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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Kardawi, Muhammad Yusuf Gunawan, Alexander Agung Santoso Bina Nusantara University Profesional Engineer Program Faculty of Engineering Jakarta11480 Indonesia Faculty of Computer Science University of Indonesia Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331513320

Remote sensing images present classification challenges due to the complexity of their structural and spatial patterns. This research explores a hybrid approach that combines convolutional neural network (CNN) and attention through feature fusion to improve scene classification accuracy in remote sensing images. The proposed architecture utilizes EfficientNet and VGGNet to extract depth features separately. The extracted features are then integrated with Dynamic Selfattention (DSA), which dynamically focuses the model on the most relevant information in the image. DSA allows the model to adaptively assign weights to different parts of the image, thus improving the discriminative ability of the model. Furthermore, a feature fusion technique is applied to combine information from different layers of the CNN and DSA modules. Experiments conducted on the UC Merced dataset showed accuracies of 0.9181 and 0.9167. These results show that the combination of CNN, DSA, and feature fusion is an effective and robust approach for remote sensing image classification. © 2025 IEEE.

关键词： Convolutional neural networks

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Remote Sensing Scene Classification Based on Hybrid Convolutional Neural Network and Attention Module

Remote Sensing Scene Classification Based on Hybrid Convolut...

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Advancement in Data Science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Muhammad Yusuf Kardawi Alexander Agung Santoso Gunawan Profesional Engineer Program Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

关键词： Adaptation models Scene classification Accuracy Electronic learning Data science Feature extraction Convolutional neural networks Data mining Remote sensing Information systems

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Application of Lean Project Management Method in Environmental Drainage Development Case Study: x Area Bekasi City

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IOP Conference Series: Materials Science and engineering 2021年第1期1096卷

作者： S Maulana F D Ariyanti Profesional Engineer Program Faculty of Engineering Bina Nusantara University Jakarta Indonesia 11480

Drainage systems are infrastructures for runoff water management and flooding in the settlements area. Many factors cause the drainage system not to function properly. This study was conducted on the implementation of an urban drainage system project in the X Area of Bekasi City using the Lean Project Management Method to analyse waste from resources, analyse risks and manage variations on costs, time, and human resources. The results of the analysis of risk responses that are important to note are the lack of supervision of the implementation of OHS in the field, lack of coordination and outreach to the residents, working hours of irregular (ineffective) use of the equipment, and unskilled labour. Based on the estimated cost of the construction project of the urban drainage system in the X Area of Bekasi City, there has been a change of actual cost (addendum contract) of 8.9% greater than the planning cost (initial contract). Based on the calculation, total savings with the CCPM method in the construction of the urban drainage system in the X area of Bekasi City are Rp. 2,894,136,099.

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