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Pratomo Setiaji
Universitas Muria Kudus
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Image Forensics Analysis of the Authenticity of Digital Payment Evidence using the K-Nearest Neighbor Algorithm Agusta, Feriyan; Setiaji, Pratomo; Triyanto, Wiwit Agus
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5728

Abstract

The rapid growth of digital transactions has also increased the risk of digital payment evidence forgery, such as screenshot manipulation or digital image editing. This study aims to develop an automated authenticity validation system for digital payment evidence by integrating Image Processing, Image Forensics, and Optical Character Recognition (OCR) technologies. The processing pipeline begins with image preprocessing, followed by forensic feature extraction and OCR-based text analysis, which are then classified using the K-Nearest Neighbor (KNN) algorithm. This study evaluates 15 experimental scenarios based on combinations of training and testing data ratios (90:10, 80:20, 70:30, 60:40, and 50:50) and random state values (42, 32, and 22). Model performance is assessed using accuracy, precision, recall, and F1-score metrics across a range of k values from 1 to 15. The results indicate that the optimal performance is achieved at k = 7, with an accuracy of 97.1%. The proposed system is able to efficiently distinguish between authentic and manipulated digital payment evidence. The system is implemented as an Android application that allows users to upload payment evidence via the device camera or gallery, after which the system automatically analyzes its authenticity. The findings demonstrate that the integration of image forensic techniques and the K-Nearest Neighbor (KNN) algorithm effectively detects indications of manipulation in digital payment evidence and enhances the efficiency of the verification process within the digital financial services ecosystem.
Co-Authors - Supriyono Adi Nugroho Adisaputra, Rizky Aditiya, Natasya Aditya, Ahsanu 'Amala Adiyono, Soni Agusta, Feriyan Alwani, Yazid Amaliyah, Fitriyah Amelia, Dwi Andy Prasetyo Utomo Anteng Widodo Aprilia Damayanti Arif Setiawan Artiarno, Andrean Maulana Aynullutfihana, Nanda Azzahro, Erina Diana Laily Fithri Eko Darmanto Fadhilah, Bayu Samudro Fajar Nugraha Fakhriyyah, Anis Ferianti, Lydya Ayu Hanif Rizqullah, Aufa Irfan Santiko Kurniasari, Nita Kusumawardani, Nada Laily Fithri, Diana Lisa Rachmawati Maula, Ahmad Inzul Muhammad Arifin Nia Zuliyana, Nia Noor Latifah Nugroho, Mochamad Bagus Iqbal Adi Nurhaliza, Maulin Pambudi, Satrio Prayogo, Adhie Putri Kurnia Handayani Putri, Rizka Ferbriliana R Rhoedy Setiawan Rachman, Muhammad Ulinnuha Ainun Noor Rahmawati, Yulinda Ramadlan As'ari, Wahyu Ramandani, Fitri Rani Setiawaty Rohmah, Putri Anjilis Romadhon, Zainur Semit, Danial Setiawan, Faris Apri Setyanto, Rangga Setyawan, Moh Dodi Suciningtyas - Suciningtyas - Suciningtyas Suciningtyas Suciningtyas Suciningtyas, Suciningtyas Suku Rahayu, Sri Intan Supriyono Supriyono Supriyono Supriyono Syafiul Muzid Tamaulina Br Sembiring Tengku Riza Zarzani N Wahyu Wibowo, Angga Widodo, Wahyu Kurniawan Ade Nur Wiwit Agus Triyanto Yoanas, Sahenda Yudie Irawan Zahra, Fatimah Az