Pemanfaatan Artificial Intelligence untuk Optimalisasi PNBP: Studi Kasus Bea Lelang pada Lelang Indonesia
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Published:
Jun 28, 2024
Keywords:
CNN, Deep Learning, Klasifikasi Gambar, Portal Lelang Indonesia, Tarif Bea Lelang
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Abstract
Misclassification of auction objects can result in an inaccuracy of the Auction Fee that is imposed, resulting in under/overpayment of government revenue, a decline in public reputation, and differences in auction fee data in SIMPONI and Portal Lelang Indonesia. These errors can be anticipaed by adding verification step by the Auctioneer. Meanwhile, the increase in the frequency of auctions is disproportionate to the number of Auctioneer, thus a mechanism that can assist the Auctioneer to do verification without adding additional work is needed. The authors propose the use of a Convolutional Neural Network to carry out the automatic classification of auction objects in the form of Buildings, Demolition, Cars, and Motorcycles. The dataset was obtained from the Portal Lelang Indonesia. The results of training and validation accuracy were 96.13% and 96.50%. The model is then applied to a dashboard for manual testing, and 100% accuracy results are obtained from all the images tested.
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How to Cite
Sujak, G. M. M., & Rofiq, H. N. (2024). Pemanfaatan Artificial Intelligence untuk Optimalisasi PNBP: Studi Kasus Bea Lelang pada Lelang Indonesia. Indonesian Treasury Review: Jurnal Perbendaharaan, Keuangan Negara Dan Kebijakan Publik, 9(2), 112–124. https://doi.org/10.33105/itrev.v9i2.873
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References
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Campesato, O. (2020). Artificial intelligence, machine learning, and deep learning. United States of America: Mercury Learning and Information.
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Geron, A (2019). Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. United States of America: O’ Riley Media.
Ertel, W., & Black, N. T. (2018). Introduction to artificial intelligence (2nd 2017 ed.). Springer International Publishing AG. https://doi.org/10.1007/978-3-319-58487-4
Harianto, R. A., Pranoto, Y. M., & Gunawan, T. P. (2021, April). Data augmentation and faster rcnn improve vehicle detection and recognition. In 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT) (pp. 128-133). IEEE.
Hasan, A. R. (2021). Artificial Intelligence (AI) in accounting & auditing: A Literature review. Open Journal of Business and Management, 10(1), 440-465.
Hemdan, E. E. D., Shouman, M. A., & Karar, M. E. (2020). Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv preprint arXiv:2003.11055.
Hilton, Marudut Sianturi, & Salman Faris. (2022). the influence of digital marketing and advertising on customer satisfaction with price as a moderating variable (case study of online shopping at shopee. International Journal of Applied Finance and Business Studies, 10(1), 47–53. https://doi.org/10.35335/ijafibs.v10i1.52
Hope, T., Resheff, Y. S., & Lieder, I. (2017). Learning tensorflow: A guide to building deep learning systems. United States of America: O'Reilly Media, Inc.
Ibrahim, D. M., Elshennawy, N. M., & Sarhan, A. M. (2021). Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases. Computers in biology and medicine, 132, 104348.
Ismael, S. A. A., Mohammed, A., & Hefny, H. (2020). An enhanced deep learning approach for brain cancer MRI images classification using residual networks. Artificial intelligence in medicine, 102, 101779.
Jakhar, D., & Kaur, I. (2020). Artificial intelligence, machine learning and deep learning: definitions and differences. Clinical and experimental dermatology, 45(1), 131-132. Kanani, P., & Padole, M. (2019). Deep learning to detect skin cancer using google colab. International Journal of Engineering and Advanced Technology Regular Issue, 8(6), 2176-2183.
Jiao, L., & Zhao, J. (2019). A survey on the new generation of deep learning in image processing. IEEE Access, 7, 172231-172263.
Karim, F. (2022). Optimalisasi pencatatan nikah terhadap fenomena perkawinan sirri di Kabupaten Boalem. Humantech: Jurnal Ilmiah Multidisiplin Indonesia, 1(7), 942-948.
Kazi, M. K., Eljack, F., & Mahdi, E. (2020). Predictive ANN models for varying filler content for cotton fiber/PVC composites based on experimental load displacement curves. Composite Structures, 254, 112885.
Kembuan, O., Rorimpandey, G. C., & Tengker, S. M. T. (2020, October). Convolutional neural network (CNN) for image classification of Indonesia sign language using Tensorflow. In 2020 2nd International Conference on Cybernetics and Intelligent System (ICORIS) (pp. 1-5). IEEE.
Kim, R. Y. (2020). The impact of COVID-19 on consumers: Preparing for digital sales. IEEE Engineering Management Review, 48(3), 212-218.
Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of emerging technologies in accounting, 14(1), 115-122.
Kothari, J. D. (2018). A case study of image classification based on deep learning using TensorFlow. International Journal of Innovative Research in Computer and Communication Engineering, 6(7), 3888-3892.
Krizanova, A., Lăzăroiu, G., Gajanova, L., Kliestikova, J., Nadanyiova, M., & Moravcikova, D. (2019). The effectiveness of marketing communication and importance of its evaluation in an online environment. Sustainability, 11(24), 7016
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.
Li, S. (2018). Application of recurrent neural networks in toxic comment classification (Doctoral dissertation, The University of California).
Lipovetsky, S. (2009). Pareto 80/20 law: derivation via random partitioning. International Journal of Mathematical Education in Science and Technology, 40(2), 271-277.
Ma, J., & Li, Q. (2005, June). The key factors influence customer purchase decision on retail web site. In Proceedings of ICSSSM'05. 2005 International Conference on Services Systems and Services Management, 2005. (Vol. 1, pp. 223-226). IEEE.
March, S. T., & Storey, V. C. (2008). Design science in the information systems discipline: an introduction to the special issue on design science research. MIS quarterly, 725-730.
McBee, M. P., Awan, O. A., Colucci, A. T., Ghobadi, C. W., Kadom, N., Kansagra, A. P., ... & Auffermann, W. F. (2018). Deep learning in radiology. Academic Radiology, 25(11), 1472-1480.
Mintz, Y., & Brodie, R. (2019). Introduction to artificial intelligence in medicine. Minimally Invasive Therapy & Allied Technologies, 28(2), 73-81.
Ottoni, A. L. C., de Amorim, R. M., Novo, M. S., & Costa, D. B. (2023). Tuning of data augmentation hyperparameters in deep learning to building construction image classification with small datasets. International Journal of Machine Learning and Cybernetics, 14(1), 171-186.
O'Shea, K., & Nash, R. (2015). An introduction to convolutional neural networks. Ithaca: Cornell University Library.
Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of management information systems, 24(3), 45-77.
Phung, V. H., & Rhee, E. J. (2018). A deep learning approach for classification of cloud image patches on small datasets. Journal of information and communication convergence engineering, 16(3), 173-178.
Phung, V. H., & Rhee, E. J. (2019). A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets. Applied Sciences, 9(21), 4500.
Plant, R. (2011). An introduction to artificial intelligence. In 32nd Aerospace Sciences Meeting and Exhibit (p. 294).
Rahmatyah, S. (2022). Analisis Kualitas Pelayanan Pajak Kendaraan Bermotor (Pkb) Dan Surat Tanda Nomor Kendaraan (Stnk) Pada SAMSAT Wilayah Kota Kendari. Sibatik Journal: Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan, 1(11), 2371-2382.
Samraj, A., Sowmiya, D., Deepthisri, K. A., & Oviya, R. (2020, November). Food genre classification from food images by deep neural network with tensorflow and Keras. In 2020 Seventh nternational Conference on Information Technology Trends (ITT) (pp. 228-231). IEEE.
Shahinfar, S., Meek, P., & Falzon, G. (2020). “How many images do I need?” Understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoring. Ecological Informatics, 57, 101085.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1), 1929-1958.
Supriyadi, E. I., & Asih, D. B. (2020). Implementasi Artificial Intelligence (Ai) Di Bidang Administrasi Publik Pada Era Revolusi Industri 4.0. Jurnal RASI, 2(2), 12-22.
Supriyanto, A., Benty, D. D. N., & Rochmawati. (2019). Kaizen: Quality Improvement Innovation Higher Education in the Industrials Revolution 4.0. The 4th International Conference on Education and Management (COEMA 2019), 108–113.
Sutarto. (2015). Manajemen Mutu Terpadu (MMT-TQM) - teori dan penerapan di lembaga pendidikan. UNY Press.
van Noordt, C., & Misuraca, G. (2022). Artificial intelligence for the public sector: results of landscaping the use of AI in government across the European Union. Government Information Quarterly, 39(3), 101714.
Venable, J., Pries-Heje, J., & Baskerville, R. (2012). A comprehensive framework for evaluation in design science research. In Design Science Research in Information Systems. Advances in Theory and Practice: 7th International Conference, DESRIST 2012, Las Vegas, NV, USA, May 14-15, 2012. Proceedings 7 (pp. 423-438). Springer Berlin Heidelberg.
Wen, Q., Sun, L., Yang, F., Song, X., Gao, J., Wang, X., & Xu, H. (2022). Time series data augmentation for deep learning: A survey. Ithaca: Cornell University Library, arXiv.org. doi:https://doi.org/10.24963/ijcai.2021/631.
Wicaksono, W. A. (2023). Implementasi continuous improvement pada aktivitas belajar di pondok pesantren. Evaluasi: Jurnal Manajemen Pendidikan Islam, 7(1), 22-33.
Widiyanto, I., & Prasilowati, S. L. (2015). Perilaku pembelian melalui internet. Jurnal manajemen dan kewirausahaan, 17(2), 109-122.
Wijayati, P. H., Suyata, & Sumarno. (2013). Model Evaluasi Pembelajaran Berbasis Kaizen Di Sekolah Menengah Atas. Jurnal Penelitian Dan Evaluasi Pendidikan, 17(2), 318–332. https://doi.org/https://doi.org/10.21831/pep.v17i2.1703
Wisnudhanti, K., & Candra, F. (2020, October). image classification of pandawa figures using convolutional neural network on raspberry Pi 4. In Journal of Physics: Conference Series (Vol. 1655, No. 1, p. 012103). IOP Publishing.
Zaccone, G., & Karim, M. R. (2018). Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with python. Birmingham, United States of Kingdom: Packt Publishing Ltd.
Republik Indonesia (2009). Undang Undang Republik Indonesia Nomor 25 Tahun 2009 tentang Pelayanan Publik
Republik Indonesia (2020). Peraturan Pemerintah Nomor 62 Tahun 2020 tentang Perubahan atas Peraturan Pemerintah Nomor 3 Tahun 2018 tentang Jenis dan Tarif atas Jenis Penerimaan Negara Bukan Pajak yang berlaku pada Kementerian Keuangan
Ahn, M. J., & Chen, Y. C. (2022). Digital transformation toward AI-augmented public administration: The perception of government employees and the willingness to use AI in government. Government Information Quarterly, 39(2), 101664.
Askary, S., Abu-Ghazaleh, N., & Tahat, Y. A. (2018). Artificial intelligence and reliability of accounting information. In Challenges and Opportunities in the Digital Era: 17th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2018, Kuwait City, Kuwait, October 30–November 1, 2018, Proceedings 17 (pp. 315-324). Springer International Publishing.
Barz, B., & Denzler, J. (2020). deep learning on small datasets without pre-training using cosine loss. Paper presented at The IEEE Winter Conference on Applications of Computer Vision.
Bente, G., Baptist, O., & Leuschner, H. (2012). To buy or not to buy: Influence of seller photos and reputation on buyer trust and purchase behavior. International Journal of Human-Computer Studies, 70(1), 1-13.
Campesato, O. (2020). Artificial intelligence, machine learning, and deep learning. United States of America: Mercury Learning and Information.
Chhabra, M., & Kumar, R. (2022). an advanced vgg16 architecture-based deep learning model to detect pneumonia from medical images. In Emergent Converging Technologies and Biomedical Systems: Select Proceedings of ETBS 2021 (pp. 457-471). Singapore: Springer Singapore.
Diaz, O., Kushibar, K., Osuala, R., Linardos, A., Garrucho, L., Igual, L., ... & Lekadir, K. (2021). Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools. Physica medica, 83, 25-37.
Dinarjito, A. (2017). optimalisasi penerimaan negara bukan pajak pada lembaga penyiaran publik televisi republik indonesia (LPP TVRI). Substansi: Sumber Artikel Akuntansi Auditing dan Keuangan Vokasi, 1(1), 107-122.
Direktorat Jenderal Kekayaan Negara (2020). 112 Tahun Lelang di Indonesia: Eksistensi, Peran, dan Pengembangannya di Era Digital (Media Kekayaan Negara Ed. 34 Tahun XI/2020 PP. 18-32). Jakarta: Direktorat Jenderal Kekayaan Negara.
Farid, I., Reksoprodjo, A. H., & Suhirwan, S. (2023). pemanfaatan artificial intelligence dalam pertahanan siber. NUSANTARA: Jurnal Ilmu Pengetahuan Sosial, 10(2), 779-788.
Fauzan, I. (2020). Artificial Intelligence (AI) pada proses pengawasan dan pengendalian kepegawaian–sebuah eksplorasi konsep setelah masa pandemi berakhir. Civil Service Journal, 14(1 Juni), 31-42.
Geron, A (2019). Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow. United States of America: O’ Riley Media.
Ertel, W., & Black, N. T. (2018). Introduction to artificial intelligence (2nd 2017 ed.). Springer International Publishing AG. https://doi.org/10.1007/978-3-319-58487-4
Harianto, R. A., Pranoto, Y. M., & Gunawan, T. P. (2021, April). Data augmentation and faster rcnn improve vehicle detection and recognition. In 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT) (pp. 128-133). IEEE.
Hasan, A. R. (2021). Artificial Intelligence (AI) in accounting & auditing: A Literature review. Open Journal of Business and Management, 10(1), 440-465.
Hemdan, E. E. D., Shouman, M. A., & Karar, M. E. (2020). Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv preprint arXiv:2003.11055.
Hilton, Marudut Sianturi, & Salman Faris. (2022). the influence of digital marketing and advertising on customer satisfaction with price as a moderating variable (case study of online shopping at shopee. International Journal of Applied Finance and Business Studies, 10(1), 47–53. https://doi.org/10.35335/ijafibs.v10i1.52
Hope, T., Resheff, Y. S., & Lieder, I. (2017). Learning tensorflow: A guide to building deep learning systems. United States of America: O'Reilly Media, Inc.
Ibrahim, D. M., Elshennawy, N. M., & Sarhan, A. M. (2021). Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases. Computers in biology and medicine, 132, 104348.
Ismael, S. A. A., Mohammed, A., & Hefny, H. (2020). An enhanced deep learning approach for brain cancer MRI images classification using residual networks. Artificial intelligence in medicine, 102, 101779.
Jakhar, D., & Kaur, I. (2020). Artificial intelligence, machine learning and deep learning: definitions and differences. Clinical and experimental dermatology, 45(1), 131-132. Kanani, P., & Padole, M. (2019). Deep learning to detect skin cancer using google colab. International Journal of Engineering and Advanced Technology Regular Issue, 8(6), 2176-2183.
Jiao, L., & Zhao, J. (2019). A survey on the new generation of deep learning in image processing. IEEE Access, 7, 172231-172263.
Karim, F. (2022). Optimalisasi pencatatan nikah terhadap fenomena perkawinan sirri di Kabupaten Boalem. Humantech: Jurnal Ilmiah Multidisiplin Indonesia, 1(7), 942-948.
Kazi, M. K., Eljack, F., & Mahdi, E. (2020). Predictive ANN models for varying filler content for cotton fiber/PVC composites based on experimental load displacement curves. Composite Structures, 254, 112885.
Kembuan, O., Rorimpandey, G. C., & Tengker, S. M. T. (2020, October). Convolutional neural network (CNN) for image classification of Indonesia sign language using Tensorflow. In 2020 2nd International Conference on Cybernetics and Intelligent System (ICORIS) (pp. 1-5). IEEE.
Kim, R. Y. (2020). The impact of COVID-19 on consumers: Preparing for digital sales. IEEE Engineering Management Review, 48(3), 212-218.
Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of emerging technologies in accounting, 14(1), 115-122.
Kothari, J. D. (2018). A case study of image classification based on deep learning using TensorFlow. International Journal of Innovative Research in Computer and Communication Engineering, 6(7), 3888-3892.
Krizanova, A., Lăzăroiu, G., Gajanova, L., Kliestikova, J., Nadanyiova, M., & Moravcikova, D. (2019). The effectiveness of marketing communication and importance of its evaluation in an online environment. Sustainability, 11(24), 7016
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.
Li, S. (2018). Application of recurrent neural networks in toxic comment classification (Doctoral dissertation, The University of California).
Lipovetsky, S. (2009). Pareto 80/20 law: derivation via random partitioning. International Journal of Mathematical Education in Science and Technology, 40(2), 271-277.
Ma, J., & Li, Q. (2005, June). The key factors influence customer purchase decision on retail web site. In Proceedings of ICSSSM'05. 2005 International Conference on Services Systems and Services Management, 2005. (Vol. 1, pp. 223-226). IEEE.
March, S. T., & Storey, V. C. (2008). Design science in the information systems discipline: an introduction to the special issue on design science research. MIS quarterly, 725-730.
McBee, M. P., Awan, O. A., Colucci, A. T., Ghobadi, C. W., Kadom, N., Kansagra, A. P., ... & Auffermann, W. F. (2018). Deep learning in radiology. Academic Radiology, 25(11), 1472-1480.
Mintz, Y., & Brodie, R. (2019). Introduction to artificial intelligence in medicine. Minimally Invasive Therapy & Allied Technologies, 28(2), 73-81.
Ottoni, A. L. C., de Amorim, R. M., Novo, M. S., & Costa, D. B. (2023). Tuning of data augmentation hyperparameters in deep learning to building construction image classification with small datasets. International Journal of Machine Learning and Cybernetics, 14(1), 171-186.
O'Shea, K., & Nash, R. (2015). An introduction to convolutional neural networks. Ithaca: Cornell University Library.
Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of management information systems, 24(3), 45-77.
Phung, V. H., & Rhee, E. J. (2018). A deep learning approach for classification of cloud image patches on small datasets. Journal of information and communication convergence engineering, 16(3), 173-178.
Phung, V. H., & Rhee, E. J. (2019). A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets. Applied Sciences, 9(21), 4500.
Plant, R. (2011). An introduction to artificial intelligence. In 32nd Aerospace Sciences Meeting and Exhibit (p. 294).
Rahmatyah, S. (2022). Analisis Kualitas Pelayanan Pajak Kendaraan Bermotor (Pkb) Dan Surat Tanda Nomor Kendaraan (Stnk) Pada SAMSAT Wilayah Kota Kendari. Sibatik Journal: Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan, 1(11), 2371-2382.
Samraj, A., Sowmiya, D., Deepthisri, K. A., & Oviya, R. (2020, November). Food genre classification from food images by deep neural network with tensorflow and Keras. In 2020 Seventh nternational Conference on Information Technology Trends (ITT) (pp. 228-231). IEEE.
Shahinfar, S., Meek, P., & Falzon, G. (2020). “How many images do I need?” Understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoring. Ecological Informatics, 57, 101085.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1), 1929-1958.
Supriyadi, E. I., & Asih, D. B. (2020). Implementasi Artificial Intelligence (Ai) Di Bidang Administrasi Publik Pada Era Revolusi Industri 4.0. Jurnal RASI, 2(2), 12-22.
Supriyanto, A., Benty, D. D. N., & Rochmawati. (2019). Kaizen: Quality Improvement Innovation Higher Education in the Industrials Revolution 4.0. The 4th International Conference on Education and Management (COEMA 2019), 108–113.
Sutarto. (2015). Manajemen Mutu Terpadu (MMT-TQM) - teori dan penerapan di lembaga pendidikan. UNY Press.
van Noordt, C., & Misuraca, G. (2022). Artificial intelligence for the public sector: results of landscaping the use of AI in government across the European Union. Government Information Quarterly, 39(3), 101714.
Venable, J., Pries-Heje, J., & Baskerville, R. (2012). A comprehensive framework for evaluation in design science research. In Design Science Research in Information Systems. Advances in Theory and Practice: 7th International Conference, DESRIST 2012, Las Vegas, NV, USA, May 14-15, 2012. Proceedings 7 (pp. 423-438). Springer Berlin Heidelberg.
Wen, Q., Sun, L., Yang, F., Song, X., Gao, J., Wang, X., & Xu, H. (2022). Time series data augmentation for deep learning: A survey. Ithaca: Cornell University Library, arXiv.org. doi:https://doi.org/10.24963/ijcai.2021/631.
Wicaksono, W. A. (2023). Implementasi continuous improvement pada aktivitas belajar di pondok pesantren. Evaluasi: Jurnal Manajemen Pendidikan Islam, 7(1), 22-33.
Widiyanto, I., & Prasilowati, S. L. (2015). Perilaku pembelian melalui internet. Jurnal manajemen dan kewirausahaan, 17(2), 109-122.
Wijayati, P. H., Suyata, & Sumarno. (2013). Model Evaluasi Pembelajaran Berbasis Kaizen Di Sekolah Menengah Atas. Jurnal Penelitian Dan Evaluasi Pendidikan, 17(2), 318–332. https://doi.org/https://doi.org/10.21831/pep.v17i2.1703
Wisnudhanti, K., & Candra, F. (2020, October). image classification of pandawa figures using convolutional neural network on raspberry Pi 4. In Journal of Physics: Conference Series (Vol. 1655, No. 1, p. 012103). IOP Publishing.
Zaccone, G., & Karim, M. R. (2018). Deep Learning with TensorFlow: Explore neural networks and build intelligent systems with python. Birmingham, United States of Kingdom: Packt Publishing Ltd.
Republik Indonesia (2009). Undang Undang Republik Indonesia Nomor 25 Tahun 2009 tentang Pelayanan Publik
Republik Indonesia (2020). Peraturan Pemerintah Nomor 62 Tahun 2020 tentang Perubahan atas Peraturan Pemerintah Nomor 3 Tahun 2018 tentang Jenis dan Tarif atas Jenis Penerimaan Negara Bukan Pajak yang berlaku pada Kementerian Keuangan