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All Journal Jurnal Pengabdian kepada Masyarakat Nusantara Journal of Soft Computing Exploration SENTRI: Jurnal Riset Ilmiah Jurnal Cakrawala Maritim
Pujiputra, Anggarjuna Puncak
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Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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Journal : SENTRI: Jurnal Riset Ilmiah

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Segmentasi Citra Pengelasan Kapal Menggunakan Convolutional Neural Network Brian, Thomas; Pujiputra, Anggarjuna Puncak; Putri Nur Rahayu; Augustino, Immanuel Freddy; Parman, Parman; Bone, Iskia Ipan Dua’
SENTRI: Jurnal Riset Ilmiah Vol. 4 No. 10 (2025): SENTRI : Jurnal Riset Ilmiah, Oktober 2025
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v4i10.4767

Abstract

Welding inspection plays a critical role in the shipbuilding industry to ensure the integrity and quality of welded joints. However, the prevailing manual inspection procedures are inherently subjective, prone to bias, and result in inconsistent quality assessments. Therefore, there is a strong need for an automated and intelligent system capable of objectively detecting welding points. To address this, we propose an advanced segmentation model based on deep learning and computer vision techniques, specifically utilizing the enhanced Nested UNet architecture with extensive architectural modifications and comprehensive hyperparameter tuning. To further optimize the segmentation performance, we systematically compare different convolutional blocks integrated into the network architecture. The dataset used consists of 548 welding images. Each image is manually annotated using the VGG Image Annotator (VIA) application by marking the weld point areas as polygons. This research focuses on the development of a Nested UNet model, a deep learning-based image segmentation model for detecting weld points from previous models using the UNet architecture. During the training process, performance on both the training and validation datasets is continuously monitored and recorded. This results in several logs recording the training loss, validation loss, training IoU, and validation IoU for each of the three types of convolutional blocks used in the dense bottleneck. Our experimental evaluation shows that the use of VGG - Dense - VGG convolutional blocks in Nested UNet yields the highest performance, achieving a Training Dice score of 0.92970 and a Validation Dice score of 0.89695 on our collected dataset.
Co-Authors Agus Khumaidi Al Hazman, Muhammad Alwi Sina Khaqiqi Augustino, Immanuel Freddy Bayu, Nurissabiqoh Binta Bone, Iskia Ipan Dua’ Brian, Thomas Budi, Perdinan Setia Gastriani, Ovi Prina Hasin, Muhammad Khoirul Malik, Alfianto Taufiqul Nisazarifa, Adristi Parman parman parman Pratama , Moh. Andris Putri Nur Rahayu Rahayu, Putri Nur Rahayuningsih, Siti Rinanto, Noorman Sholahuddin Muhammad Irsyad Sholikah, Evi Nafiatus Sukarno, Friska Intan Wibowo, Sekarsari Widyastuti, Indri Ika