Prediksi Pajak Pertambahan Nilai pada Penyediaan Jasa dengan Metode Fuzzy Time Series Model Chen

Sri Lestari, Sherli Yurinanda


For companies, tax is a burden or fee that must be paid to the state as a taxpayer. The taxes that must be paid by the company can affect the profits earned. Therefore, efforts are needed to reduce or minimize the tax burden. Efforts to minimize the tax burden include tax planning. Tax planning that is often used by companies is tax planning on Value Added Tax (VAT), because all production activities are closely related to the VAT burden. Tax planning for VAT can be done by maximizing the amount of input VAT. To be able to identify the amount of input VAT in the next period, predictions can be made on the input VAT value. The uncertain VAT value and limited data collection make it possible to predict the VAT value using the fuzzy time series method. One model that can be used in fuzzy time series is the Chen model, because it has better accuracy values than the Song and Chissom models. Based on this research, it can be seen that the results of the prediction of the VAT value for the provision of services at PT Pertamina Hulu Rokan Zone 1, for the period July 2023 using the fuzzy time series Chen model method in second order obtained IDR 1,455,000,000 with a forecasting accuracy of 82.1%. In this way, PT PHR Zone 1 can maximize input VAT of IDR 1,455,000,000 so that the goal of minimizing the tax burden is achieved.


Chen; Fuzzy Time Series; Forecasting; VAT

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A. Salim and Haeruddin, Dasar-Dasar Perpajakan (Berdasarkan UU & Peraturan Perpajakan Indonesia). Sulawesi Tengah: LPP-Mitra Edukasi, 2019.

J. Gunawan, “Pengaruh Corporate Social Responsibility Dan Corporate Governance Terhadap Agresivitas Pajak,” Jurnal Akuntansi, vol. 21, no. 3, p. 425, Nov. 2017, doi: 10.24912/ja.v21i3.246.

H. Z. A. Tatnya, S. R. Imani, T. A. Wildany, N. A. Zahirah, and S. Wijaya, “Strategi Manajemen Perpajakan Pada Perusahaan Sektor Energi,” Journal of Law, Administration, and Social Science, vol. 3, no. 2, pp. 164–175, Jul. 2023, doi: 10.54957/jolas.v3i2.452.

C. A. Pohan, Optimizing Corporate Tax Management : Kajian Perpajakan dan Tax Planning-nya Terkini, 2nd ed. Jakarta: Bumi Aksara, 2018.

R. Purba, “Pengaruh Self Assesment System Dan Ketepatan Pelaporan Spt Terhadap Penerimaan Pajak Pertambahan Nilai Pada Kantor Pelayanan Pajak Pratama Medan Belawan,” Jurnal Mutiara Akuntansi, vol. 4, no. 1, pp. 76–82, 2019.

L. Hakim, S. Sabil, A. S. Lestiningsih, and D. P. Widodo, “Pengaruh Pemungutan Pajak Pertambahan Nilai Terhadap Laporan Keuangan,” Jurnal SIKAP (Sistem Informasi, Keuangan, Auditing Dan Perpajakan), vol. 4, no. 1, p. 1, Nov. 2019, doi: 10.32897/jsikap.v4i1.119.

D. H. Nurdiansyah, E. T. Ruchjana, and M. Alfarisi, “The Analysis of Tax Planning Implementation on Added Tax (Case Study at PT Toyotomo Indonesia and PT RKN Forge Indonesia),” Jurnal Ekonomi & Bisnis JAGADITHA, vol. 7, no. 1, pp. 18–23, Apr. 2020, doi: 10.22225/jj.7.1.1365.18-23.

D. C. Frechtling, Forcaseting Tourism Demand: Methods and Strategies, 1st ed. Oxford: Elsevier, 2001.

Y. Wang, Y. Lei, X. Fan, and Y. Wang, “Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoning,” Math Probl Eng, vol. 2016, pp. 1–12, 2016, doi: 10.1155/2016/5035160.

M. Muhammad, S. Wahyuningsih, and M. Siringoringo, “Peramalan Nilai Tukar Petani Subsektor Peternakan Menggunakan Fuzzy Time Series Lee,” Jambura Journal of Mathematics, vol. 3, no. 1, pp. 1–15, Jan. 2021, doi: 10.34312/jjom.v3i1.5940.

A. B. Elfajar, B. D. Setiawan, and C. Dewi, “Peramalan Jumlah Kunjungan Wisatawan Kota Batu Menggunakan Metode Time Invariant Fuzzy Time Series,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 1, no. 2, pp. 85–94, 2017.

W. Qiu, X. Liu, and H. Li, “A Generalized Method for Forecasting Based on Fuzzy Time Series,” Expert Syst Appl, vol. 38, no. 8, pp. 10446–10453, Aug. 2011, doi: 10.1016/j.eswa.2011.02.096.

S.-M. Chen, “Forecasting Enrollments Based on Fuzzy Time Series,” Fuzzy Sets Syst, vol. 81, no. 3, pp. 311–319, Aug. 1996, doi: 10.1016/0165-0114(95)00220-0.

S. Makridakis, Metode dan Aplikasi Peramalan, 2nd ed. Jakarta: Binarupa Aksara, 1999.

C. D. Lewis, Industrial and Business Forecasting Methods : Ap Pactical Guide to Exponential Smoothing and Curve Fitting. Boston: Buttersworth Scentific, 1982.

S.-M. Chen, “Forecasting Enrollments Based on High-Order Fuzzy Time Series,” Cybern Syst, vol. 33, no. 1, pp. 1–16, Jan. 2002, doi: 10.1080/019697202753306479.



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