Prediksi Keikutsertaan Pelaku Usaha dalam Pemanfaatan Insentif Pajak dengan Artificial Neural Network

Arifin Rosid; Galih Ardin; Tri Bayu Sanjaya

doi:10.52813/jei.v11i2.178

Arifin Rosid Direktorat Jenderal Pajak, Kementerian Keuangan Republik Indonesia & Fakultas Ekonomi dan Bisnis, Universitas Indonesia
Galih Ardin Direktorat Jenderal Pajak, Kementerian Keuangan Republik Indonesia
Tri Bayu Sanjaya Direktorat Jenderal Pajak, Kementerian Keuangan Republik Indonesia

DOI: https://doi.org/10.52813/jei.v11i2.178

Keywords: Indonesia, Covid-19, insentif pajak, Artificial Neural Network

Abstract

Pemberian insentif pajak adalah salah satu kebijakan fiskal yang penting selama masa pandemi Covid-19. Karakteristik dari pelaku usaha yang terkait dengan pemanfaatan insentif adalah informasi penting di dalam perumusan kebijakan. Studi ini menawarkan pendekatan Artificial Neural Network (ANN) untuk memprediksi keikutsertaan pelaku usaha dalam insentif pajak berdasarkan karakteristik yang dimiliki. Model ANN dalam studi ini menggunakan data empiris jumlah pekerja, pangsa pasar utama, besaran omzet tahunan, sifat usaha utama, dan sumber utama pasokan dari 12.361 pelaku usaha hasil survei. Pendekatan ANN dalam studi ini memprediksi dengan tingkat akurasi sekitar 70%. Hasil studi ini menunjukkan bahwa jumlah pekerja, omzet tahunan, dan pangsa pasar utama adalah tiga variabel terpenting yang menentukan keikutsertaan pelaku usaha dalam pemanfaatan insentif pajak.

References

ADB (2020). Asian Development Outlook (ADO) 2020: What Drives innovation in Asia?. Asian Development Bank. doi: http://dx.doi.org/10.22617/FLS200119-3.

Alberola, E., Arslan, Y., Cheng, G., & Moessner, R. (2021). Fiscal response to the COVID-19 crisis in advanced and emerging market economies. Pacific Economic Review, 26(4), 459-468. doi: https://doi.org/10.1111/1468-0106.12370.

Aryadoust, V., & Baghaei, P. (2016). Does EFL readers’ lexical and grammatical knowledge predict their reading ability? Insights from a perceptron artificial neural network study. Educational Assessment, 21(2), 135-156. doi: https://doi.org/10.1080/10627197.2016.1166343.

Babbie, E. R. (2010). The pracfice of social research (12th edition). Wadsworth Cengage, Belmont, CA.

BPS. (2020a). Laju pertumbuhan PDB menurut pengeluaran (persen) 2020. Badan Pusat Statistik. Diakses 22 Juli 2021 dari https://www.bps.go.id/indicator/169/108/2/-seri-2010-laju-pertumbuhan-pdb-menurut-pengeluaran.html.

BPS. (2020b). Analisis hasil survei dampak Covid-19 terhadap pelaku usaha. Badan Pusat Statistik. Diakses 21 Juli 2021 dari http://data.bimakota.go.id/sites/default/files/publikasi/Analisis%20Hasil%20Survei%20Dampak%20Covid-19%20Terhadap%20Pelaku%20Usaha-min.pdf.

Bahrammirzaee, A. (2010). A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems. Neural Computing and Applications, 19(8), 1165-1195. doi: https://doi.org/10.1007/s00521-010-0362-z.

Bekesiene, S., Smaliukiene, R., & Vaicaitiene, R. (2021). Using artificial neural networks in predicting the level of stress among military conscripts. Mathematics, 9(6), 626. doi: https://doi.org/10.3390/math9060626.

Busom, I., Corchuelo, B., & Mart´ınez-Ros, E. (2017). Participation inertia in R&D tax incentive and subsidy programs. Small Business Economics, 48(1), 153-177. doi: https://doi.org/10.1007/s11187-016-9770-5.

Chen, J. H., Su, M. C., Chen, C. Y., Hsu, F. H., &Wu, C. C. (2011). Application of neural networks for detecting erroneous tax reports from construction companies. Automation in Construction, 20(7), 935-939. doi: https://doi.org/10.1016/j.autcon.2011.03.011.

Collier, R., Pirlot, A., & Vella, J. (2020). COVID-19 and fiscal policies: Tax policy and the COVID-19 crisis. Intertax, 48(8/9), 794-804. doi: https://doi.org/10.54648/taxi2020078.

Denton, J. W., Sayeed, L., Perkins, N. D., & Moorman, A. H. (1995). Neural networks to classify employees for tax purposes. Accounting, Management and Information Technologies, 5(2), 123-138. doi: https://doi.org/10.1016/0959-8022(95)00008-W.

Devereux, M. P., Guceri, I., Simmler, M., & Tam, E. H. (2020). Discretionary fiscal responses to the COVID-19 pandemic. Oxford Review of Economic Policy, 36(Supplement ˙1), S225-S241. doi: https://doi.org/10.1093/oxrep/graa019.

DJP. (2021). Buku statistik perpajakan 2020. Direktorat Jenderal Pajak, Jakarta.

Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. doi: https://doi.org/10.1016/j.patrec.2005.10.010.

Haykin, S. (1999). Neural networks: A comprehensive foundation (2nd edition). Macmillan College Publishing, New York.

[IBM] International Business Machines. (2017). IBM SPSS Decision Trees 25. IBM Corporation. Diakses 15 Juni 2021 dari https://www.ibm.com/docs/en/SSLVMB25.0.0/pdf/en/IBM SPSS Decision Trees.pdf.

[IBM] International Business Machines. (2019). IBM SPSS Neural Networks 26. IBM Corporation. Diakses 21 Juli 2021 dari https://www.ibm.com/docs/SSLVMB 26.0.0/pdf/en/IBM SPSS Neural Network.pdf.

[IBM] International Business Machines. (2021). IBM SPSS Neural Networks 28. IBM Corporation. Diakses 14 Juli 2022 dari https://www.ibm.com/docs/en/SSLVMB 28.0.0/pdf/IBM SPSS Neural Network.pdf.

Jang, S. B. (2019). A design of a tax prediction system based on artificial neural network. In 2019 International Conference on Platform Technology and Service (PlatCon) (pp. 1-4). IEEE. doi: 10.1109/PlatCon.2019.8669416.

Jupri, M., & Sarno, R. (2018). Taxpayer compliance classification using C4. 5, SVM, KNN, Naive Bayes and MLP. In 2018 International Conference on Information and Communications Technology (ICOIACT) (pp. 297-303). IEEE. doi: 10.1109/ICOIACT.2018.8350710.

Kementerian Keuangan RI. (2021). Laporan Keuangan Pemerintah Pusat Tahun 2020 (Audited). Diakses 21 Juli 2021 dari https://klc2.kemenkeu.go.id/kms/knowledge/laporan-keuangan-pemerintah-pusat-tahun-2020-audited-443ee9d2/detail/.

Lin, C. H., Lin, I. C., Wu, C. H., Yang, Y. C., & Roan, J. (2012). The application of decision tree and artificial neural network to income tax audit: the examples of profit-seeking enterprise income tax and individual income tax in Taiwan. Journal of the Chinese Institute of Engineers, 35(4), 401-411. doi: https://doi.org/10.1080/02533839.2012.655901.

Murorunkwere, B. F., Tuyishimire, O., Haughton, D., & Nzabanita, J. (2022). Fraud detection using neural networks: a case study of income tax. Future Internet, 14(6), 168. doi: https://doi.org/10.3390/fi14060168.

Neuman,W. L. (2014). Social research methods: qualitative and quantitative approaches (7th edition), Pearson new international edition. Pearson Education Limited.

OECD. (2020a). Tax and fiscal policy in response to the Coronavirus crisis: strengthening confidence and resilience. Organisation for Economic Co-operation and Development. Diakses 21 Juli 2021 dari https://www.oecd.org/tax/tax-policy/tax-and-fiscal-policy-in-response-to-the-coronavirus-crisis-strengtheningconfidence-and-resilience.htm.

OECD. (2020b). Revenue statistics in Asian and Pacific economies 2020. Organisation for Economic Co-operation and Development. doi: https://doi.org/10.1787/d47d0ae3-en.

Perez Lopez, C., Delgado Rodrıguez, M. J., & de Lucas Santos, S. (2019). Tax fraud detection through neural networks: An application using a sample of personal income taxpayers. Future Internet, 11(4), 86. doi: https://doi.org/10.3390/fi11040086.

Ripley, B. D. (1996). Pattern recognition and neural networks. Cambridge University Press.

Sanchez-Serrano, J. R., Alaminos, D., Garcıa-Lagos, F., & Callejon-Gil, A. M. (2020). Predicting audit opinion in consolidated financial statements with artificial neural networks. Mathematics, 8(8), 1288. doi: https://doi.org/10.3390/math8081288.

Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763-1768. doi: https://doi.org/10.1213/ANE.0000000000002864.

Sen, T. K. (2021). Exit strategy to ease or eliminate tax responses to the COVID-19 pandemic. The Governance Brief, 43. Asian Development Bank. doi: http://dx.doi.org/10.22617/BRF210142.

Weber, T. O., Fooken, J., & Herrmann, B. (2014). Behavioural economics and taxation. Taxation Papers, 41. European Commission. Diakses 21 Juli 2021 dari https://taxation-customs.ec.europa.eu/system/files/2016-09/taxation paper 41.pdf.

World Bank. (2021). A review of fiscal policy responses to COVID-19. Macroeconomics, trade and investment: equitable growth, finance & institutions insight. Diakses 19 Agustus 2022 dari https://openknowledge.worldbank.org/handle/10986/35904.

Zhang, G., Hu, M. Y., Patuwo, B. E., & Indro, D. C. (1999). Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European Journal of Operational Research, 116(1), 16-32. doi: https://doi.org/10.1016/S0377-2217(98)00051-4.