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All Journal Jurnal Pengabdian kepada Masyarakat Nusantara Akuntansi: Jurnal Riset Ilmu Akuntansi
Adrian Devano
Universitas Brawijaya
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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Pembaruan Sistem Inti Administrasi Perpajakan (PSIAP) & NPWP 16 Digit Devy Pusposari; Hendi Subandi; Adrian Devano; Lintang Ayu Dyah Choirunnisa
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 4 No. 5 (2023): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN) SPECIAL ISSUE
Publisher : Cv. Utility Project Solution

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Abstract

Rendahnya tingkat literasi perpajakan yang dimiliki oleh masyarakat tentunya akan berpengaruh signifikan terhadap keberlangsungan pajak negara. Salah satu upaya yang telah dilakukan pemerintah untuk terus meningkatkan penerimaan pajak adalah melakukan berbagai reformasi sistem, salah satunya adalah PSIAP. Minimnya pemahaman masyarakat mengenai PSIAP, tentu saja akan menghambat pengoptimalan sasaran manfaat dari adanya pembaharuan SIAP, termasuk bagi masyarakat itu sendiri yang sejatinya dapat memperoleh berbagai macam manfaat dari Proyek PSIAP. Sosialisasi Taxation Update Series dengan tujuan memberikan edukasi dan pemahaman bagi masyarakat dan Wajib Pajak terkait berbagai reformasi perpajakan. Manfaat yang diharapkan dari kegiatan ini adalah meningkatnya pemahaman literasi perpajakan dan berimplikasi terhadap lebih banyak Wajib Pajak yang sadar dan patuh untuk melaksanakan kewajiban perpajakan. Luaran pengabdian ini adalah Hak Kekayaan Intelektual (HKI).
Deteksi Potensi Penggelapan Pajak PPh Pasal 21 pada Pengisian SPT Tahunan Wajib Pajak Orang Pribadi melalui Artificial Neural Network Adrian Devano; Ayu Fury Puspita
Akuntansi Vol. 5 No. 3 (2026): September : Jurnal Riset Ilmu Akuntansi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/akuntansi.v5i3.3331

Abstract

This study aims to detect potential tax evasion of Article 21 Income Tax in filling out the Annual Tax Return of individual taxpayers using artificial intelligence such as machine learning in the form of an artificial neural network (ANN) in the form of a multilayer perceptron (MLP). The number of data is 3,000,000 which is built from aggregate data of individual taxpayers provided by the Directorate of Tax Data and Information using the Monte Carlo method which is then separated into three (3) data frames, namely 1770, 1770S, 1770SS through data pre-processing. The multilayer perceptron (MLP) model is built using the backpropagation algorithm with a supervised learning approach. The activation functions used in the hidden layer and output layer are Tanh and Sigmoid, respectively. Dimension reductionality uses Principal Component Analysis (PCA) so that the model can work efficiently. The results show that there is potential for tax evasion of Article 21 Income Tax detected by the multilayer perceptron (MLP) model on Gross Income, Net Income, Taxable Income, Assets, Debts, and Reporting Status. Evaluation with cross-validation shows that the model detects with an average precision of 83.27% and an average accuracy of 75.19% and an average ROC-AUC evaluation of 76% on the three data frames which implies that the artificial neural network (ANN) model is adequate because it has successfully passed the threshold of 0.5 in detecting potential tax evasion of Article 21 Income Tax in Personal Income Tax Return in Indonesia.
Co-Authors Ayu Fury Puspita Devy Pusposari Hendi Subandi Lintang Ayu Dyah Choirunnisa