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All Journal JSI: Jurnal Sistem Informasi (E-Journal) ICON-CSE International Journal of New Media Technology Jurnal Elektronika dan Telekomunikasi Jurnal Sisfokom (Sistem Informasi dan Komputer) Sriwijaya Journal of Informatics and Applications Jurnal Generic Jurnal Pendidikan dan Teknologi Indonesia Media Elektrik
Julian Supardi
Universitas Sriwijaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Other

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Securing and Concealing Messages in the Edge Area of Digital Images Using Least Significant Bit Method and Blowfish Algorithm Nur, Arviansyah; Supardi, Julian; Rachmatullah, M. Naufal
Sriwijaya Journal of Informatics and Applications Vol 5, No 2 (2024)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v5i2.88

Abstract

The process of cryptography produces random text that can obscure a message, making it difficult for others to read. However, cryptography itself is still not strong enough to secure the message, so steganography is needed to conceal the existence of the message from the human eye. Besides hiding the message, another objective is to assess the impact of message embedding. In the process of encrypting and decrypting the message, the Blowfish algorithm is used, while message embedding utilizes the Least Significant Bit steganography, and edge detection is performed using the Canny algorithm. Through research conducted with the combination of cryptography and steganography, an excellent image is obtained with a PSNR value of 76.6932 and MSE of 0.0013 for a message length of 64 bytes. Meanwhile, visually, the results show a relatively similar appearance between the host image and the stego image.
Optimization of VGG-16 Accuracy for Fingerprint Pattern Imager Classification Andreansyah, Agus; Supardi, Julian
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Fingerprint is a unique biometric identity commonly used as evidence in court. However, its quality can decline due to external factors such as uneven surfaces, weather conditions, or distortion. The dataset used in this study is FVC2000. Convolutional Neural Networks (CNN) were applied for fingerprint image enhancement and classification, focusing on patterns such as whorl, arch, radial loop, ulnar loop, and twinted loop. This research optimized the VGG-16 model by adding several hyperparameters. The results showed the highest accuracy of 100% on the testing data with a learning rate of 0.0001, using 50 epochs and a training-to-validation data split ratio of 80%:10% from a total of 400 fingerprint image pattern data. These findings demonstrate that the VGG-16 model successfully classified fingerprint images with optimal performance, contributing significantly to the development of CNN-based fingerprint classification systems.
Enhancing Remote Sensing Image Resolution Using Convolutional Neural Networks Supardi, Julian; Samsuryadi, Samsuryadi; Satria, Hadipurnawan; Serrano, Philip Alger M.; Arnelawati, Arnelawati
Jurnal Elektronika dan Telekomunikasi Vol 24, No 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.653

Abstract

Remote sensing imagery is a very interesting topic for researchers, especially in the fields of image and pattern recognition. Remote sensing images differ from ordinary images taken with conventional cameras. Remote sensing images are captured from satellite photos taken far above the Earth's surface. As a result, objects in satellite images appear small and have low resolution when enlarged. This condition makes it difficult to detect and recognize objects in remote-sensing images. However, detecting and recognizing objects in these images is crucial for various aspects of human life. This paper aims to address the problem of remote sensing image quality. The method used is a convolutional neural network. The results show the proposed method can improve PSNR and SSIM compared to previous methods
APPLICATION OF XCEPTION ARCHITECTURE FOR DEEP LEARNING-BASED FINGERPRINT RECOGNITION Agus Andreansyah; Julian Supardi
Jurnal Media Elektrik Vol. 22 No. 1 (2024): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v22i1.5964

Abstract

This study proposes a convolutional neural network (CNN) method with an xception architecture model that is used to classify the types of fingerprint image patterns. The data used in this study uses data taken directly using a scanning tool made using FPM 10 A sensors and Arduino Uno. The dataset consists of five types of fingerprint image patterns, namely arch, ulnar loop, whorl, radial loop and twinted loop with a total of 1000 data. The research started from data collection, pre-processing, CNN architecture design, model training and evaluation. The application of the xception architecture shows the best performance with high test accuracy values, stable and consistent. The test scenario of this study is to compare different epoch values, namely 10.30 and 50 and use two learning rates, namely 0.0001 and 0.001. The best test results were obtained at epoch 30 with a learning rate of 0.0001, which was 92% and 93% at epoch 50 with a learning rate of 0.001.
VGG-16 Accuracy Optimization for Fingerprint Pattern Imager Classification Andreansyah, Agus; Supardi, Julian
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i1.2317

Abstract

A fingerprint is a unique biometric identity commonly used as evidence in court. However, the quality of fingerprints can deteriorate due to external factors such as uneven surfaces, weather conditions, or distortion. This study uses the FVC2000 dataset and applies Convolutional Neural Networks (CNNs) to enhance and classify fingerprint images, focusing on patterns such as arches, loops, radial loops, ulnar loops, and twin loops. A novel aspect of this research is the optimization of the VGG-16 model by making specific adjustments to the hyperparameters, including setting the learning rate to 0.0001, using 50 epochs, and selecting a training-to- validation data split of 80%:10%. These adjustments were made to enhance the model’s ability to classify complex and varied fingerprint patterns, which typically present challenges to standard CNN models. The results of the study show the highest accuracy of 100% on the test data with the optimized parameters.These findings demonstrate that the optimized VGG-16 model successfully classifies fingerprint images with optimal performance. The real-world implications of achieving 100% accuracy include an increase in the reliability of biometric identification systems, especially for forensic and security applications that require high accuracy to ensure accurate decisions. This study makes a significant contribution to the development of CNN-based fingerprint classification systems, offering a new approach that supports more reliable and precise biometric applications.
Co-Authors Abdiansah, Abdiansah Agus Andreansyah Andreansyah, Agus Arnelawati, Arnelawati Endang Lestari Ruskan Firdaus Firdaus Hadipurnawan Satria Hafiz Mursid Hastie Audytra Muhammad Qurhanul Rizqie Nur, Arviansyah Rachmatullah, M. Naufal Rina Yuniarti Samsuryadi Samsuryadi Saparudin Saparudin Serrano, Philip Alger M. Sihotang, Gabriel Mediose Alfranda Umari, Zainal Vebri Dores Vikki Aria Dinata