Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Journal of Artificial Intelligence in Architecture JoDENS: Journal of Digital Ecosystem for Natural Sustainability
Chow, David
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Arsitektur Hybrid Vision Transformer–ConvNeXt dengan Multi-Task Focal Loss dan Medical Test-Time Augmentation untuk Klasifikasi Lesi Kulit Berbasis Citra Hendry; Govert Anwar, Ferry; Chow, David; Saputra, Andi; Khaerul Naim Mursalim, Muhammad
Journal of Digital Ecosystem for Natural Sustainability Vol 5 No 2 (2025): Desember 2025
Publisher : Fakultas Komputer - Universitas Universal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63643/jodens.v5i2.325

Abstract

Dermatoscopy image-based skin lesion classification is a challenge in dermatology due to the high visual variation between lesion types and the imbalanced class distribution in the dataset. In this study, a Hybrid Vision Transformer–ConvNeXt architecture is proposed, combining the global attention capability of Vision Transformer (ViT) and the spatial feature representation of ConvNeXt, to improve the classification performance of skin lesion images on the HAM10000 dataset. This study also applies Multi-Task Focal Loss, auxiliary classifier, and Weighted Random Sampler to effectively address the class imbalance. In addition, the Medical Test-Time Augmentation (TTA) approach is used in the inference stage to improve the stability of predictions. The model is trained using a two-stage strategy (head training and full fine-tuning), as well as optimization based on AdamW and Cosine Annealing Warm Restarts. The test results show that the proposed model successfully achieves a validation F1-Score of 0.8723, and after TTA it increases to 0.90, surpassing the baseline of ViT and single ConvNeXt. These findings indicate that the integration of ViT–ConvNeXt with loss strategy and medical TTA is able to significantly improve the performance of skin lesion classification, and has the potential to be applied as a clinical diagnosis support system.
A Systematic Review: Examining the Impacts of Artificial Intelligence Chow, David; Depari, Catharina Dwi Astuti; Gabriella, Eva
Journal of Artificial Intelligence in Architecture Vol. 5 No. 1 (2026): Artificial Intelligence for Human-Centric Performance: Integrating Neuroarchite
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jarina.v5i1.11340

Abstract

Since its breakthrough in the mid-20th century, Artificial Intelligence (AI) has held great promises for improving the capacity of urban planning to address complex problems. Despite this, the literature on how AI was specifically utilized and how it impacted urban planning remains limited. This study was aimed at examining how AI-driven technology shapes the landscape of urban planning. To attain this, we reviewed 48 articles after performing a systematic screening of 2,359 journal records in the Scopus database, published since the rising use of AI in urban planning. We found that urban planners have broadly adopted AI to address various complex environmental problems toward the making of sustainable and smart cities. Additionally, Machine Learning, Big Data, and the Internet of Things (IoT) are also indicated as AI-driven technologies commonly adopted in urban planning over the years.
Co-Authors Andi Saputra Catharina Dwi Astuti Depari, Catharina Dwi Astuti Gabriella, Eva Govert Anwar, Ferry Hendry . Khaerul Naim Mursalim, Muhammad