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All Journal Jurnal Simantec Jurnal Ilmiah Kursor TEKMAPRO Journal of Industrial Engineering and Management Scan : Jurnal Teknologi Informasi dan Komunikasi International Journal of Advances in Intelligent Informatics International Journal of Artificial Intelligence Research Teknika: Engineering and Sains Journal JIEET (Journal of Information Engineering and Educational Technology) IJEBD (International Journal Of Entrepreneurship And Business Development) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Penelitian SEINASI-KESI Journal of Information Systems and Informatics bit-Tech Journal of Robotics and Control (JRC) JATI (Jurnal Mahasiswa Teknik Informatika) e-NARODROID Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Journal of Applied Data Sciences International Journal of Data Science, Engineering, and Analytics (IJDASEA) Information Technology International Journal (ITIJ) Jurnal ilmiah teknologi informasi Asia Jurnal Pengabdian Masyarakat SENSASI Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
I Gede Susrama Mas Diyasa
Department Of Informatics, Faculty Of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Transportation

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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 Achmad Junaidic Adiwidyatma, Afdhal Reshanda Ahmad Naufal Mumtaz Akmal, Mohammad Faizal Alfiatun Masrifah Alhamda, Denisa Septalian Allan Ruhui Fatmah Sari Amanullah , Nurkholis Anak Agung Diah Parami Dewi Anita Muliawati Ardianto, Taruna Ariyono Setiawan Aryananda, Rangga Laksana Aurelia, Cenditya Ayu Awaludin W., Moh. Haydir Awang, Mohd Khalid Azizah, Nabila Wafiqotul Bambang Trigunarsyah Bambang Trigunarsyah Budi Nugroho Cahyani Kuswardhani, Hajjar Ayu Deshinta Arrova Dewi Dewi, Deshinta Arrova Dewi, Deshinta Arrowa Dwi Arman Prasetya Dwi Kusuma, Irma Erma Suryani Erna Harfiani Etniko Siagian, Pangestu Sandya Eva Yulia Puspaningrum Fara Disa Durry Fatmah Sari, Allan Ruhui Firmansyah, Taufik Nur Firya Nadhira Gideon Setya Budiwitjaksono Gideon Setya Budiwitjaksono Gunawan, Ellexia Leonie Hadi, Surjo Hafidz Amarul Ma’rufi Halim, Christina Hamawi, Moch. Hawin Hanif Nur Fadlilah Humairah, Sayyidah I Nyoman Dita Pahang Putra I Nyoman Dita Pahang Putra Ilham Ade Widya Sampurno Ilham Ade Widya Sampurno Intan Yuniar Purbasari Jauharis Saputra, Wahyu Syaifullah Jojok Dwiridotjahjono Kraugusteeliana Kraugusteeliana Kraugusteeliana Kraugusteeliana Mandeni, Ni Made Ika Marinni Mandyartha, Eka Prakarsa Moch. Hatta Mohamad Nur Amin Mohammad Idhom Mohammad Rafka Mahendra A Mohammad Rafka Mahendra Ariefwan Mudjahidin Muhammad Rif'an Dzulqornain Mumtaz, Ahmad Naufal Munoto Mustika, Agung Nadhira, Firya Nahusuly, Barep J. A. I. Ni Made Ika Marini Mandenni Ni Made Ika Marini Mandenni Ni Made Ika Marini Mandenni NYOMAN DITA PAHANG PUTRA, NYOMAN Prabowo, Aris Prasetyo, Galih Novian Putri, Fitri Aulia Yuliandi Raditya, Askara Rangga Laksana A Rangga Laksana Aryananda Rheza Rizqi Ahmadi Ridho Syahdindo Rizal Harjo Utomo Sabrina Charya Floribunda Santoso, Sri Fuji Senny Meliyan Setiawan, Ariyono Setiawan, Ariyono Shodiq, Ja’far Slamet Winardi Sri Wibawani, Sri Sugeng Purwanto Sugiarto S Sugiarto Sugiarto Sugiarto, Sugiarto Sukri, Hanifudin Sulianto Bhirawa Sunarko, Victor Immanuel Suryani, Dedik Taruna Ardianto Terza Damaliana, Aviolla Thalita Syahlani Putri Trimono, Trimono Wafiqotul Azizah, Nabila Wahyu Caesarendra Wahyu Dwi Lestari Wahyu S.J. Saputra Wan Awang, Wan Suryani Wardhani, Naritha Cahya Widianto, Purwito Ridho Widiastuty, Riana Retno Wijaya, Pandu Ali Yisti Vita Via Yisti Vita Via