Forecasting Taxpayer Registration Using ARIMA Models: A Case Study of KPP Pratama Meulaboh
DOI:
https://doi.org/10.36456/jstat.vol18.no2.a10769Keywords:
Time series, Forecasting, ARIMA model, Taxpayer registration, MeulabohAbstract
The number of registered taxpayers (WP) in a given region serves as a critical indicator of both the effectiveness of tax administration and the level of public participation in fulfilling tax obligations. KPP Pratama Meulaboh, as one of the vertical units of the Directorate General of Taxes, has experienced year-to-year fluctuations in taxpayer registration, highlighting the need for a systematic analytical approach to support forward-looking administrative planning. This study aims to forecast the number of registered corporate taxpayers at KPP Pratama Meulaboh using a time series approach based on the Autoregressive Integrated Moving Average (ARIMA) model. The dataset consists of annual observations covering the period 1982–2024. The analysis followed a structured procedure, including variance-stabilizing transformation, stationarity testing using the Augmented Dickey–Fuller (ADF) test, model identification through autocorrelation and partial autocorrelation analysis, and model selection based on the Akaike Information Criterion (AIC). Several ARIMA specifications were evaluated, and ARIMA(1,0,1) was selected as the optimal model, yielding the lowest AIC value (–66.0104) and statistically significant parameters. Diagnostic checks confirmed that the model residuals satisfied the white noise assumption. The selected ARIMA(1,0,1) model was subsequently used to generate ten-year-ahead forecasts, which indicate a steady upward trend in the number of registered corporate taxpayers over the forecast horizon. These results provide practical insights for tax authorities in planning administrative capacity, strengthening compliance monitoring, and supporting strategic tax base expansion within the jurisdiction of KPP Pratama Meulaboh.
References
[1] L. L. Halawa, M. Sukma, E. Limbong, and W. Pangestoeti, “Efisiensi dan efektivitas pengelolaan penerimaan pajak untuk pembangunan ekonomi di Indonesia,” Kajian Administrasi Publik dan Ilmu Komunikasi, vol. 2, no. 2, pp. 258–269, 2025. https://doi.org/10.62383/kajian.v2i2.396
[2] Direktorat Jenderal Pajak, Laporan Tahunan Direktorat Jenderal Pajak 2022. Kementerian Keuangan Republik Indonesia, 2022.
[3] Organisation for Economic Co-operation and Development (OECD), Tax Administration 2017: Comparative Information on OECD and Other Advanced and Emerging Economies. OECD Publishing, 2017.
[4] Kementerian Keuangan Republik Indonesia, Laporan Kinerja Instansi Pemerintah (LKjIP) Direktorat Jenderal Pajak Tahun 2023. Kementerian Keuangan RI, 2023.
[5] R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, 2nd ed. OTexts, 2018.
[6] G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time Series Analysis: Forecasting and Control, 5th ed. Wiley, 2015.
[7] C. Chatfield, The Analysis of Time Series: An Introduction, 6th ed. Chapman & Hall/CRC, 2004.
[8] A. Nugroho and D. Ispriyanti, “Peramalan penerimaan pajak daerah menggunakan metode ARIMA,” Jurnal Gaussian, vol. 8, no. 2, pp. 175–184, 2019.
[9] F. N. Aini and T. Haryanto, “Proyeksi jumlah wajib pajak aktif menggunakan metode ARIMA,” Jurnal Ekonomi dan Kebijakan Publik, vol. 11, no. 1, pp. 45–56, 2020.
[10] M. I. Rizki, T. Ammar, F. Fitriyani, and S. Fasya, “Peramalan Indeks Harga Saham PT Verena Multi Finance Tbk Dengan Metode Pemodelan ARIMA Dan ARCH-GARCH”, J Statistika, vol. 14, no. 1, pp. 11–23, Jul. 2021, doi: 10.36456/jstat.vol14.no1.a3774.
[11] F. N. Hayati, D. Nurlaily, and E. Pusporani, “Peramalan Data Ekspor Non Migas Provinsi Kalimantan Timur Menggunakan Univariate Time Series”, J Statistika, vol. 14, no. 2, pp. 59–66, Jan. 2022, doi: 10.36456/jstat.vol14.no2.a3858.
[12] G. E. P. Box and D. R. Cox, “An analysis of transformations,” Journal of the Royal Statistical Society: Series B (Methodological), vol. 26, no. 2, pp. 211–252, 1964.
[13] D. A. Dickey and W. A. Fuller, “Distribution of the estimators for autoregressive time series with a unit root,” Journal of the American Statistical Association, vol. 74, no. 366, pp. 427–431, 1979.
[14] G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis: Forecasting and Control. Wiley, 2008.
[15] P. J. Brockwell and R. A. Davis, Introduction to Time Series and Forecasting, 3rd ed. Springer, 2016.
[16] H. Akaike, “A new look at the statistical model identification,” IEEE Transactions on Automatic Control, vol. 19, no. 6, pp. 716–723, 1974.
