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All Journal IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal Jurnal Ilmu Komputer dan Informasi JUITA : Jurnal Informatika JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Syntax Literate: Jurnal Ilmiah Indonesia JOURNAL OF APPLIED INFORMATICS AND COMPUTING Indonesian Journal of Electrical Engineering and Computer Science
Rahadianti, Laksmita
Faculty Of Computer Science, Universitas Indonesia, Depok, Indonesia
Humanities Automotive Engineering Computer Science & IT Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Automated Assignment of Community Reports With Early Fusion Multimodal Transformer Hanif, Ikhlasul Akmal; Tjitrahardja, Eduardus; Naufal, Rahmat Bryan; Rahadianti, Laksmita
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 1 (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.v19i1.1426

Abstract

In the current digital era, city governments require effective and responsive platforms to handle public reports and feedback. One such example is Cepat Respon Masyarakat (CRM) in Jakarta, Indonesia, which allows residents to report various issues to the city government, such as infrastructure damage, traffic accidents, and environmental problems. However, after a report is created, it must be assigned to the appropriate agency. Currently, this assignment process is a challenge, taking an average of nearly two hours. To improve the efficiency and responsiveness of handling public reports through the CRM platform, this research proposes an innovative, multimodal solution for classifying public report data, using both text and images to automatically assign community reports. The proposed method was trained and evaluated using a dataset built from real CRM data. Experiments showed that the multimodal model, using a fusion of the DINOv2 transformer and Multilingual E5 with the Early Fusion method, achieved 80.73% accuracy, an increase from the 68.9% achieved by BERT and ResNet. The results of this research are expected to expedite the issue reporting process and enhance the effectiveness of public services, ultimately contributing to the prosperity of all Indonesian citizens in this era of technological advancement.
Comparing GAN, Diffusion, and Diffusion-GAN for Single-Image Deraining of UAV Imagery Salsabilah Aulia Rahman; Laksmita Rahadianti
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i1.28994

Abstract

Single-image deraining for Unmanned Aerial Vehicle (UAV) imagery remains challenging due to non-uniform rain patterns, motion blur, and real-time processing requirements. Existing generative paradigms, including Generative Adversarial Networks (GAN), Diffusion, and Diffusion–GAN, each face inherent trade-offs among restoration quality, stability, and efficiency. To address the lack of unified and fair benchmarking across these paradigms, this study presents a systematic and controlled comparative evaluation of three representative models, including TBGAN, WeatherDiff, and SupResDiffGAN, to assess their relative performance in UAV deraining tasks. The models are evaluated on the UAV-Rain1K and Rain100L datasets using PSNR, SSIM, and inference efficiency metrics to support informed selection of paradigms for UAV applications. Experimental results show that WeatherDiff achieves the highest fidelity with 19.99 dB PSNR, 0.8375 SSIM on UAV-Rain1K and 29.51 dB PSNR, 0.9093 SSIM on Rain100L. TBGAN yields sharper details but lower structural consistency, whereas SupResDiffGAN offers balanced performance with 19.03 dB PSNR and 0.7053 SSIM on UAV-Rain1K and 28.51 dB PSNR and 0.8681 SSIM on Rain100L, with faster inference. These findings highlight the practical trade-offs among the three paradigms and demonstrate that diffusion–GAN frameworks provide the most practical solution for UAV deraining, combining diffusion stability with adversarial sharpness for real-time restoration.
Burned Area Segmentation Using Random Band Selection and Ensemble Encoder Annisaa Nurul Ramadhani Novelika; Laksmita Rahadianti
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.v10i3.7370

Abstract

Wildfires are increasingly recognized as major environmental hazards that endanger ecosystems, human health, and economic stability. In practice, various monitoring efforts, such as hotspot detection, burned-area indices, and field inspections still depend on reliable spatial information to quantify fire impacts over large regions. Accurate segmentation of burned areas from satellite imagery plays a vital role in supporting post-disaster response and sustainable land management. This paper proposes a novel framework for burned area segmentation using multispectral imagery sampled by Random Subspace Band Selection (RSBS) with a U-Net architecture with an ensemble encoder. The RSBS module generates multiple 3-band subsets from Landsat-8 data, incorporating spectrally informative bands such as Near-Infrared (NIR) or Shortwave Infrared (SWIR). These subsets are used to train U-Net models with an ensemble of encoder backbones ResNet34, ResNet50, DenseNet121, MobileNetV2 and InceptionV4 for spectral and architectural diversity. The final predictions are aggregated using majority voting to enhance robustness and generalization. The framework is evaluated on a publicly available Indonesian burned area dataset encompassing diverse land cover types. Experimental results demonstrate that the proposed ensemble model achieves up to 0.7673 IoU, 0.8734 mIoU, and 0.8683 F1 Score, outperforming the best single-backbone model. These findings confirm that the proposed framework offers a scalable, accurate, and adaptable solution for wildfire damage assessment using satellite data.
Co-Authors Aniati Murni Arymurthy Annisaa Nurul Ramadhani Novelika Caryn, Femilia Hardina EVI YULIANTI Hamidiyati, Nazifa Hanif, Ikhlasul Akmal Hartanto, Cahyo Adhi Kardawi, Muhammad Yusuf Naufal, Rahmat Bryan Nugraha, Made Prastha Persada, Bayu Satria Raihan Taufiq, Muhammad Feriansyah Salsabilah Aulia Rahman Saragih, Frederic Morado Steven Christ Pinantyo Arwidarasto Susanto, Muhammad Rifqi Priyo Tjitrahardja, Eduardus