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All Journal Mulia International Journal in Science and Technical International Journal of Industrial Optimization (IJIO)
Nanna Suryana Herman
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering

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A New Model of Packet tracking Using The TSP Algorithm Method Hardi, Richki; Nanna Suryana Herman; Naim Che pee
Mulia International Journal in Science and Technical Vol 2 No 1 (2019): August
Publisher : Universitas Mulia

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

Abstract

The packet tracking system becomes essential for the sender of the packet because, with this system, the sender can know the whereabouts or position of the package being sent. If there is a delay in delivery, the sender can also find out the cause of the delay, and it could be due to traffic, wrong address, natural disaster, or other problems. More than that, this system can determine the optimal route to be passed, namely by testing and moving several paths so that with all the possibilities that there will get the optimal path. The purpose of this study is to provide a model and find out the optimal way that is feasible to take in package delivery. The method applied in this research is to use the TSP algorithm, which is an algorithm that tries all possible routes to get the optimal path. The research area will be applied to the Aceh area, and also a request from one of the national shipping companies in Indonesia, Pos. This system will be built based on the web so that the results displayed can be reached by all areas that are connected to the internet. It is hoped that this model and system that will be built can become a reference and reference for other researchers.
Semantic brain tumor segmentation from 3D MRI using u2-net with custom dilated and residual u-block Elvaret; Habibullah Akbar; Nanna Suryana Herman; Marwan Kadhim Mohammed Al-shammari
International Journal of Industrial Optimization Vol. 7 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v7i1.11576

Abstract

Segmentation of brain tumors in volumetric medical images is challenging due to the complexities of the tumor structure, the types, and the heavy-weight 3D data processing. In contrast, 2D-based segmentation methods on the slice data reduce the amount of information due to the anisotropic shape of the tumors and lead to poor segmentation results. This study proposes a 3D network structure combining ReSidual U-Block (RSU), custom dilated block, and U2-Net for automatic segmentation of brain tumors from MRI images, namely 3D RSU U2-Net+. The RSU and custom dilated block are embedded and joined in the nested U-Net structure to obtain multi-resolution features and global information, enhancing segmentation accuracy while reducing computational overhead. The proposed method outperformed the segmentation results of the standard U-Net, on brain tumor data in the medical segmentation Decathlon (MSD) dataset. The proposed model achieves an average validation soft dice loss of 0.1320 and dice score coefficient of 78% and intersection over union of 64% for testing. Although having 3 times parameters, the model requires less GPU time (397.7 minutes) than U-Net (433.6 minutes), demonstrating improved computational efficiency resulting from the effective use of residual and dilated blocks. Moreover, the model achieves 75.4% average sensitivity and 99% specificity for edema, enhancing, and non-enhancing tumors. These experimental results show that the 3D RSU U2-Net+ has been able to outperform the U-Net. However, the model’s performance on non-enhancing tumors remains relatively lower compared to other tumor types, indicating on opportunity for further optimization.
Co-Authors Akbar, Habibullah Elvaret Marwan Kadhim Mohammed Al-shammari Naim Che pee Richki Hardi