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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Agus Cahyo Nugroho
Chaoyang University of Technology
Computer Science & IT Engineering Library & Information Science

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Improving Remote Sensing Classification with Ensemble Learning and XAI-based Interpretability Radius Tanone; Li-Hua Li; Ramli Ahmad; Alok Kumar Sharma; Agus Cahyo Nugroho
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7121

Abstract

The challenge of classifying remote sensing images primarily stems from the unclear image quality produced by satellites during data collection. Relying on multiple models to recognize remote sensing images can sometimes lead to suboptimal performance. To address this issue, this study integrates transfer learning and ensemble learning to enhance the accuracy of single-model classification. This research employed pre-trained models, including EfficientNetB7, Vision Transformer, and ConvNeXt, and evaluated them on benchmark datasets RSI-CB256 and NWPU RESISC45. The results demonstrate that ensemble learning significantly boosts model performance beyond that of individual models. For the RSI-CB256 dataset, the average and geometric mean ensemble methods achieved the highest accuracy of 0.9985. For another dataset, namely NWPU RESISC45, the geometric mean ensemble achieved the best accuracy of 0.9720. Furthermore, this study explores model transparency and interpretability through eXplainable Artificial Intelligence (XAI) techniques. In addition to transparency, this study uses Grad-CAM to identify critical regions influencing the model’s predictions in remote sensing image classification tasks.
Bridging Vision and Graph Attention Learning Using Swin Transformer for Brain Tumor Classification Radius Tanone; Yos Richard Beeh; Pratyaksa Ocsa Nugraha Saian; Agus Cahyo Nugroho
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 2 (2026): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v19i2.1839

Abstract

Accurate brain tumor classification from MRI images is crucial for supporting clinical diagnosis. This study proposes a hybrid framework that integrates a Swin Transformer and Graph-based learning to enhance feature representation and relational modeling. The Swin Transformer, known for its linear computational complexity and multi-scale attention mechanism, serves as a hierarchical feature extractor. The Graph module, on the other hand, captures structural dependencies among the extracted features. The model is evaluated on a four-class brain MRI dataset using 5-Fold cross-validation. Experimental results consistently demonstrate high performance, with the best accuracy of 0.9907. The model achieves sensitivity and specificity of 0.9903 and 0.9970, respectively. Notably, all folds maintain accuracy above 0.9500, indicating strong robustness and generalization capability. The proposed approach effectively reduces misclassification across tumor categories and outperforms baseline methods, underscoring its potential for reliable brain tumor classification.
Co-Authors Alok Kumar Sharma Li-Hua Li Pratyaksa Ocsa Nugraha Saian Radius Tanone Radius Tanone Ramli Ahmad Yos Richard Beeh