predictive accuracy and can significantly improve the management of government finances. By implementing this advanced predictive model, policymakers can make more informed decisions, enhancing the efficiency of resource allocation and contributing to the overall economic stability of the nation. |
| format | Article |
| id | doaj-art-f09de15aa5a8434fbc61f47a6fe9d558 |
| institution | Kabale University |
| issn | 2088-6705 2621-5608 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Universitas Teknologi Akba Makassar, Lembaga Penelitian dan Pengabdian Masyarakat |
| record_format | Article |
| series | Inspiration |
| spelling | doaj-art-f09de15aa5a8434fbc61f47a6fe9d5582025-01-28T05:47:58ZengUniversitas Teknologi Akba Makassar, Lembaga Penelitian dan Pengabdian MasyarakatInspiration2088-67052621-56082024-06-0114111112410.35585/inspir.v14i1.6767Indonesian Government Revenue Prediction Using Long Short-Term MemoryMahmud0Windu Gata1Hafifah Bella Novitasari2Sigit Kurniawan3Dedi Dwi Saputra4Universitas Nusa MandiriUniversitas Nusa MandiriUniversitas Nusa MandiriUniversitas Nusa MandiriUniversitas Nusa MandiriGovernment revenue plays an important role in achieving national development goals. In the context of optimal state treasury management, accurate forecasts of government revenue are needed so that cash can be utilized optimally for the coming period. This study examines the appropriate method for predicting government revenue based on historical data from 2013 to 2022. It proposes applying the Long Short-Term Memory (LSTM) model for this purpose. Experiments show that the LSTM model, using two hidden layers and the right hyperparameters, can produce a Mean Absolute Percentage Error (MAPE) of 11.14% and a Root Mean Square Error (RMSE) of 15.43%. These results are better than those obtained using conventional modeling techniques such as Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA). The findings indicate that the LSTM model offers superior predictive accuracy and can significantly improve the management of government finances. By implementing this advanced predictive model, policymakers can make more informed decisions, enhancing the efficiency of resource allocation and contributing to the overall economic stability of the nation.https://ojs.unitama.ac.id/index.php/inspiration/article/view/67government revenuemachine learninglstmarimasarima |
| spellingShingle | Mahmud Windu Gata Hafifah Bella Novitasari Sigit Kurniawan Dedi Dwi Saputra Indonesian Government Revenue Prediction Using Long Short-Term Memory Inspiration government revenue machine learning lstm arima sarima |
| title | Indonesian Government Revenue Prediction Using Long Short-Term Memory |
| title_full | Indonesian Government Revenue Prediction Using Long Short-Term Memory |
| title_fullStr | Indonesian Government Revenue Prediction Using Long Short-Term Memory |
| title_full_unstemmed | Indonesian Government Revenue Prediction Using Long Short-Term Memory |
| title_short | Indonesian Government Revenue Prediction Using Long Short-Term Memory |
| title_sort | indonesian government revenue prediction using long short term memory |
| topic | government revenue machine learning lstm arima sarima |
| url | https://ojs.unitama.ac.id/index.php/inspiration/article/view/67 |
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