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All Journal Jurnal Riset Informatika Jurnal Ekonomi Manajemen Sistem Informasi Jurnal Teknologi Informatika dan Komputer Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Pengabdian Kepada Masyarakat Abdi Putra Prosiding SENTIMETER : Seminar Nasional Teknologi Informasi, Mekatronika, dan Ilmu Komputer
Gina Purnama Insany
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Public Health

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Application of Transfer Learning Method on Convolutional Neural Network (CNN) to Identify Genuine and Fake Diplomas Awaludin, Rifa; Anggun Fergina; Gina Purnama Insany
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3295

Abstract

The authenticity of diplomas plays a crucial role in maintaining the integrity of the education system and ensuring that recognized academic competencies align with an individual's actual achievements. Diplomas are not merely administrative documents, but strategic instruments in job recruitment and professional qualification assessment. However, with increasing educational mobility, document misuse through diploma forgery is becoming increasingly prevalent, potentially undermining public trust in educational institutions. Currently, the verification process is still largely carried out manually through visual inspection of document elements such as layout and stamps. The reliance on the examiner's experience makes this method vulnerable to inconsistencies and human error, especially when dealing with fake diplomas with visual qualities that increasingly resemble genuine documents. Diploma forgery is a problem that impacts the credibility of educational institutions and the validity of academic data. Manual inspection is often inconsistent and time-consuming. This study develops a model for classifying genuine and fake diplomas using a Convolutional Neural Network (CNN) with a transfer learning scheme. The performance of the ResNet50, VGG16, and MobileNetV2 architectures is comparatively analyzed. Data preprocessing included resizing, normalization, and augmentation. Test results showed the ResNet50 architecture achieved optimal performance with 92.63% accuracy, 92.16% precision, 94.00% recall, and 93.07% F1-score. The system was implemented in a Streamlit-based web application to facilitate the verification process.
Co-Authors adang badru jaman,anggun fergina, adang badru jaman,anggun fergina Ahmad Rofiqi Alfiansyah Hidayat Alida Fany Tariza Putri Alun Sujjada Alyanissa Putri Iskandar Amelinda Renjani Andi Agusti Andi Nopiandi Asep Rizki Firdaus Asep Teguh Hidayat Awaludin, Rifa Billy Achmad Bachrudin Dendi Nasrulloh Dila Aura Futri Dita Indriawati Erik Abdul R Fadlan Nabiel Fahmi Aldian Surachman Feby Alfaraby Fikri Maulana Indra Yustiana IVANA LUCIA KHARISMA Jamaludin Ahmad Rifai Juan Cerah Joseph Kamdan Kanza Salsabila Mirza Andira N Moch. Nirmansyah Moh Azral Fathurrazaq Muhammad Fadilah Nurjaman Muhammad Ilham Juardi Muhammad Raihan Izharul Haq Nugraha Putri Ayu Negara Sandra Natasya Satria Junmulyana Senita Richia Putri Shanika Layung Medal Wangi Somantri Sri Rahmawati Ujang Herlan Verdi Eza Irawan