Time Series Analysis for Customs Revenue Prediction using Arima Model in Python
DOI:
https://doi.org/10.56873/jitu.5.2.4927Keywords:
ARIMA, Customs, Import Duties, Python, Time SeriesAbstract
The Directorate General of Customs and Excise (DJBC) serves as a revenue collector in the field of customs and excise. This revenue plays an essential role in supporting infrastructure development. Predictions are needed to plan a good State Revenue and Expenditure Budget (APBN). Predictions serve as a tool for revenue optimization and control. However, forecasting is problematic because unpredictable external factors also influence these receipts. A logical and accountable approach is needed to predict acceptance to overcome this problem. The prediction method used is Autoregressive Integrated Moving Average (ARIMA). According to the computations, the Root Mean Square Percentage Error (RMSPE) value is less than 10%, indicating that the ARIMA model estimation is excellent
References
A. A. Irshadi and A. Wahyu Santoso, “PENGGUNAAN DATA MINING DALAM EKSTENSIFIKASI PENELITIAN ULANG,” J. Perspekt. BEA DAN CUKAI, vol. 5, no. 2, pp. 218–132, Nov. 2021, doi: 10.31092/jpbc.v5i2.1305.
T. K. Keuangan, “Informasi APBN 2021,” 2021, 2020.
Kemenkeu, “Realisasi Pendapatan Negara 2021 Capai Rp. 2.003,1 triliun, Lampaui Target APBN 2021,” Kemenkeu.go.id, 2022.
Kemenkeu, “Informasi APBN 2021 Percepatan Pemulihan Ekonomi dan Penguatan Reformasi,” Kementeri. Keuang. Direktorat Jenderal Anggar., no. September 2020, 2021.
D. Gunawan and W. Astika, “The Autoregressive Integrated Moving Average (ARIMA) Model for Predicting Jakarta Composite Index,” J. Inform. Ekon. Bisnis, Feb. 2022, doi: 10.37034/infeb.v4i1.114.
H. Thamrin, “Analyzing and Forecasting Admission data using Time Series Model,” J. Online Inform., vol. 5, no. 1, 2020.
V. Kulshreshtha and N. K. Garg, “Predicting the New Cases of Coronavirus [COVID-19] in India by Using Time Series Analysis as Machine Learning Model in Python,” J. Inst. Eng. Ser. B, vol. 102, no. 6, 2021, doi: 10.1007/s40031-021-00546-0.
J. Fattah, L. Ezzine, Z. Aman, H. El Moussami, and A. Lachhab, “Forecasting of demand using ARIMA model,” Int. J. Eng. Bus. Manag., vol. 10, 2018, doi: 10.1177/1847979018808673.
Kementerian Keuangan Republik Indonesia, “Laporan Kinerja Kementerian Keuangan Tahun 2020,” 2020.
F. Lazzeri, Machine learning for time series forecasting with python. wiley, 2020. doi: 10.1002/9781119682394.
M. H. Alsharif, M. K. Younes, and J. Kim, “Time series ARIMA model for prediction of daily and monthly average global solar radiation: The case study of Seoul, South Korea,” Symmetry (Basel)., vol. 11, no. 2, Feb. 2019, doi: 10.3390/sym11020240.
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