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All Journal Jurnal Simantec Jurnal Ilmiah Kursor JIEET (Journal of Information Engineering and Educational Technology) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Robotics and Control (JRC) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Journal of Applied Data Sciences ITIJ
Mas Diyasa, I Gede Susrama
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mathematics Mechanical Engineering

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Journal : Jurnal Simantec

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MRI image enhancement of the brain using U-NET Etniko Siagian, Pangestu Sandya; Puspaningrum, Eva Yulia; Wan Awang, Wan Suryani; Mas Diyasa, I Gede Susrama
Jurnal Simantec Vol 13, No 2 (2025): Jurnal Simantec Juni 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/simantec.v13i2.29775

Abstract

The quality of Magnetic Resonance Imaging (MRI) images is often compromised by various types of noise, such as salt, pepper, salt-and-pepper, and speckle noise, caused by technical or environmental disturbances. This study aims to develop a brain MRI image denoising model based on the U-Net architecture, capable of effectively removing different types of noise. The methodology includes collecting normal brain MRI datasets, applying data augmentation to increase variability, and introducing artificial noise to simulate possible noise conditions. The U-Net model is trained and evaluated using the Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) metrics. The novelty of this study lies in its combination of augmentation techniques, multi-intensity artificial noise variations, and its exclusive focus on normal brain MRI images. The results demonstrate that the U-Net model achieves optimal performance on salt-and-pepper noise at an intensity of 0.1, marked by the highest PSNR value of 37.2047 dB and the lowest MSE value of 0.000207. Conversely, the model shows the lowest performance on high-intensity speckle noise, indicating greater challenges in addressing multiplicative noise. This study contributes a systematic and empirically tested approach to improving the quality of brain MRI images with high efficiency, supporting the development of image-based diagnostic systems in the medical field.Keywords: Deep Learning, Denoising, Image Enhancement, Noise, U-Net.
Optimization of facial recognition authentication system using InceptionResNetV1 with Pretrained VGGFACE2 Gunawan, Ellexia Leonie; Mas Diyasa, I Gede Susrama; Jauharis Saputra, Wahyu Syaifullah
Jurnal Simantec Vol 13, No 2 (2025): Jurnal Simantec Juni 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/simantec.v13i2.29776

Abstract

Face recognition as a biometric authentication method continues to evolve due to its high security and ease of use. However, training models from scratch faces challenges such as the need for large datasets and high computational resources. This study aims to optimize the face authentication system using the InceptionResNetV1 architecture with a transfer learning approach from the pretrained VGGFace2 model and to compare its performance with CASIA-WebFace. Face detection is conducted using YOLOv8, face embeddings are generated by InceptionResNetV1, and authentication is performed by calculating the Euclidean distance between embeddings. Face data were collected from university students and divided into training and testing datasets. Performance evaluation includes accuracy, precision, recall, F1-score, and the confusion matrix. The results show that the VGGFace2 model achieved an accuracy of 98.75%, a recall of 100%, and an F1-score of 99.26%, with no False Negatives, while CASIA-WebFace achieved an accuracy of 86.25% with a recall of 85.07%. The main contribution of this study is to demonstrate that the use of transfer learning with the pretrained VGGFace2 model can significantly improve the accuracy of face authentication systems and to show its effectiveness for developing systems with limited data and computational resources. This study contributes by highlighting the superiority of the pretrained VGGFace2 model in face authentication systems and emphasizing the effectiveness of transfer learning for implementing accurate systems under resource constraints.Keywords: Authentication System, InceptionResNetV1, Face Recognition, Transfer Learning, VGGFace2
Co-Authors Achmad Junaidi Adiwidyatma, Afdhal Reshanda Akmal, Mohammad Faizal Alhamda, Denisa Septalian Amanullah , Nurkholis Ardianto, Taruna Aryananda, Rangga Laksana Awang, Mohd Khalid Budi Nugroho Cahyani Kuswardhani, Hajjar Ayu Dewi, Deshinta Arrova Dewi, Deshinta Arrowa Dwi Arman Prasetya Dwi Kusuma, Irma Etniko Siagian, Pangestu Sandya Eva Yulia Puspaningrum Fara Disa Durry Fatmah Sari, Allan Ruhui Gunawan, Ellexia Leonie Hadi, Surjo Halim, Christina Humairah, Sayyidah Jauharis Saputra, Wahyu Syaifullah Mumtaz, Ahmad Naufal Mustika, Agung Nadhira, Firya Santoso, Sri Fuji Setiawan, Ariyono Sri Wibawani, Sri Sugiarto S Sukri, Hanifudin Sunarko, Victor Immanuel Terza Damaliana, Aviolla Trimono, Trimono Wafiqotul Azizah, Nabila Wahyu Caesarendra Wahyu Dwi Lestari Wan Awang, Wan Suryani Widiastuty, Riana Retno Wijaya, Pandu Ali Yisti Vita Via