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All Journal Jurnal Ilmu Komputer dan Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Inovasi Teknologi Pendidikan Register: Jurnal Ilmiah Teknologi Sistem Informasi Briliant: Jurnal Riset dan Konseptual Syntax Literate: Jurnal Ilmiah Indonesia JURNAL EDUCATION AND DEVELOPMENT MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pendidikan dan Pengabdian Masyarakat JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Evan Tanuwijaya
Universitas Ciputra Surabaya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

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RECOGNITION OF HUMAN FACES IN VIDEO CONFERENCE APPLICATIONS USING THE CNN PIPELINE Evan Tanuwijaya; Reinaldo Lewis Lordianto; Reiner Anggriawan Jasin
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 2 (2022): JUTIF Volume 3, Number 2, April 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.2.219

Abstract

The COVID-19 pandemic has forced daily face-to-face activities to be carried out online using video conferencing applications. To record participant participation in meetings using a video conference application, an online form application is used. However, participants sometimes do not see this and are often missed due to the large number of incoming chats. Therefore, the use of face detection for attendance using a combination of CNN to detect all the faces in a video conference using YOLO Face and CNN to recognize the owner of a face using Smaller VGG in a pipeline will make it easier to recognize participants who are present at the video conference. The results of the Smaller VGG training are obtained, namely the loss value of 0.059, the accuracy value is 0.995, the recall value is 0.994, the precision value is 0.996. Meanwhile, for the validation phase of the model, the loss value is 0.497, the accuracy value is 0.979, the recall value is 0.979 and the precision value is 0.981. In terms of training duration, the smaller VGG has a duration of 4 minutes and 16 seconds. The Smaller VGG model was combined with YOLO to create a CNN pipeline and was successful in recognizing the faces of video conference participants
HUMAN FACE RECOGNITION ON IMAGE VIDEO CONFERENCE APPLICATION USING SIAMESE NETWORK WITH SKIP CONNECTION SMALLER VGG MODEL Evan Tanuwijaya; Averill Saladin Atma Setiawan; Achmad Rijalu Arianindita; Timothy Kristanto
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.5.981

Abstract

Attendance recording is needed to find out someone's attendance at a meeting or meeting. These meetings are sometimes conducted online through the video conferencing application. Recording attendance at online meetings is using an online form that is distributed via chat. There are several problems such as chats piling up and meeting participants arriving late so they cannot access the form link. Therefore, facial recognition can be used to record attendance using screenshots as an attendance record with the aim of helping to facilitate attendance recording through video conferencing applications using computer vision technology. This study proposes a method of using the Siamese network with the Smaller VGG skip connection model to improve human face recognition in video conferencing application images. Has validation accuracy results in the training phase of 98%, precision of 98%, and recall of 98%. For the similarity phase where the model is applied to the Siamese network, the accuracy is 95%, the precision is 53%, and the recall is 78%. Then the model is applied to the pipeline system with the YOLO-face model to classify the results of face detection from Yolo with the faces in the database so that the model does not need to be retrained if there are new faces, it only needs to add facial images to the database to be compared with the query image..
COMPARATIVE STUDY OF CNN-BASED ARCHITECTURES ON EYE DISEASES CLASSIFICATION USING FUNDUS IMAGES TO AID OPHTHALMOLOGIST Yaurentius, Evelyn Callista; Saputri, Theresia Ratih Dewi; Tanuwijaya, Evan; Sutanto, Richard Evan
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.3699

Abstract

Eye health has a significant impact on quality of life, with more than 2.2 billion people experiencing vision problems. Many of these cases can be prevented or treated. The use of AI for eye disease classification helps healthcare professionals provide optimal care. However, the complexity of fundus images challenges classification performance. This study examines various Convolutional Neural Network (CNN) architectures using Transfer Learning and Adam optimization. Fundus images are processed using CLAHE (clip limit and grid size) and the Wiener filter (size) to enhance contrast and reduce noise. Afterward, ResNet-152, EfficientNet, MobileNetV1, and DenseNet-121 are tested to identify the most effective model. The study aims to determine the optimal CNN architecture for eye disease classification, assisting ophthalmologists in diagnosing eye diseases through fundus images. The best CNN model, ResNet-152, achieved an accuracy of 94.82%, outperforming other models by 3.95 - 8.29%.
Co-Authors AA Sudharmawan, AA Achmad Rijalu Arianindita Agus Zainal Arifin Angelica Roseanne Arif Mudi Priyatno Averill Saladin Atma Setiawan Baskoro Nugroho Chastine Fatichah Christian Tanjono David Christian Kartamihardja Dini Adni Navastara, Dini Adni Eha Renwi Astuti Jimmy Ellya Kurniawan Kenneth Sutjiawan Kuncoro Dewi Rahmawati Laura Mahendratta Tjahjono Lestari, Caecilia Citra Mohammad Fatoni Anggris, Mohammad Fatoni Mychael Maoeretz Engel Noviando Yuliando Rarasmaya Indraswari Reinaldo Lewis Lordianto Reiner Anggriawan Jasin Rinabi Tanamal Rudy Susanto Rudy Susanto Safri Adam Siswanto, Felicia Devina Sutanto, Richard Evan Theresia Ratih Dewi Saputri Timothy Kristanto Timotius Timotius Leonardo Lianoto Tota Pirdo Kasih Tota Pirdo Kasih Yaurentius, Evelyn Callista Yuwono Marta Dinata