Digital Forensic Approaches for Counterfeit Money Detection: A Compratie of KNN, Logistic Regression, and SVM Classifiers
Keywords:
Digital Forensic, Counterfeit Money Detection, Machine Learning, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbors (KNN), Image Preprocessing, Gray Filter, Blur Filter, Edge Detection, Confusing Matrix Evaluatio
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Abstract view : 150 timesAbstract
Counterfeit currency presents a substantial risk to economic stability and financial security, necessitating efficient and dependable detection techniques in both forensic and practical contexts. This research examines digital forensic methodologies for the identification of counterfeit banknotes employing three machine learning classifiers: K-Nearest Neighbors (KNN), Logistic Regression, and Support Vector Machine (SVM). A dataset was generated by photographing authentic and counterfeit Indonesian banknotes using a mobile phone camera, thereafter undergoing preprocessing and augmentation to enhance resilience. To improve classification performance, three image preprocessing techniques—grayscale filtering, edge detection, and blurring—were employed. The models were assessed based on accuracy, precision, recall, and F1-score obtained from confusion matrix analysis. The experimental findings demonstrated that SVM and Logistic Regression consistently surpassed KNN in all settings, with SVM attaining the best overall accuracy of 0.997 under gray and blur filtering. Logistic Regression exhibited high reliability, with an accuracy of 0.994–0.997 using gray and blur filters. KNN, although originally less successful, showed significant enhancement when integrated with blur filtering, attaining an accuracy of 0.973. Conversely, edge detection was found to be detrimental to the performance of all tested models.
References
Affandy, R., & Yusuf, H. (2025). Tindak Pidana Pengedaran Uang Palsu : Analisis Yuridis , Dampak Ekonomi , Dan Strategi Penanggulangan Dalam Rangka Menjaga Stabilitas Sistem Keuangan Nasional The Criminal Act of Circulating Counterfeit Money : Legal Analysis , Economic Impact , and Mitig. Jurnal Intelek Dan Cenddikiawan Nasional, 1(6), 10660–10668. https://jicnusantara.com/index.php/jicn
Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. American Statistician, 46(3), 175–185. https://doi.org/10.1080/00031305.1992.10475879
Anand, S., & Priya, L. (2019). Digital Image Fundamentals. In A Guide for Machine Vision in Quality Control. https://doi.org/10.1201/9781003002826-2
Biosa, G., Giurghita, D., Alladio, E., Vincenti, M., & Neocleous, T. (2020). Evaluation of Forensic Data Using Logistic Regression-Based Classification Methods and an R Shiny Implementation. Frontiers in Chemistry, 8(October). https://doi.org/10.3389/fchem.2020.00738
Boateng, E. Y., Otoo, J., & Abaye, D. A. (2020). Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review. Journal of Data Analysis and Information Processing, 08(04), 341–357. https://doi.org/10.4236/jdaip.2020.84020
Budiwitjaksono, G. S., Trisnaningsih, S., Susilowati, E., & Habibie. (2025). Akuntansi Forensik (Memahami Fraud dan Investigasi Keuangan) (Issue August). https://www.researchgate.net/publication/394470990
Canny, J. (1986). A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6), 679–698. https://doi.org/10.1109/TPAMI.1986.4767851
Demircioğlu, A. (2022). The effect of preprocessing filters on predictive performance in radiomics. European Radiology Experimental, 6(1). https://doi.org/10.1186/s41747-022-00294-w
Denil, M., & Trappenberg, T. (2011). A Characterization of the Combined Effects of Overlap and Imbalance on the SVM Classifier. ResearchGate, September 2011. http://arxiv.org/abs/1109.3532
Fernández-Delgado, M., Cernadas, E., Barro, S., & Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems? Journal of Machine Learning Research, 15(October), 3133–3181.
Gopane, S., & Kotecha, R. (2020). Indian Counterfeit Banknote Detection Using Support Vector Machine. SSRN Electronic Journal, March. https://doi.org/10.2139/ssrn.3568724
Hantono, B. S., Alfarozi, S. A. I., Pratama, A. R., Rizqi, A. A. A., Mustika, I. W., Riasetiawan, M., & Asih, A. M. S. (2023). Enhancing Counterfeit Detection with Multi-Features on Secure 2D Grayscale Codes. Computers, 12(9), 1–14. https://doi.org/10.3390/computers12090183
Harsani, P., Maulana Muhammad, & Negara, T. (2024). Identifikasi Citra untuk Membedakan Uang Asli dan Palsu Menggunakan Algoritma Convolutional Neural Network (CNN). JST (Jurnal Sains Dan Teknologi), 13(2), 328–337. https://doi.org/10.23887/jstundiksha.v13i2.83416
Hasanah, nur Y., & Fatah, Z. (2024). Gudang Jurnal Multidisiplin Ilmu Deteksi Keaslian Uang Kertas Berdasarkan Citra Digital Dengan Menggunakan Teachable Machine Learning. Gudang Jurnal Multidisiplin Ilmu, 2(12), 44–50. https://doi.org/https://doi.org/10.59435/gjmi.v2i12.1111
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415
Lee, J. W., Hong, H. G., Kim, K. W., & Park, K. R. (2017). A survey on banknote recognition methods by various sensors. Sensors (Switzerland), 17(2). https://doi.org/10.3390/s17020313
Miller, C. M. (2023). A survey of prosecutors and investigators using digital evidence: A starting point. Forensic Science International: Synergy, 6(November 2022). https://doi.org/10.1016/j.fsisyn.2022.100296
Mishra, A. K., & Essop, M. H. (2019). Low-cost spectrogram based counterfeit medicine detection. AirXiv, 600. http://arxiv.org/abs/1904.07152
Pham, T. D., Lee, Y. W., Park, C., & Park, K. R. (2022). Deep Learning-Based Detection of Fake Multinational Banknotes in a Cross-Dataset Environment Utilizing Smartphone Cameras for Assisting Visually Impaired Individuals. Mathematics, 10(9). https://doi.org/10.3390/math10091616
Prasath, V. B. S., Alfeilat, H. A. A., Hassanat, A. B. A., Lasassmeh, O., Tarawneh, A. S., Alhasanat, M. B., & Salman, H. S. E. (2019). Distance and Similarity Measures Effect on the Performance of K-Nearest Neighbor Classifier -- A Review. AirXiv, 1–39. https://doi.org/10.1089/big.2018.0175
Rick Nie. (2023). Comparative Analysis Of Machine Learning Based Bank Note Authentication Through Variable Selection. December 20, 2023, 1–12. https://nhsjs.com/2023/comparative-analysis-of-machine-learning-based-bank-note-authentication-through-variable-selection
Roy, V., Mishra, G., Mannadiar, R., & Patil, S. (2019). Fake Currency Detection using Image Processing. International Journal of Computer Science and Mobile Computing, 8(4), 88–93. https://doi.org/10.1109/ISCS61804.2024.10581207
Sharan, V., & Kaur, A. (2019). Detection of counterfeit indian currency note osing image processing. International Journal of Engineering and Advanced Technology, 9(1), 2440–2447. https://doi.org/10.35940/ijeat.A9972.109119
Shi, C., Chen, L., Wang, C., & Zhou, X. (2023). Mathematics-11-03134-V4.Pdf. Mathematics, 11(3134), 1–33. https://doi.org/https://doi.org/10.3390/math11143134
Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4), 427–437. https://doi.org/10.1016/j.ipm.2009.03.002
Uddin, M. S., Das, P. P., & Roney, M. S. A. (2016). Image-based approach for the detection of counterfeit banknotes of Bangladesh. 2016 5th International Conference on Informatics, Electronics and Vision, ICIEV 2016, February, 1067–1072. https://doi.org/10.1109/ICIEV.2016.7760162
Upton, K. (1995). A modern approach. Manufacturing Engineer, 74(3), 111–113. https://doi.org/10.1049/me:19950308
Wang, A., Goldsztein, G., & Sun, Z. (2022). Banknote Authentication Using Logistic Regression and Artificial Neural Networks. Journal of Student Research, 11(3), 1–11. https://doi.org/10.47611/jsrhs.v11i3.3777
Wang, L., Zhang, Y., Lanchi, X., Zhang, X., Guang, X., Li, Z., Li, Z., Shi, G., Hu, X., & Zhang, N. (2022). Automated detection and classification of counterfeit banknotes using quantitative features captured by spectral-domain optical coherence tomography. Science and Justice, 62(5), 624–631. https://doi.org/10.1016/j.scijus.2022.09.004
Zarpala, L., & Casino, F. (2021). A blockchain-based forensic model for financial crime investigation: the embezzlement scenario. Digital Finance, 3(3–4), 301–332. https://doi.org/10.1007/s42521-021-00035-5
Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. American Statistician, 46(3), 175–185. https://doi.org/10.1080/00031305.1992.10475879
Anand, S., & Priya, L. (2019). Digital Image Fundamentals. In A Guide for Machine Vision in Quality Control. https://doi.org/10.1201/9781003002826-2
Biosa, G., Giurghita, D., Alladio, E., Vincenti, M., & Neocleous, T. (2020). Evaluation of Forensic Data Using Logistic Regression-Based Classification Methods and an R Shiny Implementation. Frontiers in Chemistry, 8(October). https://doi.org/10.3389/fchem.2020.00738
Boateng, E. Y., Otoo, J., & Abaye, D. A. (2020). Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review. Journal of Data Analysis and Information Processing, 08(04), 341–357. https://doi.org/10.4236/jdaip.2020.84020
Budiwitjaksono, G. S., Trisnaningsih, S., Susilowati, E., & Habibie. (2025). Akuntansi Forensik (Memahami Fraud dan Investigasi Keuangan) (Issue August). https://www.researchgate.net/publication/394470990
Canny, J. (1986). A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6), 679–698. https://doi.org/10.1109/TPAMI.1986.4767851
Demircioğlu, A. (2022). The effect of preprocessing filters on predictive performance in radiomics. European Radiology Experimental, 6(1). https://doi.org/10.1186/s41747-022-00294-w
Denil, M., & Trappenberg, T. (2011). A Characterization of the Combined Effects of Overlap and Imbalance on the SVM Classifier. ResearchGate, September 2011. http://arxiv.org/abs/1109.3532
Fernández-Delgado, M., Cernadas, E., Barro, S., & Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems? Journal of Machine Learning Research, 15(October), 3133–3181.
Gopane, S., & Kotecha, R. (2020). Indian Counterfeit Banknote Detection Using Support Vector Machine. SSRN Electronic Journal, March. https://doi.org/10.2139/ssrn.3568724
Hantono, B. S., Alfarozi, S. A. I., Pratama, A. R., Rizqi, A. A. A., Mustika, I. W., Riasetiawan, M., & Asih, A. M. S. (2023). Enhancing Counterfeit Detection with Multi-Features on Secure 2D Grayscale Codes. Computers, 12(9), 1–14. https://doi.org/10.3390/computers12090183
Harsani, P., Maulana Muhammad, & Negara, T. (2024). Identifikasi Citra untuk Membedakan Uang Asli dan Palsu Menggunakan Algoritma Convolutional Neural Network (CNN). JST (Jurnal Sains Dan Teknologi), 13(2), 328–337. https://doi.org/10.23887/jstundiksha.v13i2.83416
Hasanah, nur Y., & Fatah, Z. (2024). Gudang Jurnal Multidisiplin Ilmu Deteksi Keaslian Uang Kertas Berdasarkan Citra Digital Dengan Menggunakan Teachable Machine Learning. Gudang Jurnal Multidisiplin Ilmu, 2(12), 44–50. https://doi.org/https://doi.org/10.59435/gjmi.v2i12.1111
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415
Lee, J. W., Hong, H. G., Kim, K. W., & Park, K. R. (2017). A survey on banknote recognition methods by various sensors. Sensors (Switzerland), 17(2). https://doi.org/10.3390/s17020313
Miller, C. M. (2023). A survey of prosecutors and investigators using digital evidence: A starting point. Forensic Science International: Synergy, 6(November 2022). https://doi.org/10.1016/j.fsisyn.2022.100296
Mishra, A. K., & Essop, M. H. (2019). Low-cost spectrogram based counterfeit medicine detection. AirXiv, 600. http://arxiv.org/abs/1904.07152
Pham, T. D., Lee, Y. W., Park, C., & Park, K. R. (2022). Deep Learning-Based Detection of Fake Multinational Banknotes in a Cross-Dataset Environment Utilizing Smartphone Cameras for Assisting Visually Impaired Individuals. Mathematics, 10(9). https://doi.org/10.3390/math10091616
Prasath, V. B. S., Alfeilat, H. A. A., Hassanat, A. B. A., Lasassmeh, O., Tarawneh, A. S., Alhasanat, M. B., & Salman, H. S. E. (2019). Distance and Similarity Measures Effect on the Performance of K-Nearest Neighbor Classifier -- A Review. AirXiv, 1–39. https://doi.org/10.1089/big.2018.0175
Rick Nie. (2023). Comparative Analysis Of Machine Learning Based Bank Note Authentication Through Variable Selection. December 20, 2023, 1–12. https://nhsjs.com/2023/comparative-analysis-of-machine-learning-based-bank-note-authentication-through-variable-selection
Roy, V., Mishra, G., Mannadiar, R., & Patil, S. (2019). Fake Currency Detection using Image Processing. International Journal of Computer Science and Mobile Computing, 8(4), 88–93. https://doi.org/10.1109/ISCS61804.2024.10581207
Sharan, V., & Kaur, A. (2019). Detection of counterfeit indian currency note osing image processing. International Journal of Engineering and Advanced Technology, 9(1), 2440–2447. https://doi.org/10.35940/ijeat.A9972.109119
Shi, C., Chen, L., Wang, C., & Zhou, X. (2023). Mathematics-11-03134-V4.Pdf. Mathematics, 11(3134), 1–33. https://doi.org/https://doi.org/10.3390/math11143134
Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4), 427–437. https://doi.org/10.1016/j.ipm.2009.03.002
Uddin, M. S., Das, P. P., & Roney, M. S. A. (2016). Image-based approach for the detection of counterfeit banknotes of Bangladesh. 2016 5th International Conference on Informatics, Electronics and Vision, ICIEV 2016, February, 1067–1072. https://doi.org/10.1109/ICIEV.2016.7760162
Upton, K. (1995). A modern approach. Manufacturing Engineer, 74(3), 111–113. https://doi.org/10.1049/me:19950308
Wang, A., Goldsztein, G., & Sun, Z. (2022). Banknote Authentication Using Logistic Regression and Artificial Neural Networks. Journal of Student Research, 11(3), 1–11. https://doi.org/10.47611/jsrhs.v11i3.3777
Wang, L., Zhang, Y., Lanchi, X., Zhang, X., Guang, X., Li, Z., Li, Z., Shi, G., Hu, X., & Zhang, N. (2022). Automated detection and classification of counterfeit banknotes using quantitative features captured by spectral-domain optical coherence tomography. Science and Justice, 62(5), 624–631. https://doi.org/10.1016/j.scijus.2022.09.004
Zarpala, L., & Casino, F. (2021). A blockchain-based forensic model for financial crime investigation: the embezzlement scenario. Digital Finance, 3(3–4), 301–332. https://doi.org/10.1007/s42521-021-00035-5
Published
2025-11-04
How to Cite
Sabrina Nur Amalia, & Imam Yuadi. (2025). Digital Forensic Approaches for Counterfeit Money Detection: A Compratie of KNN, Logistic Regression, and SVM Classifiers. Jurnal Multimedia Dan Teknologi Informasi (Jatilima), 7(03), 794-809. https://doi.org/10.54209/jatilima.v7i03.1764
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Copyright (c) 2025 Sabrina Nur Amalia, Imam Yuadi
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