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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) The Indonesian Journal of Computer Science
Aniati Murni Arimurthy
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Transfer learning pada Network VGG16 dan ResNet50 Fauzan Muhammad; Aniati Murni Arimurthy; Dina Chahyati
The Indonesian Journal of Computer Science Vol. 12 No. 1 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i1.3130

Abstract

Transfer Learning adalah prinsip yang digunakan pada neural network dengan tujuan membantu pelatihan pada data yang sedikit, mempercepat waktu dan meningkatkan performa pelatihan. Salah satu metode transfer learning adlaah fine tuning. Pada metode tersebut, melakukan pembekuan pada sejumlah layer pada model network yang dilatih sebelumnya dan melakukan pelatihan pada lapisan yang lainnya dan pada lapisan feature extractor. Pada penelitian ini akan dilihat salah satu metode transfer learning pada model network VGG6 dan ResNet50, yaitu fine tuning, dengan melakukan pembekuan dengan jumlah yang lapisan berbeda pada masng-masing model. Kemudian pada kedua model tersebut dilihat kinerja dan akurasinya pada saat pelatihan dan pengujian
Remote Sensing Scene Classification using ConvNeXt-Tiny Model with Attention Mechanism and Label Smoothing Rachmawan Atmaji Perdana; Aniati Murni Arimurthy; Risnandar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 3 (2024): June 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i3.5731

Abstract

Remote Sensing Scene Classification (RSSC) is the discrete categorization of remote sensing images into various classes of scene categories based on their image content. RSSC plays an important role in many fields, such as agriculture, land mapping, and identification of disaster-prone areas. Therefore, a reliable and accurate RSSC algorithm is required to ensure the accuracy of land identification. Many existing studies in recent years have used deep learning methods, especially CNN combined with attention modules to solve this problem. This study focuses on solving the RSSC problem by proposing a deep learning-based method (CNN) with the ConvNeXt-Tiny model integrated with Efficient Channel Attention Module (ECANet) and label smoothing regularization (LSR). The ConvNeXt-Tiny model shows that a persistent superior outperforms the ‘large’ model in convinced metrics. The ConvNeXt-Tiny model also has a huge advantage in high-precision positioning and higher classification accuracy and localization precision in a variety of complicated scenarios of remote sensing scene recognition. The experiments in this study also aim to prove that the integration of the attention module and LSR into the basic CNN network can improve precision, because the attention module can strengthen important features and weaken features that are less useful for classification. The experimental results proved that the integration of ECANet and LSR in the ConvNeXt-Tiny base network obtained a higher precision of 0.38% in the UC-Merced dataset, 0.7% in the AID, and 0.4% in the WHU-RS19 dataset than the ConvNeXt-Tiny model without ECANet and LSR. The ConvNeXt-Tiny model with ECANet integration and LSR obtained an accuracy of 99.00±0.41% in the UC-Merced dataset, 95.08±0.20% in AID, and 99.50±0.31% in the WHU-RS19 dataset.
Co-Authors Dina Chahyati Fauzan Muhammad Rachmawan Atmaji Perdana Risnandar